Context: Methodological research on systematic literature reviews (SLRs)in Software Engineering (SE)has so far focused on developing and evaluating guidelines for conducting systematic reviews. However, the support for quality assessment of completed SLRs has not received the same level of attention. Objective: To raise awareness of the need for a critical appraisal tool (CAT)for assessing the quality of SLRs in SE. To initiate a community-based effort towards the development of such a tool. Method: We reviewed the literature on the quality assessment of SLRs to identify the frequently used CATs in SE and other fields. Results: We identified that the CATs currently used is SE were borrowed from medicine, but have not kept pace with substantial advancements in the field of medicine. Conclusion: In this paper, we have argued the need for a CAT for quality appraisal of SLRs in SE. We have also identified a tool that has the potential for application in SE. Furthermore, we have presented our approach for adapting this state-of-the-art CAT for assessing SLRs in SE. © 2019 The Authors

Context: The trust in systematic literature reviews (SLRs) to provide credible recommendations is critical for establishing evidence-based software engineering (EBSE) practice. The reliability of SLR as a method is not a given and largely depends on the rigor of the attempt to identify, appraise and aggregate evidence. Previous research, by comparing SLRs on the same topic, has identified search as one of the reasons for discrepancies in the included primary studies. This affects the reliability of an SLR, as the papers identified and included in it are likely to influence its conclusions. Objective: We aim to propose a comprehensive evaluation checklist to assess the reliability of an automated-search strategy used in an SLR. Method: Using a literature review, we identified guidelines for designing and reporting automated-search as a primary search strategy. Using the aggregated design, reporting and evaluation guidelines, we formulated a comprehensive evaluation checklist. The value of this checklist was demonstrated by assessing the reliability of search in 27 recent SLRs. Results: Using the proposed evaluation checklist, several additional issues (not captured by the current evaluation checklist) related to the reliability of search in recent SLRs were identified. These issues severely limit the coverage of literature by the search and also the possibility to replicate it. Conclusion: Instead of solely relying on expensive replications to assess the reliability of SLRs, this work provides means to objectively assess the likely reliability of a search-strategy used in an SLR. It highlights the often-assumed aspect of repeatability of search when using automated-search. Furthermore, by explicitly considering repeatability and consistency as sub-characteristics of a reliable search, it provides a more comprehensive evaluation checklist than the ones currently used in EBSE. © 2018 Elsevier B.V.

Requirements engineering (RE) being a foundation of software development has gained a great recognition in the recent era of prevailing software industry. A number of journals and conferences have published a great amount of RE research in terms of various tools, techniques, methods, and frameworks, with a variety of processes applicable in different software development domains. The plethora of empirical RE research needs to be synthesized to identify trends and future research directions. To represent a state-of-the-art of requirements engineering, along with various trends and opportunities of empirical RE research, we conducted a systematic mapping study to synthesize the empirical work done in RE. We used four major databases IEEE, ScienceDirect, SpringerLink and ACM and Identified 270 primary studies till the year 2012. An analysis of the data extracted from primary studies shows that the empirical research work in RE is on the increase since the year 2000. The requirements elicitation with 22 % of the total studies, requirements analysis with 19 % and RE process with 17 % are the major focus areas of empirical RE research. Non-functional requirements were found to be the most researched emerging area. The empirical work in the sub-area of requirements validation and verification is little and has a decreasing trend. The majority of the studies (50 %) used a case study research method followed by experiments (28 %), whereas the experience reports are few (6 %). A common trend in almost all RE sub-areas is about proposing new interventions. The leading intervention types are guidelines, techniques and processes. The interest in RE empirical research is on the rise as whole. However, requirements validation and verification area, despite its recognized importance, lacks empirical research at present. Furthermore, requirements evolution and privacy requirements also have little empirical research. These RE sub-areas need the attention of researchers for more empirical research. At present, the focus of empirical RE research is more about proposing new interventions. In future, there is a need to replicate existing studies as well to evaluate the RE interventions in more real contexts and scenarios. The practitioners’ involvement in RE empirical research needs to be increased so that they share their experiences of using different RE interventions and also inform us about the current requirements-related challenges and issues that they face in their work. © 2016 Springer-Verlag London

Background: Empirical studies involving human participants need to follow procedures to avoid causing harm to the subjects. However, it is not always clear how researchers should report these procedures. Aim: This study investigates how researchers report ethical issues in the software engineering journal publications, particularly informed consent, confidentiality, and anonymity. Method: We conducted a literature review to understand the reporting of ethical issues in software engineering journals. In addition, in a workshop, we discussed the importance of reporting the different ethical issues. Results: The results indicate that 49 out of 95 studies reported some ethical issues. Only six studies discussed all three ethical issues. The subjects were mainly informed about the study purpose and procedure. There are limited discussions on how the subjects were informed about the risks involved in the study. Studies reported on how authors ensured confidentiality have also discussed anonymity in most cases. The results of the workshop discussion indicate that reporting ethical issues is important to improve the reliability of the research results. We propose a checklist based on the literature review, which we validated through a workshop. Conclusion: The checklist proposed in this paper is a step towards enhancing ethical reporting in software engineering research.

Contemporary practices such as InnerSource (adopting open source practices within an organization), continuous integration and delivery (CI/CD), and the use of microservices promote software reuse. Although the benefits of individual contemporary practices on reuse may be known, the implications of collective contemporary practices, mainly challenges and improvements to mitigate the challenges, are to a large extent unknown. In this study, we investigate the additional effort (cost factors), benefits, challenges, and potential improvements in contemporary reuse at Ericsson.

We performed the study in two phases: a) the initial data collection based on a combination of data collection methods (e.g., interviews, discussions, company portals), and b) a follow-up group discussion after a year to understand the status of the challenges and improvements identified in the first phase.

Our results indicate that developing reusable assets resulted in upfront cost factors, such as additional effort in ensuring compliance. Furthermore, development with reuse also resulted in cost factors, for example, additional effort in integrating and understanding reusable assets. Ericsson perceived the cost factors as an investment resulting in long-term benefits such as improved quality, productivity, customer experience, and way of working. The main challenge faced by Ericsson was the pressure on the producers of reusable assets. Our study shows how InnerSource can lead to an increase in contributions to reusable assets. Furthermore, Ericsson implemented measures like automating compliance checks that improved the maturity of reusable assets, resulting in an increase in their reuse.

In summary, effective use of contemporary practices such as InnerSource and CI/CD, along with the use of microservices, can facilitate large scale reuse. © The Author(s) 2024.

Contemporary practices such as InnerSource and DevOps promote software reuse. This study investigates the implications of using contemporary practices on software reuse. In particular, we investigate the costs, benefits, challenges, and potential improvements in contemporary reuse at Ericsson. We performed the study in two phases: a) the initial data collection based on a combination of data collection methods (e.g., interviews, discussions, company portals), and b) a follow-up group discussion after a year to understand the status of the challenges and improvements identified in the first phase. Our results indicate that developing reusable assets resulted in upfront costs, such as additional effort in ensuring compliance. Furthermore, development with reuse also resulted in additional effort, for example, in integrating and understanding reusable assets. Ericsson perceived the additional effort as an investment resulting in long-term benefits such as improved quality, productivity, customer experience, and way of working. Ericsson's main challenge was increased pressure on the producers of reusable assets, which was mitigated by scaling the InnerSource adoption. InnerSource success is evident from the increase in the contributions to reusable assets. In addition, Ericsson implemented measures such as automating the compliance check, which enhanced the maturity of reusable assets and resulted in increased reuse.  

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. 

Designing and assessing learning outcomes could be a challenging activity for any SoftwareEngineering (SE) educator. To support the process of designing and assessing SE courses, educators have been applied the cognitive domain of Bloom's taxonomy. However, to the best of our knowledge, the evidence on the usage of Bloom's taxonomy in SE higher education has not yet been systematically aggregated or reviewed. Therefore, in this paper we report the state of the art on the usage of Bloom's taxonomy in SE education, identified by conducted a systematic mapping study. As a result of the performed systematic mapping study, 26 studies were deemed as relevant. The main findings from these studies are: i) Bloom's taxonomy has mostly been applied at undergraduate level for both design and assessment of software engineering courses; ii) software construction is the leading SE subarea in which Bloom's taxonomy has been applied. The results clearly point out the usefulness of Bloom's taxonomy in the SE education context. We intend to use the results from this systematic mapping study to develop a set of guidelines to support the usage of Bloom's taxonomycognitive levels to design and assess SE courses.

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.

Background: Software practice is evolving with changing technologies and practices such as InnerSource, DevOps, and microservices. It is important to investigate the impact of contemporary software engineering (SE) practices on software reuse.Aim: This study aims to characterize software reuse in contemporary SE practices and investigate its implications in terms of costs, benefits, challenges, and potential improvements in a medium-sized company.Method: We performed an exploratory case study by conducting interviews, group discussions, and reviewing company documentation to investigate software reuse in the context of contemporary SE practices in the case company.Results: The results indicate that the development for reuse in contemporary SE practices incurs additional coordination, among other costs. Development with reuse led to relatively fewer additional costs and resulted in several benefits such as better product quality and less development and delivery time. Ownership of reusable assets is challenging in contemporary SE practice. InnerSource practices may help mitigate the top perceived challenges: discoverability and ownership of the reusable assets, knowledge sharing and reuse measurement.Conclusion: Reuse in contemporary SE practices is not without additional costs and challenges. However, the practitioners perceive costs as investments that benefit the company in the long run.

Companies adopt various software reuse practices to achieve more benefits. Existing secondary studies aggregated primary evidence on software reuse benefits more than the costs. It is important to know how the software reuse costs and benefits are measured and the strength of the evidence. This study aims to conduct a systematic literature review to identify the observed software reuse costs and benefits from industrial cases and investigate how the identified software reuse costs and benefits are measured. In addition, we also assess the quality of the included primary studies to understand the strength of evidence behind the identified software reuse costs and benefits. We included 30 primary studies on software reuse costs and benefits in industrial cases using a mixed search strategy - automated search and snowballing. We identified nine software reuse benefits, six software reuse costs, and the metrics used to measure them. Better quality and improved productivity are the most investigated software reuse benefits, and most primary studies that report these two benefits are of good or moderate quality. Few primary studies reported software reuse costs, and most of them are of low quality. Generally, the quality of primary studies reporting software reuse benefits is better than those reporting software reuse costs.

Context: Software reuse costs and benefits have been investigated in several primary studies, which have been aggregated in multiple secondary studies as well. However, existing secondary studies on software reuse have not critically appraised the evidence in primary studies. Moreover, there has been relatively less focus on how software reuse costs and benefits were measured in the primary studies, and the aggregated evidence focuses more on software reuse benefits than reuse costs. Objective: This study aims to cover the gaps mentioned in the context above by synthesizing and critically appraising the evidence reported on software reuse costs and benefits from industrial cases. Method: We used a systematic literature review (SLR) to conduct this study. The results of this SLR are based on a final set of 30 primary studies. Results: We identified nine software reuse benefits and six software reuse costs, in which better quality and improved productivity were investigated the most. The primary studies mostly used defect-based and development time-based metrics to measure reuse benefits and costs. Regarding the reuse practices, the results show that software product lines, verbatim reuse, and systematic reuse were the top investigated ones, contributing to more reuse benefits. The quality assessment of the primary studies showed that most of them are either of low (20%) or moderate (67%) quality. Conclusion: Based on the number and quality of the studies, we conclude that the strength of evidence for better quality and improved productivity as reuse benefits is high. There is a need to conduct more high quality studies to investigate, not only other reuse costs and benefits, but also how relatively new reuse-related practices, such as InnerSource and microservices architecture, impact software reuse. © 2024 The Author(s)

Background: InnerSource consists of the use of open source development techniques within the corporation. It helps improve software reuse through increased transparency and inter-team collaboration. Companies need to understand their context and specific needs before deciding to adopt any specific InnerSource practices since they cannot apply all InnerSource practices at once. Aim: This study aims to support the case company in assessing its readiness for adopting InnerSource practices to improve its internal reuse, identify and prioritize the improvement areas, and identify suitable solutions. Method: We performed a case study using a questionnaire and a workshop to check the current and desired status of adopting InnerSource practices and collect potential solutions. Results: The study participants identified that the company needs to prioritize the improvements related to the discoverability, communication channels, and ownership of the reusable assets. In addition, they identified certain InnerSource practices as solutions for the prioritized improvement areas, such as better structured repositories for storing and searching the reusable assets and standardized documentation of the reusable assets. Conclusion: The questionnaire instrument aids the case company in identifying the improvement areas related to InnerSource and reuse practices. InnerSource practices could improve the development and maintenance of reusable assets. Keywords: InnerSource, software reuse, readiness © 2023 Owner/Author.

Context: A large number of source code metrics are reported in the literature. It is necessary to systematically collect, describe and classify source code metrics to support research and practice.Objective: We aim to utilize existing secondary studies to develop a cat- alog of source code metrics together with their descriptions. The catalog will also provide information about which units of code (e.g., operators, operands, lines of code, variables, parameters, code blocks, or functions) are used to measure the internal quality attributes and the scope on which they are collected. 

Method: We conducted a tertiary study to identify secondary studies re- porting source code metrics. We have classified the source code metrics according to the measured internal quality attributes, the units of code used in the measures, and the scope at which the source code metrics are collected. 

Results: From 711 secondary studies, we identified 52 relevant secondary studies. We reported 423 source code metrics together with their de- scriptions and the internal quality attributes they measure. Source code metrics predominantly incorporate function as a unit of code to measure internal quality attributes. In contrast, several source code metrics use more than one unit of code when measuring internal quality attributes. Nearly 51% of the source code metrics are collected at the class scope, while almost 12% and 15% of source code metrics are collected at module and application levels, respectively. 

Conclusions: Researchers and practitioners can use the extensive catalog to assess which source code metrics meet their individual needs based on the description and classification scheme presented. 

Context: Several secondary studies have investigated the relationship between internal quality attributes, source code metrics and external quality attributes. Sometimes they have contradictory results. Objective: We synthesize evidence of the link between internal quality attributes, source code metrics and external quality attributes along with the efficacy of the prediction models used. Method: We conducted a tertiary review to identify, evaluate and synthesize secondary studies. We used several characteristics of secondary studies as indicators for the strength of evidence and considered them when synthesizing the results. Results: From 711 secondary studies, we identified 15 secondary studies that have investigated the link between source code and external quality. Our results show : (1) primarily, the focus has been on object-oriented systems, (2) maintainability and reliability are most often linked to internal quality attributes and source code metrics, with only one secondary study reporting evidence for security, (3) only a small set of complexity, coupling, and size-related source code metrics report a consistent positive link with maintainability and reliability, and (4) group method of data handling (GMDH) based prediction models have performed better than other prediction models for maintainability prediction. Conclusions: Based on our results, lines of code, coupling, complexity and the cohesion metrics from Chidamber & Kemerer (CK) metrics are good indicators of maintainability with consistent evidence from high and moderate-quality secondary studies. Similarly, four CK metrics related to coupling, complexity and cohesion are good indicators of reliability, while inheritance and certain cohesion metrics show no consistent evidence of links to maintainability and reliability. Further empirical studies are needed to explore the link between internal quality attributes, source code metrics and other external quality attributes, including functionality, portability, and usability. The results will help researchers and practitioners understand the body of knowledge on the subject and identify future research directions. © 2023 The Author(s)

Software engineering practitioners have information needs to support strategic, tactical and operational decision-making. However, there is scarce research on understanding which information needs exist and how they are currently fulfilled in practice. This study aims to identify the information needs, the frequency of their occurrence, the sources of information used to satisfy the needs, and the perception of practitioners regarding the usefulness of the sources currently used. For this purpose, a literature review was conducted to aggregate the current state of understanding in this area. We built on the results of the literature review and developed further insights through in-depth interviews with 17 practitioners. We further triangulated the findings from these two investigations by conducting a web-based survey (with 83 completed responses). Based on the results, we infer that information regarding product design, product architecture and requirements gathering are the most frequently faced needs. Software practitioners mostly use blogs, community forums, product documentation, and discussion with colleagues to address their information needs.

The open source software (OSS) assessment has become important given the increased adoption of OSS in commercial product development. Researchers proposed many OSS assessment models. However, little is known about the industrial relevance of the models. In this study, we proposed an automated tool based on the OSS assessment attributes identified together with a European cloud provider company. We analyzed 51 repositories to observe patterns in maintenance activities over their lifetime (from inception to the latest release). Based on the analysis, we propose a novel approach for evaluating the maturity of the OSS project. Finally, we assessed the usefulness of our automated solution in a pilot study. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Kubernetes is a free, open-source container orchestration system for deploying and managing Docker containers that host microservices. Kubernetes cluster logs help in determining the reason for the failure. However, as systems become more complex, identifying failure reasons manually becomes more difficult and time-consuming. This study aims to identify effective and efficient classification algorithms to automatically determine the failure reason. We compare five classification algorithms, Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting Classifier, and Multilayer Perceptron. Our results indicate that Random Forest produces good accuracy while requiring fewer computational resources than other algorithms. © 2023 Owner/Author.

Code review is a commonly used practice in software development. It refers to the process of reviewing new code changes before they are merged with the code base. However, to perform the review, developers are mostly assigned manually to code changes. This may lead to problems such as: a time-consuming selection process, limited pool of known candidates and risk of over-allocation of a few reviewers. To address the above problems, we developed Carrot, a machine learning-based tool to recommend code reviewers. We conducted an improvement case study at Ericsson. We evaluated Carrot using a mixed approach. we evaluated the prediction accuracy using historical data and the metrical Mean Reciprocal Rank (MRR). Furthermore, we deployed the tool in one Ericsson project and evaluated how adequate the recommendations were from the point of view of the tool users and the recommended reviewers.We also asked the opinion of senior developers about the usefulness of the tool. The results show that Carrot can help identify relevant non-obvious reviewers and be of great assistance to new developers. However, there were mixed opinions on Carrot's ability to assist with workload balancing and the decrease code review lead time. © 2020 IEEE Computer Society. All rights reserved.

Contribution: In this study, we accumulated the knowledge and generated evidence on how and in what context CDIO framework has been used in software engineering (SE) education. The aggregated evidence will enable SE academics in making informed decisions while adopting CDIO for SE education and build upon it. Background: CDIO framework is relevant for SE as it focuses on enabling engineering graduates in conceiving, designing, implementing, and operating complex systems and products. We were not able to find any study that identifies and aggregates the evidence on the use of CDIO for SE education. Research Questions: This study attempts to answer the following research questions: 1) how CDIO has been used in SE education? and 2) what are the experiences of academics in applying the CDIO framework in SE education? Methodology: Using a mixed-method approach (systematic mapping study and interview study with experienced academics in SE), we established the state of the art and practice on the use of CDIO in SE education. Findings: Getting a commitment from the higher management, teachers, and students is a major challenge in the adoption of the CDIO initiative followed by a lack of competence, finance, and resources. Ownership, motivation, persistence, and training of teachers and students are required not only to adopt CDIO for SE but also to sustain it.

Background: Systematic literature reviews (SLRs) have become a standard practice as part of software engineering (SE) research, although their quality varies. To build on the reviews, both for future research and industry practice, they need to be of high quality.Aim: To assess the quality of SLRs in SE, we put forward an appraisal instrument for SLRs.Method: A well-established appraisal instrument from research in healthcare was used as a starting point to develop the instrument. It is adapted to SE using guidelines, checklists, and experiences from SE. The first version was reviewed by four external experts on SLRs in SE and updated based on their feedback. To demonstrate its use, the updated version was also used by the authors to assess a sample of six selected systematic literature studies.Results: The outcome of the research is an appraisal instrument for quality assessment of SLRs in SE. The instrument includes 15 items with different options to capture the quality. The instrument also supports consolidating the items into groups, which are then used to assess the overall quality of an SLR.Conclusion: The presented instrument may be helpful support for an appraiser in assessing the quality of SLRs in SE.

This article presents an ecosystem that Ericsson developed to systematically practice large-scale reuse of microservices in a cloud-native context. We discuss how various ecosystem aspects, such as its continuous delivery mechanism, marketplace, and automated checking of design rules, facilitated the development and reuse of microservices across Ericsson. We also share lessons learned while developing the ecosystem including the initiatives related to the adoption of InnerSource practices for sustaining the ecosystem. 

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.

Context: Software projects frequently incur schedule and budget overruns. Planning and estimation are particularlychallenging in large and globally distributed projects. While software engineering researchers have beeninvestigating effort estimation for many years to help practitioners to improve their estimation processes, there is littleresearch about effort estimation in large-scale distributed agile projects.Objective: The main objective of this paper is three-fold: i) to identify how effort estimation is carried out in largescaledistributed agile projects; ii) to analyze the accuracy of the effort estimation processes in large-scale distributedagile projects; and iii) to identify the factors that impact the accuracy of effort estimates in large-scale distributed agileprojects.Method: We performed an exploratory longitudinal case study. The data collection was operationalized througharchival research and semi-structured interviews.Results: The main findings of this study are: 1) underestimation is the dominant trend in the studied case, 2) reestimationat the analysis stage improves the accuracy of the effort estimates, 3) requirements with large size/scopeincur larger effort overruns, 4) immature teams incur larger effort overruns, 5) requirements developed in multi-sitesettings incur larger effort overruns as compared to requirements developed in a collocated setting, and 6) requirementspriorities impact the accuracy of the effort estimates.Conclusion: Effort estimation is carried out at quotation and analysis stages in the studied case. It is a challengingtask involving coordination amongst many different stakeholders. Furthermore, lack of details and changes in requirements,immaturity of the newly on-boarded teams and the challenges associated with the large-scale add complexitiesin the effort estimation process.

In Agile Software Development (ASD) effort estimation plays an important role during release and iteration planning. The state of the art and practice on effort estimation in ASD have been recently identified. However, this knowledge has not yet been organized. The aim of this study is twofold: (1) To organize the knowledge on effort estimation in ASD and (2) to use this organized knowledge to support practice and the future research on effort estimation in ASD. We applied a taxonomy design method to organize the identified knowledge as a taxonomy of effort estimation in ASD. The proposed taxonomy offers a faceted classification scheme to characterize estimation activities of agile projects. Our agile estimation taxonomy consists of four dimensions: estimation context, estimation technique, effort predictors and effort estimate. Each dimension in turn has several facets. We applied the taxonomy to characterize estimation activities of 10 agile projects identified from the literature to assess whether all important estimation-related aspects are reported. The results showed that studies do not report complete information related to estimation. The taxonomy was also used to characterize the estimation activities of four agile teams from three different software companies. The practitioners involved in the investigation found the taxonomy useful in characterizing and documenting the estimation sessions. © 2017 The Author(s).

Regulatory compliance is a well-studied area, including research on how to model, check, analyse, enact, and verify compliance of software. However, while the theoretical body of knowledge is vast, empirical evidence on challenges with regulatory compliance, as faced by industrial practitioners particularly in the Software Engineering domain, is still lacking. In this paper, we report on an industrial case study which aims at providing insights into common practices and challenges with checking and analysing regulatory compliance, and we discuss our insights in direct relation to the state of reported evidence. Our study is performed at Ericsson AB, a large telecommunications company, which must comply to both locally and internationally governing regulatory entities and standards such as GDPR. The main contributions of this work are empirical evidence on challenges experienced by Ericsson that complement the existing body of knowledge on regulatory compliance. © 2020, Springer Nature Switzerland AG.

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

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.

There has been a lot of research on personality and its impact on software engineering practice. These studies use different psychological tests to identify personality types of software practitioners. The administration of these tests requires expertise. As the humans are involved, other ethical issues, such as consent, also become important. In this study, we evaluated a small sample of 15 studies that used a psychological test Myers-Briggs Type Indicator (MBTI) in a software engineering context with respect to different ethical issues related to informed consent, qualification of the test administrators and the use of appropriate tests. The results show that most of the studies in our sample seriously lack with respect to various ethical issues. © 2019 Association for Computing Machinery.

Expert judgment based effort estimation techniques are widely used for estimating software effort. In the absence of process support, experts may overlook important factors during estimation, leading to inconsistent estimates. This might cause underestimation, which is a common problem in software projects. This multi-case study aims to improve expert estimation of software development effort. Our goal is two-fold: 1) to propose a process to develop and evolve estimation checklists for agile teams, and 2) to evaluate the usefulness of the checklists in improving expert estimation processes. The use of checklists improved the accuracy of the estimates in two case companies. In particular, the underestimation bias was reduced to a large extent. For the third case, we could not perform a similar analysis, due to the unavailability of historical data. However, when checklist was used in two sprints, the estimates were quite accurate (median Balanced Relative Error (BRE) bias of -0.05 ). The study participants from the case companies observed several benefits of using the checklists during estimation, such as increased confidence in estimates, improved consistency due to help in recalling relevant factors, more objectivity in the process, improved understanding of the tasks being estimated, and reduced chances of missing tasks.