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  • 1. Azhar, Damir
    et al.
    Riddle, Patricia
    Mendes, Emilia
    Blekinge Institute of Technology, School of Computing.
    Mittas, Nikolaos
    Angelis, Lefteris
    Using ensembles for web effort estimation2013Conference paper (Refereed)
    Abstract [en]

    Background: Despite the number of Web effort estimation techniques investigated, there is no consensus as to which technique produces the most accurate estimates, an issue shared by effort estimation in the general software estimation domain. A previous study in this domain has shown that using ensembles of estimation techniques can be used to address this issue. Aim: The aim of this paper is to investigate whether ensembles of effort estimation techniques will be similarly successful when used on Web project data. Method: The previous study built ensembles using solo effort estimation techniques that were deemed superior. In order to identify these superior techniques two approaches were investigated: The first involved replicating the methodology used in the previous study, while the second approach used the Scott-Knott algorithm. Both approaches were done using the same 90 solo estimation techniques on Web project data from the Tukutuku dataset. The replication identified 16 solo techniques that were deemed superior and were used to build 15 ensembles, while the Scott-Knott algorithm identified 19 superior solo techniques that were used to build two ensembles. Results: The ensembles produced by both approaches performed very well against solo effort estimation techniques. With the replication, the top 12 techniques were all ensembles, with the remaining 3 ensembles falling within the top 17 techniques. These 15 effort estimation ensembles, along with the 2 built by the second approach, were grouped into the best cluster of effort estimation techniques by the Scott-Knott algorithm. Conclusion: While it may not be possible to identify a single best technique, the results suggest that ensembles of estimation techniques consistently perform well even when using Web project data

  • 2.
    Britto, Ricardo
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Freitas, Vitor
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Usman, Muhammad
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Effort Estimation in Global Software Development: A systematic Literature Review2014In: Proceedings of the 2014 9th IEEE International Conference on Global Software Engineering, 2014, p. 135-144Conference paper (Refereed)
    Abstract [en]

    Nowadays, software systems are a key factor in the success of many organizations as in most cases they play a central role helping them attain a competitive advantage. However, despite their importance, software systems may be quite costly to develop, so substantially decreasing companies’ profits. In order to tackle this challenge, many organizations look for ways to decrease costs and increase profits by applying new software development approaches, like Global Software Development (GSD). Some aspects of the software project like communication, cooperation and coordination are more chal- lenging in globally distributed than in co-located projects, since language, cultural and time zone differences are factors which can increase the required effort to globally perform a software project. Communication, coordination and cooperation aspects affect directly the effort estimation of a project, which is one of the critical tasks related to the management of a software development project. There are many studies related to effort estimation methods/techniques for co-located projects. However, there are evidences that the co-located approaches do not fit to GSD. So, this paper presents the results of a systematic literature review of effort estimation in the context of GSD, which aimed at help both researchers and practitioners to have a holistic view about the current state of the art regarding effort estimation in the context of GSD. The results suggest that there is room to improve the current state of the art on effort estimation in GSD. 

  • 3.
    Britto, Ricardo
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Börstler, Jürgen
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    An Empirical Investigation on Effort Estimation in Agile Global Software Development2015In: Proceedings of the 2015 IEEE 10th International Conference on Global Software Engineering, 2015, p. 38-45Conference paper (Refereed)
    Abstract [en]

    Effort estimation is a project management activity that is mandatory for the execution of softwareprojects. Despite its importance, there have been just a few studies published on such activities within the Agile Global Software Development (AGSD) context. Their aggregated results were recently published as part of a secondary study that reported the state of the art on effort estimationin AGSD. This study aims to complement the above-mentioned secondary study by means of anempirical investigation on the state of the practice on effort estimation in AGSD. To do so, a survey was carried out using as instrument an on-line questionnaire and a sample comprising softwarepractitioners experienced in effort estimation within the AGSD context. Results show that the effortestimation techniques used within the AGSD and collocated contexts remained unchanged, with planning poker being the one employed the most. Sourcing strategies were found to have no or a small influence upon the choice of estimation techniques. With regard to effort predictors, globalchallenges such as cultural and time zone differences were reported, in addition to factors that are commonly considered in the collocated context, such as team experience. Finally, many challenges that impact the accuracy of the effort estimates were reported by the respondents, such as problems with the software requirements and the fact that the communication effort between sites is not properly accounted.

  • 4.
    Britto, Ricardo
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Wohlin, Claes
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    A Specialized Global Software Engineering Taxonomy for Effort Estimation2016In: International Conference on Global Software Engineering, IEEE Computer Society, 2016, p. 154-163Conference paper (Refereed)
    Abstract [en]

    To facilitate the sharing and combination of knowledge by Global Software Engineering (GSE) researchers and practitioners, the need for a common terminology and knowledge classification scheme has been identified, and as a consequence, a taxonomy and an extension were proposed. In addition, one systematic literature review and a survey on respectively the state of the art and practice of effort estimation in GSE were conducted, showing that despite its importance in practice, the GSE effort estimation literature is rare and reported in an ad-hoc way. Therefore, this paper proposes a specialized GSE taxonomy for effort estimation, which was built on the recently proposed general GSE taxonomy (including the extension) and was also based on the findings from two empirical studies and expert knowledge. The specialized taxonomy was validated using data from eight finished GSE projects. Our effort estimation taxonomy for GSE can help both researchers and practitioners by supporting the reporting of new GSE effort estimation studies, i.e. new studies are to be easier to identify, compare, aggregate and synthesize. Further, it can also help practitioners by providing them with an initial set of factors that can be considered when estimating effort for GSE projects.

  • 5.
    Britto, Ricardo
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Usman, Muhammad
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Effort Estimation in Agile Global Software Development Context2014In: Agile Methods. Large-Scale Development, Refactoring, Testing, and Estimation: XP 2014 International Workshops, Rome, Italy, May 26-30, 2014, Revised Selected Papers, Springer, 2014, Vol. 199, p. 182-192Conference paper (Refereed)
    Abstract [en]

    Both Agile Software Development (ASD) and Global Software Development (GSD) are 21st century trends in the software industry. Many studies are reported in the literature wherein software companies have applied an agile method or practice GSD. Given that effort estimation plays a remarkable role in software project management, how do companies perform effort estimation when they use agile method in a GSD context? Based on two effort estimation Systematic Literature Reviews (SLR) - one in within the ASD context and the other in a GSD context, this paper reports a study in which we combined the results of these SLRs to report the state of the art of effort estimation in agile global software development (ASD) context.

  • 6.
    Britto, Ricardo
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wohlin, Claes
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    An Extended Global Software Engineering Taxonomy2016In: Journal of Software Engineering Research and Development, ISSN 2195-1721, Vol. 4, no 3Article in journal (Refereed)
    Abstract [en]

    In Global Software Engineering (GSE), the need for a common terminology and knowledge classification has been identified to facilitate the sharing and combination of knowledge by GSE researchers and practitioners. A GSE taxonomy was recently proposed to address such a need, focusing on a core set of dimensions; however its dimensions do not represent an exhaustive list of relevant GSE factors. Therefore, this study extends the existing taxonomy, incorporating new GSE dimensions that were identified by means of two empirical studies conducted recently.

  • 7.
    Felizardo, Katia
    et al.
    Federal University of Technology, BRA.
    De Souza, Erica
    Federal University of Technology, BRA.
    Falbo, Ricardo
    Federal University of Esp'rito Santo, BRA.
    Vijaykumar, Nandamudi
    Instituto Nacional de Pesquisas Espaciais, BRA.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Nakagawa, Elisayumi
    Universidade de Sao Paulo, BRA.
    Defining protocols of systematic literature reviews in software engineering: A survey2017In: Proceedings - 43rd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2017 / [ed] Felderer, M; Olsson, HH; Skavhaug, A, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 202-209, article id 8051349Conference paper (Refereed)
    Abstract [en]

    Context: Despite being defined during the first phase of the Systematic Literature Review (SLR) process, the protocol is usually refined when other phases are performed. Several researchers have reported their experiences in applying SLRs in Software Engineering (SE) however, there is still a lack of studies discussing the iterative nature of the protocol definition, especially how it should be perceived by researchers conducting SLRs. Objective: The main goal of this study is to perform a survey aiming to identify: (i) the perception of SE researchers related to protocol definition; (ii) the activities of the review process that typically lead to protocol refinements; and (iii) which protocol items are refined in those activities. Method: A survey was performed with 53 SE researchers. Results: Our results show that: (i) protocol definition and pilot test are the two activities that most lead to further protocol refinements; (ii) data extraction form is the most modified item. Besides that, this study confirmed the iterative nature of the protocol definition. Conclusions: An iterative pilot testcan facilitate refinements in the protocol. © 2017 IEEE.

  • 8. Kalinowski, Marcos
    et al.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Travassos, G.H.
    An industry ready defect causal analysis approach exploring Bayesian networks2014In: Lecture Notes in Business Information Processing, Vienna: Springer , 2014, Vol. 166, p. 12-33Conference paper (Refereed)
    Abstract [en]

    Defect causal analysis (DCA) has shown itself an efficient means to improve the quality of software processes and products. A DCA approach exploring Bayesian networks, called DPPI (Defect Prevention-Based Process Improvement), resulted from research following an experimental strategy. Its conceptual phase considered evidence-based guidelines acquired through systematic reviews and feedback from experts in the field. Afterwards, in order to move towards industry readiness the approach evolved based on results of an initial proof of concept and a set of primary studies. This paper describes the experimental strategy followed and provides an overview of the resulting DPPI approach. Moreover, it presents results from applying DPPI in industry in the context of a real software development lifecycle, which allowed further comprehension and insights into using the approach from an industrial perspective.

  • 9. Kocaguneli, Ekrem
    et al.
    Menzies, Tim
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Transfer learning in effort estimation2015In: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 20, no 3, p. 813-843Article in journal (Refereed)
    Abstract [en]

    When projects lack sufficient local data to make predictions, they try to transfer information from other projects. How can we best support this process? In the field of software engineering, transfer learning has been shown to be effective for defect prediction. This paper checks whether it is possible to build transfer learners for software effort estimation. We use data on 154 projects from 2 sources to investigate transfer learning between different time intervals and 195 projects from 51 sources to provide evidence on the value of transfer learning for traditional cross-company learning problems. We find that the same transfer learning method can be useful for transfer effort estimation results for the cross-company learning problem and the cross-time learning problem. It is misguided to think that: (1) Old data of an organization is irrelevant to current context or (2) data of another organization cannot be used for local solutions. Transfer learning is a promising research direction that transfers relevant cross data between time intervals and domains.

  • 10. Lokan, Chris
    et al.
    Mendes, Emilia
    Blekinge Institute of Technology, School of Computing.
    Investigating the use of duration-based moving windows to improve software effort prediction2012Conference paper (Refereed)
    Abstract [en]

    To date most research in software effort estimation has not taken into account any form of chronological split when selecting projects for training and testing sets. A chronological split represents the use of a project's starting and completion dates, such that any model that estimates effort for a new project p only uses as its training set projects that were completed prior to p's starting date. Three recent studies investigated the use of chronological splits, using a type of chronological split called a moving window, which represented a subset of the most recent projects completed prior to a project p's starting date. They found some evidence in favour of using windows whenever projects were recent. These studies all defined window sizes as being fixed numbers of recent projects. In practice, we suggest that estimators are more likely to think in terms of elapsed time than the size of the data set, when deciding which projects to include in a training set. Therefore, this paper investigates the effect on accuracy when using moving windows of various durations to form training sets on which to base effort estimates. Our results show that the use of windows based on duration can affect the accuracy of estimates (in this data set, a window of about three years duration appears best), but to a lesser extent than windows based on a fixed number of projects

  • 11. Lokan, Chris
    et al.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Investigating the use of duration-based moving windows to improve software effort prediction: A replicated study2014In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 56, no 9, p. 1063-1075Article in journal (Refereed)
    Abstract [en]

    Context: Most research in software effort estimation has not considered chronology when selecting projects for training and testing sets. A chronological split represents the use of a projects starting and completion dates, such that any model that estimates effort for a new project p only uses as training data projects that were completed prior to p's start. Four recent studies investigated the use of chronological splits, using moving windows wherein only the most recent projects completed prior to a projects starting date were used as training data. The first three studies (S1-S3) found some evidence in favor of using windows; they all defined window sizes as being fixed numbers of recent projects. In practice, we suggest that estimators think in terms of elapsed time rather than the size of the data set, when deciding which projects to include in a training set. In the fourth study (S4) we showed that the use of windows based on duration can also improve estimation accuracy. Objective: This papers contribution is to extend S4 using an additional dataset, and to also investigate the effect on accuracy when using moving windows of various durations. Method: Stepwise multivariate regression was used to build prediction models, using all available training data, and also using windows of various durations to select training data. Accuracy was compared based on absolute residuals and MREs; the Wilcoxon test was used to check statistical significances between results. Accuracy was also compared against estimates derived from windows containing fixed numbers of projects. Results: Neither fixed size nor fixed duration windows provided superior estimation accuracy in the new data set. Conclusions: Contrary to intuition, our results suggest that it is not always beneficial to exclude old data when estimating effort for new projects. When windows are helpful, windows based on duration are effective.

  • 12.
    Lokan, Chris
    et al.
    UNSW Canberra, Australia.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Investigating the use of moving windows to improve software effort prediction: a replicated study2017In: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 22, no 2, p. 716-767Article in journal (Refereed)
    Abstract [en]

    To date most research in software effort estimation has not taken chronology into account when selecting projects for training and validation sets. A chronological split represents the use of a project’s starting and completion dates, such that any model that estimates effort for a new project p only uses as its training set projects that have been completed prior to p’s starting date. A study in 2009 (“S3”) investigated the use of chronological split taking into account a project’s age. The research question investigated was whether the use of a training set containing only the most recent past projects (a “moving window” of recent projects) would lead to more accurate estimates when compared to using the entire history of past projects completed prior to the starting date of a new project. S3 found that moving windows could improve the accuracy of estimates. The study described herein replicates S3 using three different and independent data sets. Estimation models were built using regression, and accuracy was measured using absolute residuals. The results contradict S3, as they do not show any gain in estimation accuracy when using windows for effort estimation. This is a surprising result: the intuition that recent data should be more helpful than old data for effort estimation is not supported. Several factors, which are discussed in this paper, might have contributed to such contradicting results. Some of our future work entails replicating this work using other datasets, to understand better when using windows is a suitable choice for software companies.

  • 13.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Applying a knowledge management technique to improve risk assessment and effort estimation of healthcare software projects2014In: Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937, Vol. 457, p. 40-56Article in journal (Refereed)
    Abstract [en]

    One of the pillars for sound Software Project Management is reliable effort estimation. Therefore it is important to fully identify what are the fundamental factors that affect an effort estimate for a new project and how these factors are inter-related. This paper describes a case study where a Knowledge Management technique was employed to build an expert-based effort estimation model to estimate effort for healthcare software projects. This model was built with the participation of seven project managers, and was validated using data from 22 past finished projects. The model led to numerous changes in process and also in business. The company adapted their existing effort estimation process to be in line with the model that was created, and the use of a mathematically- based model also led to an increase in the number of projects being delegated to this company by other company branches worldwide.

  • 14.
    Mendes, Emilia
    Blekinge Institute of Technology, School of Computing.
    Improving Software Effort Estimation Using an Expert-centred Approach2012In: Lecture Notes in Computer Science, Springer , 2012, Vol. 7623, p. 18-33Conference paper (Refereed)
    Abstract [en]

    A cornerstone of software project management is effort estimation, the process by which effort is forecasted and used as basis to predict costs and allocate resources effectively, so enabling projects to be delivered on time and within budget. Effort estimation is a very complex domain where the relationship between factors is non-deterministic and has an inherently uncertain nature, and where corresponding decisions and predictions require reasoning with uncertainty. Most studies in this field, however, have to date investigated ways to improve software effort estimation by proposing and comparing techniques to build effort prediction models where such models are built solely from data on past software projects - data-driven models. The drawback with such approach is threefold: first, it ignores the explicit inclusion of uncertainty, which is inherent to the effort estimation domain, into such models; second, it ignores the explicit representation of causal relationships between factors; third, it relies solely on the variables being part of the dataset used for model building, under the assumption that those variables represent the fundamental factors within the context of software effort prediction. Recently, as part of a New Zealand and later on Brazilian government-funded projects, we investigated the use of an expert-centred approach in combination with a technique that enables the explicit inclusion of uncertainty and causal relationships as means to improve software effort estimation. This paper will first provide an overview of the effort estimation process, followed by the discussion of how an expert-centred approach to improving such process can be advantageous to software companies. In addition, we also detail our experience building and validating six different expert-based effort estimation models for ICT companies in New Zealand and Brazil. Post-mortem interviews with the participating companies showed that they found the entire process extremely beneficial and worthwhile, and that all the models created remained in use by those companies. Finally, the methodology focus of this paper, which focuses on expert knowledge elicitation and participation, can be employed not only to improve a software effort estimation process, but also to improve other project management-related activities.

  • 15.
    Mendes, Emilia
    Blekinge Institute of Technology, School of Computing.
    Using expert-based bayesian networks as decision support systems to improve project management of healthcare software projects2013Conference paper (Refereed)
    Abstract [en]

    One of the pillars for sound Software Project Management is reliable effort estimation. Therefore it is important to fully identify what are the fundamental factors that affect an effort estimate for a new project and how these factors are inter-related. This paper describes a case study where a Bayesian Network model to estimate effort for healthcare software projects was built. This model was solely elicited from expert knowledge, with the participation of seven project managers, and was validated using data from 22 past finished projects. The model led to numerous changes in process and also in business. The company adapted their existing effort estimation process to be in line with the model that was created, and the use of a mathematically-based model also led to an increase in the number of projects being delegated to this company by other company branches worldwide.

  • 16.
    Mendes, Emilia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Ali, Nauman bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Counsell, Steve
    Brunel University London, GBR.
    Baldassare, Maria Teresa
    Università degli Studi di Bari, ITA.
    Special issue on evaluation and assessment in software engineering2019In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 151, p. 224-225Article in journal (Refereed)
  • 17.
    Mendes, Emilia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Kalinowski, M.
    Martins, D.
    Ferrucci, F.
    Sarro, F.
    Cross- vs. Within-company cost estimation studies revisited: An extended systematic review2014Conference paper (Refereed)
    Abstract [en]

    The objective of this paper is to extend a previously conducted systematic literature review (SLR) that investigated under what circumstances individual organizations would be able to rely on cross-company based estimation models. [Method] We applied the same methodology used in the SLR we are extending herein (covering the period 2006-2013) based on primary studies that compared predictions from cross-company models with predictions from within-company models constructed from analysis of project data. [Results] We identified 11 additional papers; however two of these did not present independent results and one had inconclusive findings. Two of the remaining eight papers presented both, trials where cross-company predictions were not significantly different from within-company predictions and others where they were significantly different. Four found that cross-company models gave prediction accuracy significantly different from within-company models (one of them in favor of cross-company models), while two found no significant difference. The main pattern when examining the study related factors was that studies where cross-company predictions were significantly different from within-company predictions employed larger within-company data sets. [Conclusions] Overall, half of the analyzed evidence indicated that cross-company estimation models are not significantly worse than within-company estimation models. Moreover, there is some evidence that sample size does not imply in higher estimation accuracy, and that samples for building estimation models should be carefully selected/filtered based on quality control and project similarity aspects. The results need to be combined with the findings from the SLR we are extending to allow further investigating this topic.

  • 18.
    Mendes, Emilia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Rodriguez, Pilar
    University of Oulu, FIN.
    Freitas, Vitor
    University of Oulu, FIN.
    Baker, Simon
    University of Cambridge, GBR.
    Atoui, Mohamed Amine
    University of Oulu, FIN.
    Correction to: Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework (Software Quality Journal, (2018), 26, 2, (607-656), 10.1007/s11219-017-9360-z)2018In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 26, no 4, p. 1595-1596Article in journal (Other academic)
    Abstract [en]

    The original version of this article unfortunately contained a mistake in Figs. 1 and 21. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

  • 19.
    Mendes, Emilia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Rodriguez, Pilar
    University of Oulu, FIN.
    Freitas, Vitor
    University of Oulu, FIN.
    Baker, Simon
    University of Cambridge, GBR.
    Atoui, Mohamed Amine
    University of Oulu, FIN.
    Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework2018In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 26, no 2, p. 607-656Article in journal (Refereed)
    Abstract [en]

    To sustain growth, maintain competitive advantage and to innovate, companies must make a paradigm shift in which both short- and long-term value aspects are employed to guide their decision-making. Such need is clearly pressing in innovative industries, such as ICT, and is also the core of Value-based Software Engineering (VBSE). The goal of this paper is to detail a framework called VALUE—improving decision-making relating to software-intensive products and services development—and to show its application in practice to a large ICT company in Finland. The VALUE framework includes a mixed-methods approach, as follows: to elicit key stakeholders’ tacit knowledge regarding factors used during a decision-making process, either transcripts from interviews with key stakeholders are analysed and validated in focus group meetings or focus-group meeting(s) are directly applied. These value factors are later used as input to a Web-based tool (Value tool) employed to support decision making. This tool was co-created with four industrial partners in this research via a design science approach that includes several case studies and focus-group meetings. Later, data on key stakeholders’ decisions gathered using the Value tool, plus additional input from key stakeholders, are used, in combination with the Expert-based Knowledge Engineering of Bayesian Network (EKEBN) process, coupled with the weighed sum algorithm (WSA) method, to build and validate a company-specific value estimation model. The application of our proposed framework to a real case, as part of an ongoing collaboration with a large software company (company A), is presented herein. Further, we also provide a detailed example, partially using real data on decisions, of a value estimation Bayesian network (BN) model for company A. This paper presents some empirical results from applying the VALUE Framework to a large ICT company; those relate to eliciting key stakeholders’ tacit knowledge, which is later used as input to a pilot study where these stakeholders employ the Value tool to select features for one of their company’s chief products. The data on decisions obtained from this pilot study is later applied to a detailed example on building a value estimation BN model for company A. We detail a framework—VALUE framework—to be used to help companies improve their value-based decisions and to go a step further and also estimate the overall value of each decision. © 2017 The Author(s)

  • 20.
    Mendes, Emilia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Vaz, Veronica Taquete
    UFRJ Fed Univ Rio De Janeiro, POB 68511, Rio De Janeiro, Brazil..
    Muradas, Fernando
    Naval Syst Anal Ctr, BR-20091000 San Diego, CA, Brazil..
    An Expert-Based Requirements Effort Estimation Model Using Bayesian Networks2016In: SOFTWARE QUALITY: THE FUTURE OF SYSTEMS- AND SOFTWARE DEVELOPMENT, 2016, p. 79-93Conference paper (Refereed)
    Abstract [en]

    [Motivation]: There are numerous software companies worldwide that split the software development life cycle into at least two separate projects an initial project where a requirements specification document is prepared; and a follow-up project where the previously prepared requirements document is used as input to developing a software application. These follow-up projects can also be delegated to a third party, as occurs in numerous global software development scenarios. Effort estimation is one of the cornerstones of any type of project management; however, a systematic literature review on requirements effort estimation found hardly any empirical study investigating this topic. [Objective]: The goal of this paper is to describe an industrial case study where an expert-based requirements effort estimation model was built and validated for the Brazilian Navy. [Method]: A knowledge engineering of Bayesian networks process was employed to build the requirements effort estimation model. [Results]: The expert-based requirements effort estimation model was built with the participation of seven software requirements analysts and project managers, leading to 28 prediction factors and 30+ relationships. The model was validated based on real data from 11 large requirements specification projects. The model was incorporated into the Brazilian navy's quality assurance process to be used by their software requirements analysts and managers. [Conclusion]: This paper details a case study where an expert-based requirements effort estimation model based solely on knowledge from requirements analysts and project managers was successfully built to help the Brazilian Navy estimate the requirements effort for their projects.

  • 21.
    Mendes, Emilia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Viana, Davi
    Univ Fed Maranhao, BRA.
    Vishnubhotla, Sai Datta
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Realising Individual and Team Capability in Agile Software Development: A Qualitative Investigation2018In: Proceedings - 44th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2018 / [ed] Bures, T Angelis, L, IEEE , 2018, p. 183-190Conference paper (Refereed)
    Abstract [en]

    Several studies have shown that both individual and team capability can affect software development performance and project success; a deeper understating of such phenomena is crucial within the context of Agile Software Development (ASD), given that its workforce is a key source of agility. This paper contributes towards such understanding by means of a case study that uses data from 14 interviews carried out at a large telecommunications company, within the context of a mobile money transfer system developed in Sweden and India, to identify individual and team capability measures used to form productive teams. Our results identified 10 individual and five team capability measures, of which, respectively, five and four have not been previously characterised by a systematic literature review (SLR) on this same topic. Such review aggregated evidence for a total of 133 individual and 28 team capability measures. Further work entails extending our findings via interviewing other software/software-intensive industries practicing ASD.

  • 22.
    Mendes, Emilia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Winkler, Dietmar
    Technische Universitat Wien, AUT.
    Special issue on “software quality in software-intensive systems”2018In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 26, no 2, p. 657-660Article in journal (Refereed)
  • 23. Minku, Leandro
    et al.
    Sarro, Federica
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Ferrucci, Filomena
    How to Make Best Use of Cross-Company Data for Web Effort Estimation?2015In: 2015 ACM/IEEE INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT (ESEM), 2015, p. 172-181Conference paper (Refereed)
    Abstract [en]

    [Context]: The numerous challenges that can hinder software companies from gathering their own data have motivated over the past 15 years research on the use of cross-company (CC) datasets for software effort prediction. Part of this research focused on Web effort prediction, given the large increase worldwide in the development of Web applications. Some of these studies indicate that it may be possible to achieve better performance using CC models if some strategy to make the CC data more similar to the within-company (WC) data is adopted. [Goal]: This study investigates the use of a recently proposed approach called Dycom to assess to what extent Web effort predictions obtained using CC datasets are effective in relation to the predictions obtained using WC data when explicitly mapping the CC models to the WC context. [Method]: Data on 125 Web projects from eight different companies part of the Tukutuku database were used to build prediction models. We benchmarked these models against baseline models (mean and median effort) and a WC base learner that does not benefit of the mapping. We also compared Dycom against a competitive CC approach from the literature (NN-filtering). We report a company-by-company analysis. [Results]: Dycom usually managed to achieve similar or better performance than a WC model while using only half of the WC training data. These results are also an improvement over previous studies that investigated the use of different strategies to adapt CC models to the WC data for Web effort estimation. [Conclusions]: We conclude that the use of Dycom for Web effort prediction is quite promising and in general supports previous results when applying Dycom to conventional software datasets.

  • 24.
    Molléri, Jefferson Seide
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Felderer, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersen, Kai
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Reasoning about Research Quality Alignment in Software EngineeringIn: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228Article in journal (Refereed)
    Abstract [en]

    Context: Research quality is intended to assess the design and reporting of studies. It comprises a series of concepts such as methodological rigor, practical relevance, and conformance to ethical standards. Depending on the perspective, different views of importance are given to the conceptual dimensions of research quality.

    Objective: We aim to better understand what constitutes research quality from the perspective of the empirical software engineering community. In particular, we intend to assess the level of alignment between researchers with regard to a conceptual model of research quality.

    Method: We conducted a mixed methods approach comprising an internal case study and a complementary focus group. We carried out a hierarchical voting prioritization based on the conceptual model to collect relative values for importance. In the focus group, we also moderate discussions with experts to address potential misalignment.

    Results: We provide levels of alignment with regard to the importance of quality dimensions in the view of the participants. Moreover, the conceptual model fairly expresses the quality of research but has limitations with regards the structure and description of its components.

    Conclusion: Based on the results, we revised the conceptual model and provided an updated version adjusted to the context of empirical software engineering research. We also discussed how to assess quality alignment in research using our approach, and how to use the revised model of quality to characterize an assessment instrument.

  • 25.
    Molléri, Jefferson Seide
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersen, Kai
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Felderer, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Aligning the Views of Research Quality in Empirical Software EngineeringIn: ACM Transactions on Software Engineering and Methodology, ISSN 1049-331X, E-ISSN 1557-7392Article in journal (Refereed)
    Abstract [en]

    Context: Research quality is intended to assess the design and reporting of studies. It comprises a series of concepts such as methodological rigor, practical relevance, and conformance to ethical standards. Depending on the perspective, different views of importance are given to the conceptual dimensions of research quality.

    Objective: We intend to assess the level of alignment between researchers with regard to a conceptual model of research quality. This includes aligning the definition of research quality and reasoning on the relative importance of quality characteristics.

    Method: We conducted a mixed methods approach comprising an internal case study and a complementary focus group. We carried out a hierarchical voting prioritization based on the conceptual model to collect relative values for importance. In the focus group, we also moderate discussions with experts to address potential misalignment.

    Results: The alignment at the research group level was higher compared to that at community level. Moreover, the interdisciplinary conceptual quality model was seeing to express fairly the quality of research, but presented limitations regarding its structure and components' description, which resulted in an updated model. 

    Conclusion: The interdisciplinary model used was suitable for the software engineering context. The process used for reflecting on the alignment of quality with respect to definitions and priorities was working well. 

  • 26.
    Molléri, Jefferson Seide
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersen, Kai
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    An Empirically Evaluated Checklist for Surveys in Software EngineeringIn: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025Article in journal (Refereed)
    Abstract [en]

    Context: Over the past decade Software Engineering research has seen a steady increase in survey-based studies, and there are several guidelines providing support for those willing to carry out surveys. The need for auditing survey research has been raised in the literature. Checklists have been used to assess different types of empirical studies, such as experiments and case studies.

    Objective: This paper proposes a checklist to support the design and assessment of survey-based research in software engineering grounded in existing guidelines for survey research. We further evaluated the checklist in the research practice context.

    Method: To construct the checklist, we systematically aggregated knowledge from 12 methodological studies supporting survey-based research in software engineering. We identified the key stages of the survey process and its recommended practices through thematic analysis and vote counting. To improve our initially designed checklist we evaluated it using a mixed evaluation approach involving experienced researchers.

    Results: The evaluation provided insights regarding the limitations of the checklist in relation to its understanding and objectivity. In particular, 19 of the 38 checklist items were improved according to the feedback received from its evaluation. Finally, a discussion on how to use the checklist and what its implications are for research practice is also provided.

    Conclusion: The proposed checklist is an instrument suitable for auditing survey reports as well as a support tool to guide ongoing research with regard to the survey design process.

  • 27.
    Molléri, Jefferson Seide
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersen, Kai
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    CERSE - Catalog for empirical research in software engineering: A Systematic mapping study2019In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 105, p. 117-149Article in journal (Refereed)
    Abstract [en]

    Context Empirical research in software engineering contributes towards developing scientific knowledge in this field, which in turn is relevant to inform decision-making in industry. A number of empirical studies have been carried out to date in software engineering, and the need for guidelines for conducting and evaluating such research has been stressed. Objective: The main goal of this mapping study is to identify and summarize the body of knowledge on research guidelines, assessment instruments and knowledge organization systems on how to conduct and evaluate empirical research in software engineering. Method: A systematic mapping study employing manual search and snowballing techniques was carried out to identify the suitable papers. To build up the catalog, we extracted and categorized information provided by the identified papers. Results: The mapping study comprises a list of 341 methodological papers, classified according to research methods, research phases covered, and type of instrument provided. Later, we derived a brief explanatory review of the instruments provided for each of the research methods. Conclusion: We provide: an aggregated body of knowledge on the state of the art relating to guidelines, assessment instruments and knowledge organization systems for carrying out empirical software engineering research; an exemplary usage scenario that can be used to guide those carrying out such studies is also provided. Finally, we discuss the catalog's implications for research practice and the needs for further research. © 2018 Elsevier B.V.

  • 28.
    Molléri, Jefferson Seide
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersen, Kai
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Towards understanding the relation between citations and research quality in software engineering studies2018In: Scientometrics, ISSN 0138-9130, E-ISSN 1588-2861Article in journal (Refereed)
    Abstract [en]

    The importance of achieving high quality in research practice has been highlighted in different disciplines. At the same time, citations are utilized to measure the impact of academic researchers and institutions. One open question is whether the quality in the reporting of research is related to scientific impact, which would be desired. In this exploratory study we aim to: (1) Investigate how consistently a scoring rubric for rigor and relevance has been used to assess research quality of software engineering studies; (2) Explore the relationship between rigor, relevance and citation count. Through backward snowball sampling we identified 718 primary studies assessed through the scoring rubric. We utilized cluster analysis and conditional inference tree to explore the relationship between quality in the reporting of research (represented by rigor and relevance) and scientiometrics (represented by normalized citations). The results show that only rigor is related to studies’ normalized citations. Besides that, confounding factors are likely to influence the number of citations. The results also suggest that the scoring rubric is not applied the same way by all studies, and one of the likely reasons is because it was found to be too abstract and in need to be further refined. Our findings could be used as a basis to further understand the relation between the quality in the reporting of research and scientific impact, and foster new discussions on how to fairly acknowledge studies for performing well with respect to the emphasized research quality. Furthermore, we highlighted the need to further improve the scoring rubric. © 2018, The Author(s).

  • 29.
    Moraes, Ana Louiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Eivazzadeh, Shahryar
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Prognosis of Dementia Employing Machine Learning and Microsimulation Techniques: A Systematic Literature Review2016In: Procedia Computer Science / [ed] Martinho R.,Rijo R.,Cruz-Cunha M.M.,Bjorn-Andersen N.,Quintela Varajao J.E., Elsevier, 2016, Vol. 100, p. 480-488Conference paper (Refereed)
    Abstract [en]

    OBJECTIVE: The objective of this paper is to investigate the goals and variables employed in the machine learning and microsimulation studies for the prognosis of dementia. METHOD: According to preset protocols, the Pubmed, Socups and Web of Science databases were searched to find studies that matched the defined inclusion/exclusion criteria, and then its references were checked for new studies. A quality checklist assessed the selected studies, and removed the low quality ones. The remaining ones (included set) had their data extracted and summarized. RESULTS: The summary of the data of the 37 included studies showed that the most common goal of the selected studies was the prediction of the conversion from mild cognitive impairment to Alzheimer's Disease, for studies that used machine learning, and cost estimation for the microsimulation ones. About the variables, neuroimaging was the most frequent used. CONCLUSIONS: The systematic literature review showed clear trends in prognosis of dementia research in what concerns machine learning techniques and microsimulation.

  • 30. Moraes, Ana Luiza Dallora
    et al.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Kvist, Ola
    KI, SWE.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Ruiz, Sandra
    KI, SWE.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis2019In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 7, article id e0220242Article in journal (Refereed)
    Abstract [en]

    Background The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. Objective The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. Method A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. Results 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. Conclusions There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. Copyright: © 2019 Dallora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • 31.
    Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Eivazzadeh, Shahryar
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 6, article id e0179804Article in journal (Refereed)
    Abstract [en]

    Background Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. Objective The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. Method To achieve our goal we carried out a systematic literature review, in which three large databases -Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. Results In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer's disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Conclusions Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies' different contexts. © 2017 Dallora et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • 32.
    Mourao, Erica
    et al.
    Fluminense Fed Univ, BRA.
    Kalinowski, Marcos
    Pontifical Catholic Univ Rio de Janeiro PUC Rio, BRA.
    Murta, Leonardo
    Fluminense Fed Univ, BRA.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wohlin, Claes
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Blekinge Inst Technol, Karlskrona, Sweden..
    Investigating the Use of a Hybrid Search Strategy for Systematic Reviews2017In: 11TH ACM/IEEE INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT (ESEM 2017), IEEE , 2017, p. 193-198Conference paper (Refereed)
    Abstract [en]

    [Background] Systematic Literature Reviews (SLRs) are one of the important pillars when employing an evidence-based paradigm in Software Engineering. To date most SLRs have been conducted using a search strategy involving several digital libraries. However, significant issues have been reported for digital libraries and applying such search strategy requires substantial effort. On the other hand, snowballing has recently arisen as a potentially more efficient alternative or complementary solution. Nevertheless, it requires a relevant seed set of papers. [Aims] This paper proposes and evaluates a hybrid search strategy combining searching in a specific digital library (Scopus) with backward and forward snowballing. [Method] The proposed hybrid strategy was applied to two previously published SLRs that adopted database searches. We investigate whether it is able to retrieve the same included papers with lower effort in terms of the number of analysed papers. The two selected SLRs relate respectively to elicitation techniques (not confined to Software Engineering (SE)) and to a specific SE topic on cost estimation. [Results] Our results provide preliminary support for the proposed hybrid search strategy as being suitable for SLRs investigating a specific research topic within the SE domain. Furthermore, it helps overcoming existing issues with using digital libraries in SE. [Conclusions] The hybrid search strategy provides competitive results, similar to using several digital libraries. However, further investigation is needed to evaluate the hybrid search strategy.

  • 33. Riaz, Mehwish
    et al.
    Mendes, Emilia
    Blekinge Institute of Technology, School of Computing.
    Tempero, Ewan
    Sulayman, Muhammad
    Using CBR and CART to predict maintainability of relational database-driven software applications2013Conference paper (Refereed)
    Abstract [en]

    Relational database-driven software applications have gained significant importance in modern software development. Given that software maintainability is an important quality attribute, predicting these applications' maintainability can provide various benefits to software organizations, such as adopting a defensive design and more informed resource management. Aims: The aim of this paper is to present the results from employing two well-known prediction techniques to estimate the maintainability of relational database-driven applications. Method: Case-based reasoning (CBR) and classification and regression trees (CART) were applied to data gathered on 56 software projects from software companies. The projects concerned development and/or maintenance of relational database-driven applications. Unlike previous studies, all variables (28 independent and 1 dependent) were measured on a 5-point bi-polar scale. Results: Results showed that CBR performed slightly better (at 76.8% correct predictions) in terms of prediction accuracy when compared to CART (67.8%). In addition, the two important predictors identified were documentation quality and understandability of the applications. Conclusions: The results show that CBR can be used by software companies to formalize and improve their process of maintainability prediction. Future work involves gathering more data and also employing other prediction techniques.

  • 34. Riaz, Mehwish
    et al.
    Tempero, Ewan
    Sulayman, Muhammad
    Mendes, Emilia
    Blekinge Institute of Technology, School of Computing.
    Maintainability Predictors For Relational Database-Driven Software Applications: Extended Results From A Survey2013In: International Journal of Software Engineering and Knowledge Engineering, ISSN 0218-1940 , Vol. 23, no 4, p. 507-522Article in journal (Refereed)
    Abstract [en]

    Software maintainability is a very important quality attribute. Its prediction for relational database-driven software applications can help organizations improve the maintainability of these applications. The research presented herein adopts a survey-based approach where a survey was conducted with 40 software professionals aimed at identifying and ranking the important maintainability predictors for relational database-driven software applications. The survey results were analyzed using frequency analysis. The results suggest that maintainability prediction for relational database-driven applications is not the same as that of traditional software applications in terms of the importance of the predictors used for this purpose. The results also provide a baseline for creating maintainability prediction models for relational database-driven software applications.

  • 35.
    Rodriguez, Pilar
    et al.
    Oulun Yliopisto, FIN.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Turhan, Buran
    Oulun Yliopisto, FIN.
    Key Stakeholders' Value Propositions for Feature Selection in Software-intensive Products: An Industrial Case Study2018In: IEEE Transactions on Software Engineering, ISSN 0098-5589, E-ISSN 1939-3520Article in journal (Refereed)
    Abstract [en]

    Numerous software companies are adopting value-based decision making. However, what does value mean for key stakeholders making decisions? How do different stakeholder groups understand value? Without an explicit understanding of what value means, decisions are subject to ambiguity and vagueness, which are likely to bias them. This case study provides an in-depth analysis of key stakeholders' value propositions when selecting features for a large telecommunications company's software-intensive product. Stakeholder' value propositions were elicited via interviews, which were analyzed using Grounded Theory coding techniques (open and selective coding). Thirty-six value propositions were identified and classified into six dimensions: customer value, market competitiveness, economic value/profitability, cost efficiency, technology & architecture, and company strategy. Our results show that although propositions in the customer value dimension were those mentioned the most, the concept of value for feature selection encompasses a wide range of value propositions. Moreover, stakeholder groups focused on different and complementary value dimensions, calling to the importance of involving all key stakeholders in the decision making process. Although our results are particularly relevant to companies similar to the one described herein, they aim to generate a learning process on value-based feature selection for practitioners and researchers in general. IEEE

  • 36. Salleh, Norsaremah
    et al.
    Mendes, Emilia
    Blekinge Institute of Technology, School of Computing.
    Grundy, John
    Investigating the effects of personality traits on pair programming in a higher education setting through a family of experiments2014In: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 19, no 3, p. 714-752Article in journal (Refereed)
    Abstract [en]

    Evidence from a systematic literature that we conducted previously revealed numerous inconsistencies in findings from the Pair Programming (PP) literature regarding the effects of personality on PP’s effectiveness. It also showed that, despite numerous investigations, the effect of differing personality traits of pairs on the successful implementation of pair-programming (PP) within a higher education setting is still unclear. In addition, our results also showed that the personality instrument used the most had been the Myers-Briggs Type Indicator (MBTI), despite being an indicator criticized by personality psychologists as unreliable in measuring an individual’s personality traits. These issues motivated our research, where we conducted a series of five formal experiments at the University of Auckland (between 2009 and 2010) using 594 undergraduate students as subjects to investigate the effects of personality composition on PP’s effectiveness. Our studies employed the Five-Factor personality framework, comprising five broad traits (Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). Our experiments investigated three of the five traits - Conscientiousness, Neuroticism, and Openness. Our findings showed that Conscientiousness and Neuroticism did not present a statistically significantly effect upon paired students’ academic performance. However, Openness played a significant role in differentiating paired students’ academic performance. Participants survey results also indicated that PP not only caused an increase in satisfaction and confidence levels but also brought enjoyment to the tutorial classes and enhanced students’ motivation.

  • 37.
    Santos, Rodrigo
    et al.
    Fed Univ State Rio de Janeiro, BRA.
    Teixeira, Eldanae
    Univ Fed Rio de Janeiro, BRA.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    McGregor, John
    Clemson Univ, USA.
    2nd Workshop on Social, Human, and Economic Aspects of Software (WASHES) Special Edition for Software Reuse2017In: MASTERING SCALE AND COMPLEXITY IN SOFTWARE REUSE (ICSR 2017) / [ed] Botterweck, G Werner, C, SPRINGER INTERNATIONAL PUBLISHING AG , 2017, p. 223-224Conference paper (Refereed)
    Abstract [en]

    The Special Edition for Software Reuse of the Workshop on Social, Human, and Economic Aspects of Software (WASHES) aims at bringing together researchers and practitioners who are interested in social, human, and economic aspects of software. WASHES is a forum to discuss models, methods, techniques, and tools to achieve software quality, improve reuse and deal with the existing issues in this context. This special edition's main topic is "Challenges of Reuse and the Social, Human, and Economic Aspects of Software". We believe it is important to investigate software reuse beyond the technical perspective and understand how the non-technical barriers of reuse affect practices, processes and tools in practice.

  • 38. Soomro, Arjumand Bano
    et al.
    Salleh, Norsaremah
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grundy, John
    Burch, Giles
    Nordin, Azlin
    The effect of software engineers' personality traits on team climate and performance: A Systematic Literature Review2016In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 73, p. 52-65Article in journal (Refereed)
    Abstract [en]

    Context: Over the past 50 years numerous studies have investigated the possible effect that software engineers' personalities may have upon their individual tasks and teamwork. These have led to an improved understanding of that relationship; however, the analysis of personality traits and their impact on the software development process is still an area under investigation and debate. Further, other than personality traits, "team climate" is also another factor that has also been investigated given its relationship with software teams' performance. Objective: The aim of this paper is to investigate how software professionals' personality is associated with team climate and team performance. Method: In this paper we detail a Systematic Literature Review (SLR) of the effect of software engineers' personality traits and team climate on software team performance. Results: Our main findings include 35 primary studies that have addressed the relationship between personality and team performance without considering team climate. The findings showed that team climate comprises a wide range of factors that fall within the fields of management and behavioral sciences. Most of the studies used undergraduate students as subjects and as surrogates of software professionals. Conclusions: The findings from this SLR would be beneficial for understanding the personality assessment of software development team members by revealing the traits of personality taxonomy, along with the measurement of the software development team working environment. These measurements would be useful in examining the success and failure possibilities of software projects in development processes. General terms: Human factors, performance. (C) 2016 Elsevier B.V. All rights reserved.

  • 39. Sulayman, Muhammad
    et al.
    Mendes, Emilia
    Blekinge Institute of Technology, School of Computing.
    Urquhart, Cathy
    Riaz, Mehwish
    Tempero, Ewan
    Towards a theoretical framework of SPI success factors for small and medium web companies2014In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 56, no 7, p. 807-820Article in journal (Refereed)
    Abstract [en]

    Context: The context of this research is software process improvement (SPI) success factors for small and medium Web companies. Objective: The primary objective of this paper is to propose a theoretical framework of SPI success factors for small and medium Web companies. Method: The theoretical framework presented in this study aggregated the results of three previous research phases by applying principles of theoretical integration and comparative analysis. Those three previous phases were all empirical in nature, and comprise: a systematic review of SPI in small and medium Web companies [1,2]; a replication study [3] and a grounded theory-based initial exploratory framework of factors in small and medium Web companies [4]. Results: The theoretical framework includes 18 categories of SPI success factors, 148 properties of these categories and 25 corresponding relationships, which bind these categories together. With the help of these relationships, the categories and properties of SPI success factors can be directly translated into a set of guidelines, which can then be used by the practitioners of small and medium Web companies to improve the current state of SPI in their companies and achieve overall company success. Conclusion: The comprehensive theoretical framework of SPI success factors presented herein provides evidence regarding key factors for predicting SPI success for small and medium Web companies. The framework can be used as a baseline for a successful implementation of SPI initiatives in the mentioned domain.

  • 40.
    Turhan, Burak
    et al.
    Univ Oulu, Dept Informat Proc Sci, Oulu 90014, Finland..
    Mendes, Emilia
    Blekinge Institute of Technology, School of Computing. Blekinge Inst Technol, Software Engn Res Lab, Karlskrona, Sweden..
    A Comparison of Cross- versus Single-company Effort Prediction Models for Web Projects2014In: 2014 40TH EUROMICRO CONFERENCE SERIES ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2014), 2014, p. 285-292Conference paper (Refereed)
    Abstract [en]

    Background: In order to address the challenges in companies having no or limited effort datasets of their own, cross-company models have been a focus of interest for previous studies. Further, a particular domain of investigation has been Web projects. Aim: This study investigates to what extent effort predictions obtained using cross-company (CC) datasets are effective in relation to the predictions obtained using single-company (SC) datasets within the domain of web projects. Method: This study uses the Tukutuku database. We employed data on 125 projects from eight different companies and built cross and single-company models with stepwise linear regression (SWR) with and without relevancy filtering. We also benchmarked these models against mean and median based models. We report a case-by-case analysis per company as well as a meta-analysis of the findings. Results: Results showed that CC models provided poor predictions and performed significantly worse than SC models. However, relevancy filtered CC models yielded comparable results to that of SC models. These results corroborate with previous research. An interesting result was that the median-based models were consistently better than other models. Conclusions: We conclude that companies that carry out Web development may use a median-based CC model for prediction until it is possible for the company to build its own SC model, which can be used by itself or in combination with median-based estimations.

  • 41.
    Usman, Muhammad
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Britto, Ricardo
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Börstler, Jürgen
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Taxonomies in software engineering: A Systematic mapping study and a revised taxonomy development method2017In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 85, p. 43-59Article in journal (Refereed)
    Abstract [en]

    Context: Software Engineering (SE) is an evolving discipline with new subareas being continuously developed and added. To structure and better understand the SE body of knowledge, taxonomies have been proposed in all SE knowledge areas. Objective: The objective of this paper is to characterize the state-of-the-art research on SE taxonomies. Method: A systematic mapping study was conducted, based on 270 primary studies. Results: An increasing number of SE taxonomies have been published since 2000 in a broad range of venues, including the top SE journals and conferences. The majority of taxonomies can be grouped into the following SWEBOI(knowledge areas: construction (19.55%), design (19.55%), requirements (15.50%) and maintenance (11.81%). Illustration (45.76%) is the most frequently used approach for taxonomy validation. Hierarchy (53.14%) and faceted analysis (39.48%) are the most frequently used classification structures. Most taxonomies rely on qualitative procedures to classify subject matter instances, but in most cases (86.53%) these procedures are not described in sufficient detail. The majority of the taxonomies (97%) target unique subject matters and many taxonomy-papers are cited frequently. Most SE taxonomies are designed in an ad-hoc way. To address this issue, we have revised an existing method for developing taxonomies in a more systematic way. Conclusion: There is a strong interest in taxonomies in SE, but few taxonomies are extended or revised. Taxonomy design decisions regarding the used classification structures, procedures and descriptive bases are usually not well described and motivated. (C) 2017 The Authors. Published by Elsevier B.V.

  • 42.
    Usman, Muhammad
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Börstler, Jürgen
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Effort estimation in agile software development: a survey on the state of the practice2015In: Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering (EASE 2015), ACM Digital Library, 2015Conference paper (Refereed)
  • 43.
    Usman, Muhammad
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Weidt, F.
    Britto, R.
    Effort estimation in Agile Software Development: A systematic literature review2014Conference paper (Refereed)
    Abstract [en]

    Ever since the emergence of agile methodologies in 2001, many software companies have shifted to Agile Software Development (ASD), and since then many studies have been conducted to investigate effort estimation within such context; however to date there is no single study that presents a detailed overview of the state of the art in effort estimation for ASD. Objectives: The aim of this study is to provide a detailed overview of the state of the art in the area of effort estimation in ASD. Method: To report the state of the art, we conducted a systematic literature review in accordance with the guidelines proposed in the evidence-based software engineering literature. Results: A total of 25 primary studies were selected; the main findings are: i) Subjective estimation techniques (e.g. expert judgment, planning poker, use case points estimation method) are the most frequently applied in an agile context; ii) Use case points and story points are the most frequently used size metrics respectively; iii) MMRE (Mean Magnitude of Relative Error) and MRE (Magnitude of Relative Error) are the most frequently used accuracy metrics; iv) team skills, prior experience and task size are cited as the three important cost drivers for effort estimation in ASD; and v) Extreme Programming (XP) and SCRUM are the only two agile methods that are identified in the primary studies. Conclusion: Subjective estimation techniques, e.g. expert judgment-based techniques, planning poker or the use case points method, are the one used the most in agile effort estimation studies. As for the size metrics, the ones that were used the most in the primary studies were story points and use case points. Several research gaps were identified, relating to the agile methods, size metrics and cost drivers, thus suggesting numerous possible avenues for future work

  • 44.
    Usman, Muhammad
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Weidt, Francila
    Britto, Ricardo
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Effort estimation in agile software development: a systematic literature review2014In: Proceedings of the 10th International Conference on Predictive Models in Software Engineering, 2014, p. 82-91Conference paper (Refereed)
    Abstract [en]

    Context: Ever since the emergence of agile methodologies in 2001, many software companies have shifted to Agile Software Development (ASD), and since then many studies have been conducted to investigate effort estimation within such context; however to date there is no single study that presents a detailed overview of the state of the art in effort estimation for ASD. Objectives: The aim of this study is to provide a detailed overview of the state of the art in the area of effort estimation in ASD. Method: To report the state of the art, we conducted a systematic literature review in accordance with the guidelines proposed in the evidence-based software engineering literature.Results: A total of 25 primary studies were selected; the main findings are: i) Subjective estimation techniques (e.g. expert judgment, planning poker, use case points estimation method) are the most frequently applied in an agile context; ii) Use case points and story points are the most frequently used size metrics respectively; iii) MMRE (Mean Magnitude of Relative Error) and MRE (Magnitude of Relative Error) are the most frequently used accuracy metrics; iv) team skills, prior experience and task size are cited as the three important cost drivers for effort estimation in ASD; and v) Extreme Programming (XP) and SCRUM are the only two agile methods that are identified in the primary studies. Conclusion: Subjective estimation techniques, e.g. expert judgment-based techniques, planning poker or the use case points method, are the one used the most in agile effort estimation studies. As for the size metrics, the ones that were used the most in the primary studies were story points and use case points. Several research gaps were identified, relating to the agile methods, size metrics and cost drivers, thus suggesting numerous possible avenues for future work.

  • 45.
    Vishnubhotla, Sai Datta
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    An insight into the capabilities of professionals and teams in agile software development: A systematic literature review2018In: PROCEEDINGS OF 2018 7TH INTERNATIONAL CONFERENCE ON SOFTWARE AND COMPUTER APPLICATIONS (ICSCA 2018), Association for Computing Machinery , 2018, p. 10-19Conference paper (Refereed)
    Abstract [en]

    Background: Previous studies investigated key characteristics of software engineers and factors influencing the performance of individuals, productivity of teams and project success within agile software development (ASD). They aided in the active investigation of human aspects in ASD. However, capability measurement and prediction with respect to agile workforce, owing to its importance, is an area that needs spotlight. Objective: The objective of this paper is to present the state of the art relating to capability measurement of software engineers and teams working in ASD projects. Method: We carried out a systematic literature review (SLR) focused on identifying attributes used for measuring and predicting the capabilities of individual software engineers and teams. Results: Evidence from 16 studies showed attributes that can measure capabilities of engineers and teams, and also attributes that can be used as capability predictors. Further, different instruments used to measure those attributes were presented. Conclusions: The SLR presented a wide list of attributes that were grouped into various categories. This information can be used by project managers as, for example, a checklist to consider when allocating software engineers to teams and in turn teams to a project. Further, this study indicated the necessity for an investigation into capability prediction models. © 2018 Association for Computing Machinery.

  • 46.
    Vishnubhotla, Sai Datta
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Designing a capability-centric web tool to support agile team composition and task allocation: A work in progress2018In: 2018 IEEE/ACM 11TH INTERNATIONAL WORKSHOP ON COOPERATIVE AND HUMAN ASPECTS OF SOFTWARE ENGINEERING (CHASE), IEEE Computer Society , 2018, Vol. F137813, p. 41-44Conference paper (Refereed)
    Abstract [en]

    A significant number of studies reported models for competence profiling, measuring capabilities of professionals and recommendation systems for roles within agile software development (ASD). These models coordinated in human resource management within ASD. However, in the light of swift, incremental and iterative nature of ASD practices, designing solutions that easily integrate capability measurements with ongoing project management routines, is an important area for investigation. With the support of interviews, grounded theory procedure and workshops, we identified the aspects valued by our industrial collaborator while allocating professionals to tasks. This information was further utilized towards devising a framework for capability-centric Web tool. This tool provides a one-stop solution for project managers to create projects, keep track of capabilities and execute allocation routines. © 2018 ACM.

  • 47.
    Wnuk, Krzysztof
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    The Project management perspective on Software Value: A Literature Review2015Conference paper (Refereed)
    Abstract [en]

    Context: To remain competitive, innovative and to grow, companies must change from cost-based decision-making to value-based decision-making where the decisions taken maximize software value and support company’s overall value creation. Objective: The objective of this paper is to complement and expand an existing classification of value aspects within the context of product management and development with additional aspects relating to value within the context of project management and development. Method: In this study, we present the results from a snowballing literature review that focuses on software value in software project management. In the research for relevance literature we focus on software value aspects different than cost. Results: We have identified nine primary studies in two snowball iterations. From these studies, we derived three categories of value aspects: financial, risk analysis and process improvement based on value identification.

1 - 47 of 47
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