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Petersen, Kai
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Publications (10 of 101) Show all publications
Molléri, J. S., Petersen, K. & Mendes, E. (2020). An Empirically Evaluated Checklist for Surveys in Software Engineering. Information and Software Technology, Article ID 106240.
Open this publication in new window or tab >>An Empirically Evaluated Checklist for Surveys in Software Engineering
2020 (English)In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, article id 106240Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Checklist, Assessment, Survey, Methodology
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-17645 (URN)10.1016/j.infsof.2019.106240 (DOI)
Available from: 2019-02-27 Created: 2019-02-27 Last updated: 2019-12-27Bibliographically approved
Minhas, N. M., Petersen, K., Börstler, J. & Wnuk, K. (2020). Regression testing for large-scale embedded software development: Exploring the state of practice. Information and Software Technology, 120, Article ID 106254.
Open this publication in new window or tab >>Regression testing for large-scale embedded software development: Exploring the state of practice
2020 (English)In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 120, article id 106254Article in journal (Refereed) Published
Abstract [en]

Context: A majority of the regression testing techniques proposed by academics have not been adopted in industry. To increase adoption rates, we need to improve our understanding of the practitioners’ perspectives on regression testing. Objective: This study aims at exploring the regression testing state of practice in the large-scale embedded software development. The study has two objectives: 1) to highlight the potential challenges in practice, and 2) to identify the industry-relevant research areas regarding regression testing. Method: We conducted a qualitative study in two large-scale embedded software development companies, where we carried out semi-structured interviews with representatives from five software testing teams. Results: The practitioners run regression testing mostly with limited scope based on the size, complexity, and location of the change. Test cases are prioritized on the basis of risk and critical functionality. The practitioners rely on their knowledge and experience for the decision making regarding selection and prioritization of test cases. The companies are using both automated and manual regression testing, and mainly rely on in-house developed tools for test automation. The challenges identified in the companies are: time to test, information management, test suite maintenance, lack of communication, test selection/prioritization, lack of assessment, etc. Regression testing goals identified in this study are customer satisfaction, critical defect detection, confidence, effectiveness, efficiency, and controlled slip through of faults. Conclusions: Considering the current state of practice and the identified challenges we conclude that there is a need to reconsider the regression test strategy in the companies. Researchers need to analyze the industry perspective when proposing new regression testing techniques. © 2019

Place, publisher, year, edition, pages
Elsevier B.V., 2020
Keywords
Challenges, Goals, Multi-case study, Practices, Regression testing, Customer satisfaction, Decision making, Embedded software, Information management, Regression analysis, Software design, Testing, Software testing
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-19134 (URN)10.1016/j.infsof.2019.106254 (DOI)2-s2.0-85077514972 (Scopus ID)
Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-01-28Bibliographically approved
Minhas, N. M., Masood, S., Petersen, K. & Nadeem, A. (2019). A Systematic Mapping of Test Case Generation Techniques Using UML Interaction Diagrams. Journal of Software: Evolution and Process, Article ID e2235.
Open this publication in new window or tab >>A Systematic Mapping of Test Case Generation Techniques Using UML Interaction Diagrams
2019 (English)In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, article id e2235Article in journal (Refereed) Epub ahead of print
Abstract [en]

Testing plays a vital role for assuring software quality. Among the activities performed during testing process, test cases generation is a challenging and labor intensive task. Test case generation techniques based on UML models are getting the attention of researchers and practitioners. This study provides a systematic mapping of test case generation techniques based on interaction diagrams. The study compares the test case generation techniques, regarding their capabilities and limitations, and it also assesses the reporting quality of the primary studies. It has been revealed that UML interaction diagrams based techniques are mainly used for integration testing. The majority of the techniques are using sequence diagrams as input models, while some are using collaboration. A notable number of techniques are using interaction diagram along with some other UML diagram for test case generation. These techniques are mainly focusing on interaction, scenario, operational, concurrency, synchronization and deadlock related faults.

From the results of this study, we can conclude that the studies presenting test case generation techniques using UML interaction diagrams failed to illustrate the use of rigorous methodology, and these techniques did not demonstrate the empirical evaluation in an industrial context. Our study revealed the need for tool support to facilitate the transfer of solutions to industry.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019
Keywords
Software testing, Test case generation, Interaction diagrams, Model based testing, Systematic mapping
National Category
Computer Systems Software Engineering
Identifiers
urn:nbn:se:bth-17363 (URN)10.1002/smr.2235 (DOI)000503853200001 ()
Projects
EASE - Embedded Applications Software Engineering
Funder
Vinnova, 2015-03235
Available from: 2018-11-30 Created: 2018-11-30 Last updated: 2020-01-09Bibliographically approved
Molléri, J. S., Petersen, K. & Mendes, E. (2019). CERSE - Catalog for empirical research in software engineering: A Systematic mapping study. Information and Software Technology, 105, 117-149
Open this publication in new window or tab >>CERSE - Catalog for empirical research in software engineering: A Systematic mapping study
2019 (English)In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 105, p. 117-149Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier B.V., 2019
Keywords
Empirical methods, Empirical research, Mapping study, Decision making, Mapping, Software engineering, Assessment instruments, Empirical method, Empirical research in software engineering, Empirical Software Engineering, Knowledge organization systems, Mapping studies, Systematic mapping studies, Knowledge management
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-17108 (URN)10.1016/j.infsof.2018.08.008 (DOI)000452586900008 ()2-s2.0-85054061108 (Scopus ID)
Available from: 2018-10-11 Created: 2018-10-11 Last updated: 2019-04-24Bibliographically approved
Alégroth, E., Gorschek, T., Petersen, K. & Mattsson, M. (2019). Characteristics that affect Preference of Decision Models for Asset Selection: An Industrial Questionnaire Survey. Software quality journal
Open this publication in new window or tab >>Characteristics that affect Preference of Decision Models for Asset Selection: An Industrial Questionnaire Survey
2019 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367Article in journal (Refereed) Epub ahead of print
Abstract [en]

Modern software development relies on a combination of development and re-use of technical asset, e.g. software components, libraries and APIs.In the past, re-use was mostly conducted with internal assets but today external; open source, customer off-the-shelf (COTS) and assets developed through outsourcing are also common.This access to more asset alternatives presents new challenges regarding what assets to optimally chose and how to make this decision.To support decision-makers, decision-theory has been used to develop decision models for asset selection.However, very little industrial data has been presented in literature about the usefulness, or even perceived usefulness, of these models.Additionally, only limited information has been presented about what model characteristics that determine practitioner preference towards one model over another.

Objective: The objective of this work is to evaluate what characteristics of decision models for asset selection that determine industrial practitioner preference of a model when given the choice of a decision-model of high precision or a model with high speed.

Method: An industrial questionnaire survey is performed where a total of 33 practitioners, of varying roles, from 18 companies are tasked to compare two decision models for asset selection.Textual analysis and formal and descriptive statistics are then applied on the survey responses to answer the study's research questions.

Results: The study shows that the practitioners had clear preference towards the decision model that emphasised speed over the one that emphasised decision precision.This conclusion was determined to be because one of the models was perceived faster, had lower complexity, had, was more flexible in use for different decisions, was more agile how it could be used in operation, its emphasis on people, its emphasis on ``good enough'' precision and ability to fail fast if a decision was a failure.Hence, seven characteristics that the practitioners considered important for their acceptance of the model.

Conclusion: Industrial practitioner preference, which relates to acceptance, of decision models for asset selection is dependent on multiple characteristics that must be considered when developing a model for different types of decisions such as operational day-to-day decisions as well as more critical tactical or strategic decisions.The main contribution of this work are seven identified characteristics that can serve as industrial requirements for future research on decision models for asset selection.

Keywords
Decision-models, Characteristics, Industrial study, Survey, Model comparison
National Category
Computer Systems Software Engineering
Identifiers
urn:nbn:se:bth-18687 (URN)10.1007/s11219-019-09489-8 (DOI)000504580300001 ()
Projects
ORION
Funder
Knowledge Foundation, 20140218
Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2020-01-23Bibliographically approved
Alégroth, E., Gorschek, T., Petersen, K. & Mattsson, M. (2019). Characteristics that affect Preference of Decision Models for Asset Selection: An Industrial Questionnaire Survey - Appendix A: Questionnaire Introduction. Decision-making in Practice / Appendix B: Survey results.
Open this publication in new window or tab >>Characteristics that affect Preference of Decision Models for Asset Selection: An Industrial Questionnaire Survey - Appendix A: Questionnaire Introduction. Decision-making in Practice / Appendix B: Survey results
2019 (English)Data set, Aggregated data
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-18692 (URN)
Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2020-01-23
Molléri, J. S., Nurdiani, I., Fotrousi, F. & Petersen, K. (2019). Experiences of studying attention through EEG in the context of review tasks. In: PROCEEDINGS OF EASE 2019 - EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING: . Paper presented at 23rd Evaluation and Assessment in Software Engineering Conference, EASE Copenhagen, 14 April 2019 through 17 April (pp. 313-318). Association for Computing Machinery
Open this publication in new window or tab >>Experiences of studying attention through EEG in the context of review tasks
2019 (English)In: PROCEEDINGS OF EASE 2019 - EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING, Association for Computing Machinery , 2019, p. 313-318Conference paper, Published paper (Refereed)
Abstract [en]

Context: Electroencephalograms (EEG) have been used in a few cases in the context of software engineering (SE). EEGs allow capturing emotions and cognitive functioning. Such human factors have already shown to be important to understand software engineering tasks. Therefore, it is essential to gain experience in the community to utilize EEG as a research tool. Objective: To report experiences of using EEG in the context of a software engineering education (review of master theses proposals). We provide our reflections and lessons learned of (1) how to plan an EEG study, (2) how to conduct and execute (e.g., tools), (3) how to analyze. Method: We carried out an experiment using an EEG headset to measure the participants’ attention rate. The experiment task includes reviewing three master thesis project plans. Results: We describe how we evolved our understanding of experimentation practices to collect and analyze psychological and cognitive data. We also provide a set of lessons learned regarding the application of EEG technology for research. Conclusions: We believe that that EEG could benefit software engineering research to collect cognitive information under certain conditions. The lessons learned reported here should be used as inputs for future experiments in software engineering, where human aspects are of interest. © 2019 Association for Computing Machinery.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2019
Keywords
Attention, Electroencephalogram, Experiment, Human subjects, Bioelectric phenomena, Electroencephalography, Engineering research, Experiments, Cognitive information, Electro-encephalogram (EEG), Human aspects, Project plans, Research tools, Software engineering
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-17890 (URN)10.1145/3319008.3319357 (DOI)000493383400033 ()2-s2.0-85064765914 (Scopus ID)9781450371452 (ISBN)
Conference
23rd Evaluation and Assessment in Software Engineering Conference, EASE Copenhagen, 14 April 2019 through 17 April
Available from: 2019-05-21 Created: 2019-05-21 Last updated: 2019-11-14Bibliographically approved
Ghazi, A. N., Petersen, K., Reddy, S. S. & Nekkanti, H. (2019). Survey Research in Software Engineering: Problems and Mitigation Strategies. IEEE Access, 7, 24703-24718
Open this publication in new window or tab >>Survey Research in Software Engineering: Problems and Mitigation Strategies
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 24703-24718Article in journal (Refereed) Published
Abstract [en]

Background: The need for empirical investigations in software engineering is growing. Many researchers nowadays, conduct and validate their solutions using empirical research. The Survey is an empirical method which enables researchers to collect data from a large population. The main aim of the survey is to generalize the findings.

Aims: In this study, we aim to identify the problems researchers face during survey design and mitigation strategies.

Method: A literature review, as well as semi-structured interviews with nine software engineering researchers, were conducted to elicit their views on problems and mitigation strategies. The researchers are all focused on empirical software engineering.

Results: We identified 24 problems and 65 strategies, structured according to the survey research process. The most commonly discussed problem was sampling, in particular, the ability to obtain a sufficiently large sample. To improve survey instrument design, evaluation and execution recommendations for question formulation and survey pre-testing were given. The importance of involving multiple researchers in the analysis of survey results was stressed.

Conclusions: The elicited problems and strategies may serve researchers during the design of their studies. However, it was observed that some strategies were conflicting. This shows that it is important to conduct a trade-off analysis between strategies.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Empirical software engineering, surveys
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-17250 (URN)10.1109/ACCESS.2018.2881041 (DOI)000460836000001 ()
Note

open access

Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2019-03-21Bibliographically approved
Nurdiani, I., Börstler, J., Fricker, S., Petersen, K. & Chatzipetrou, P. (2019). Understanding the order of agile practice introduction: Comparing agile maturity models and practitioners’ experience. Journal of Systems and Software, 156, 1-20
Open this publication in new window or tab >>Understanding the order of agile practice introduction: Comparing agile maturity models and practitioners’ experience
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2019 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 156, p. 1-20Article in journal (Refereed) Published
Abstract [en]

Context: Agile maturity models (AMMs) suggest that agile practices are introduced in a certain order. However, whether the order of agile practice introduction as suggested in the AMMs is relevant in industry has not been evaluated in an empirical study. Objectives: In this study, we want to investigate: (1) order of agile practice introduction mentioned in AMMs, (2) order of introducing agile practices in industry, and (3) similarities and differences between (1) and (2). Methods: We conducted a literature survey to identify strategies proposed by the AMMs. We then compared the AMMs’ suggestions to the strategies used by practitioners, which we elicited from a survey and a series of interviews from an earlier study. Results: The literature survey revealed 12 AMMs which provide explicit mappings of agile practices to maturity levels. These mappings showed little agreement on when practices should be introduced. Comparison of the AMMs’ suggestions and the empirical study revealed that the guidance suggested by AMMs are not aligned with industry practice. Conclusion: Currently, AMMs do not provide sufficient information to guide agile adoption in industry. Our results suggest that there might be no universal strategy for agile adoption that works better than others. © 2019 Elsevier Inc.

Place, publisher, year, edition, pages
Elsevier Inc., 2019
Keywords
Agile maturity model, Agile practice, Introduction strategies, Mapping, Agile adoptions, Agile practices, Empirical studies, Industry practices, Introduction strategy, Literature survey, Maturity levels, Maturity model, Surveys
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-18038 (URN)10.1016/j.jss.2019.05.035 (DOI)000483658000001 ()2-s2.0-85066489426 (Scopus ID)
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-10-09Bibliographically approved
Nurdiani, I., Börstler, J., Fricker, S. & Petersen, K. (2019). Usage, Retention, and Abandonment of Agile Practices. e-Informatica Software Engineering Journal, 13(1), 7-35
Open this publication in new window or tab >>Usage, Retention, and Abandonment of Agile Practices
2019 (English)In: e-Informatica Software Engineering Journal, ISSN 1897-7979, E-ISSN 2084-4840, Vol. 13, no 1, p. 7-35Article in journal (Refereed) Published
Abstract [en]

Background: A number of Agile maturity models (AMMs) have been proposed to guide software organizations in their adoption of Agile practices. Typically the AMMs suggest that higher maturity levels are reached by gradually adding more practices. However, recent research indicates that certain Agile practices, like test-driven development and continuous integration are being abandoned. Little is known on the rationales for abandoning Agile practices. Aim: We aim to identify which Agile practices are abandoned in industry, as well as the reasons for abandoning them. Method: We conducted a web survey with 51 respondents and interviews with 11 industry practitioners with experience in Agile adoption to investigate why Agile practices are abandoned. Results: Of the 17 Agile practices that were included in the survey, all have been abandoned at some point. Nevertheless, respondents who retained all practices as well as those who abandoned one or more practices, perceived their overall adoption of Agile practices as successful. Conclusion: Going against the suggestions of the AMMs, i.e. abandoning Agile one or more practices, could still lead to successful outcomes. This indicates that introducing Agile practices gradually in a certain sequence, as the AMMs suggest, may not always be suitable in different contexts.

Place, publisher, year, edition, pages
Software Engineering Section of the Committee on Informatics of the Polish Academy of Sciences and Wrocław University of Science and Technology., 2019
Keywords
Agile maturity models (AMMs), Agile practices
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-16236 (URN)10.5277/e-Inf190101 (DOI)000453279600001 ()
Available from: 2018-05-30 Created: 2018-05-30 Last updated: 2019-02-21Bibliographically approved
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