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[Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a more conventional literature review. Very often, SE researchers embark on an SLR with such doubts. We aspire to provide more understanding of when an SLR in SE should be conducted. [Objective] The first step of our investigation was focused on the dataset, i.e., the reviewed papers, in an SLR, which indicates the development of a research topic or area. The objective of this step is to provide a better understanding of the characteristics of the datasets of SLRs in SE. [Method] A research synthesis was conducted on a sample of 170 SLRs published in top-tier SE journals. We extracted and analysed the quantitative attributes of the datasets of these SLRs. [Results] The findings show that the median size of the datasets in our sample is 57 reviewed papers, and the median review period covered is 14 years. The number of reviewed papers and review period have a very weak and non-significant positive correlation. [Conclusions] The results of our study can be used by SE researchers as an indicator or benchmark to understand whether an SLR is conducted at a good time. © 2023 IEEE.

Today's software industry is global, virtual, and depending more than ever on strong and reliable processes. Stakeholders and infrastructure are distributed across the globe, posing challenges that go beyond those with co-located teams and servers. Software Engineering continues to be a complex undertaking, with projects challenged to meet expectations, especially regarding costs. We know that Software Engineering is an ever-changing discipline, with the result that firms and their employees must regularly embrace new methods, tools, technologies, and processes. In 2020, the International Conference on Global Software Engineering (ICGSE) and the International Conference on Systems and Software Processes (ICSSP) joined forces aiming to create a holistic understanding of the software landscape both from the perspective of human and infrastructure distribution and also the processes to support software development. Unfortunately, these challenges have become even more personal to many more in 2020 due to the disruption introduced by the COVID-19 pandemic, which forced both conferences to be held virtually. As an outcome of the joint event, we selected a set of the best papers from the two conferences, which were invited to submit extended versions to this Special Issue in the Journal of Software: Maintenance and Evolution. Dedicated committees were established to identify the best papers. Eight papers were invited and ultimately, seven of these invited papers have made it into this Special Issue. © 2023 John Wiley & Sons, Ltd.

Context: Peer review is a key activity intended to preserve the quality and integrity of scientific publications. However, in practice it is far from perfect. Objective: We aim at understanding how reviewers, including those who have won awards for reviewing, perform their reviews of software engineering papers to identify both what makes a good reviewing approach and what makes a good paper. Method: We first conducted a series of interviews with recognised reviewers in the software engineering field. Then, we used the results of those interviews to develop a questionnaire used in an online survey and sent out to reviewers from well-respected venues covering a number of software engineering disciplines, some of whom had won awards for their reviewing efforts. Results: We analyzed the responses from the interviews and from 175 reviewers who completed the online survey (including both reviewers who had won awards and those who had not). We report on several descriptive results, including: Nearly half of award-winners (45%) are reviewing 20+ conference papers a year, while 28% of non-award winners conduct that many. The majority of reviewers (88%) are taking more than two hours on journal reviews. We also report on qualitative results. Our findings suggest that the most important criteria of a good review is that it should be factual and helpful, which ranked above others such as being detailed or kind. The most important features of papers that result in positive reviews are a clear and supported validation, an interesting problem, and novelty. Conversely, negative reviews tend to result from papers that have a mismatch between the method and the claims and from papers with overly grandiose claims. Further insights include, if not limited to, that reviewers view data availability and its consistency as being important or that authors need to make their contribution of the work very clear in their paper. Conclusions: Based on the insights we gained through our study, w, open access

The European Project Space at PROFES 2019 provides an opportunity for researchers involved in ongoing and recently completed research projects (national, European, and international) related to the topics of the conference to present their projects and disseminate the objectives, deliverables, or outcome. © Springer Nature Switzerland AG 2019.

The microservice architecture enables organizations to shorten development cycles and deliver cloud-native applications rapidly. However, it also brings security concerns that need to be addressed by developers. Therefore, security testing in microservices becomes even more critical. Recent research papers indicate that security testing of microservices is often neglected for reasons such as lack of time, lack of experience in the security domain, and absence of automated test environments. Even though several security scanning tools exist to detect container, containerized workload management (Kubernetes), and network issues, none individually is sufficient to cover all security problems in microservices. Using multiple scanning tools increases the complexity of analyzing findings and mitigating security vulnerabilities. This paper presents a fully automated test tool suite that can help developers address security issues in microservices and resolve them. It targets to reduce time and effort in security activities by encapsulating open-source scanning tools into one suite and providing improved feedback. The developed security scanning suite is named Pomegranate. To develop Pomegranate, we employed Design Science and conducted our investigation in Ericsson. We have evaluated our tool using a static approach. The evaluation results indicate that the Pomegranate could be helpful to developers by providing simplified and classified outputs for security vulnerabilities in microservices. More than half of the practitioners who give us feedback found Pomegranate helpful in detecting and mitigating security problems in microservices. We conclude that a fully automated test tool suite can help developers to address most security issues in microservices. Based on the findings in this paper, the direction for future work is to conduct a dynamic validation of Pomegranate in a live project. © 2023 IEEE.

In the taxonomic trace links concept, the source and target artifacts are connected through knowledge organization structure (e.g., taxonomy). We introduce in this paper a recommender system that recommends labels to requirements artifacts from domain-specific taxonomy to establish taxonomic trace links. The tool exploits the hierarchical nature of taxonomies and uses requirements text and context information as input to the recommender. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

The microservice architecture enables organizations to shorten development cycles and deliver cloud-native applications rapidly. However, it also brings security concerns that need to be addressed by developers. Therefore, security testing in microservices becomes even more critical. Recent research papers indicate that security testing of microservices is often neglected for reasons such as lack of time, lack of experience in the security domain, and absence of automated test environments. Even though several security scanning tools exist to detect container, containerized workload management (Kubernetes), and network issues, none individually is sufficient to cover all security problems in microservices. Using multiple scanning tools increases the complexity of analyzing findings and mitigating security vulnerabilities. This paper presents a fully automated test tool suite that can help developers address security issues in microservices and resolve them. It targets to reduce time and effort in security activities by encapsulating open-source scanning tools into one suite and providing improved feedback. The developed security scanning suite is named Pomegranate. To develop Pomegranate, we employed Design Science and conducted our investigation in Ericsson. We have evaluated our tool using a static approach. The evaluation results indicate that the Pomegranate could be helpful to developers by providing simplified and classified outputs for security vulnerabilities in microservices. More than half of the practitioners who give us feedback found Pomegranate helpful in detecting and mitigating security problems in microservices. We conclude that a fully automated test tool suite can help developers to address most security issues in microservices. Based on the findings in this paper, the direction for future work is to conduct a dynamic validation of Pomegranate in a live project. © 2023 IEEE.

The microservice architecture enables organizations to shorten development cycles and deliver cloud-native applications rapidly. However, it also brings security concerns that need to be addressed by developers. Therefore, security testing in microservices becomes even more critical. Recent research papers indicate that security testing of microservices is often neglected for reasons such as lack of time, lack of experience in the security domain, and absence of automated test environments. Even though several security scanning tools exist to detect container, containerized workload management (Kubernetes), and network issues, none individually is sufficient to cover all security problems in microservices. Using multiple scanning tools increases the complexity of analyzing findings and mitigating security vulnerabilities. This paper presents a fully automated test tool suite that can help developers address security issues in microservices and resolve them. It targets to reduce time and effort in security activities by encapsulating open-source scanning tools into one suite and providing improved feedback. The developed security scanning suite is named Pomegranate. To develop Pomegranate, we employed Design Science and conducted our investigation in Ericsson. We have evaluated our tool using a static approach. The evaluation results indicate that the Pomegranate could be helpful to developers by providing simplified and classified outputs for security vulnerabilities in microservices. More than half of the practitioners who give us feedback found Pomegranate helpful in detecting and mitigating security problems in microservices. We conclude that a fully automated test tool suite can help developers to address most security issues in microservices. Based on the findings in this paper, the direction for future work is to conduct a dynamic validation of Pomegranate in a live project. © 2023 IEEE.

The microservice architecture enables organizations to shorten development cycles and deliver cloud-native applications rapidly. However, it also brings security concerns that need to be addressed by developers. Therefore, security testing in microservices becomes even more critical. Recent research papers indicate that security testing of microservices is often neglected for reasons such as lack of time, lack of experience in the security domain, and absence of automated test environments. Even though several security scanning tools exist to detect container, containerized workload management (Kubernetes), and network issues, none individually is sufficient to cover all security problems in microservices. Using multiple scanning tools increases the complexity of analyzing findings and mitigating security vulnerabilities. This paper presents a fully automated test tool suite that can help developers address security issues in microservices and resolve them. It targets to reduce time and effort in security activities by encapsulating open-source scanning tools into one suite and providing improved feedback. The developed security scanning suite is named Pomegranate. To develop Pomegranate, we employed Design Science and conducted our investigation in Ericsson. We have evaluated our tool using a static approach. The evaluation results indicate that the Pomegranate could be helpful to developers by providing simplified and classified outputs for security vulnerabilities in microservices. More than half of the practitioners who give us feedback found Pomegranate helpful in detecting and mitigating security problems in microservices. We conclude that a fully automated test tool suite can help developers to address most security issues in microservices. Based on the findings in this paper, the direction for future work is to conduct a dynamic validation of Pomegranate in a live project. © 2023 IEEE.

The microservice architecture enables organizations to shorten development cycles and deliver cloud-native applications rapidly. However, it also brings security concerns that need to be addressed by developers. Therefore, security testing in microservices becomes even more critical. Recent research papers indicate that security testing of microservices is often neglected for reasons such as lack of time, lack of experience in the security domain, and absence of automated test environments. Even though several security scanning tools exist to detect container, containerized workload management (Kubernetes), and network issues, none individually is sufficient to cover all security problems in microservices. Using multiple scanning tools increases the complexity of analyzing findings and mitigating security vulnerabilities. This paper presents a fully automated test tool suite that can help developers address security issues in microservices and resolve them. It targets to reduce time and effort in security activities by encapsulating open-source scanning tools into one suite and providing improved feedback. The developed security scanning suite is named Pomegranate. To develop Pomegranate, we employed Design Science and conducted our investigation in Ericsson. We have evaluated our tool using a static approach. The evaluation results indicate that the Pomegranate could be helpful to developers by providing simplified and classified outputs for security vulnerabilities in microservices. More than half of the practitioners who give us feedback found Pomegranate helpful in detecting and mitigating security problems in microservices. We conclude that a fully automated test tool suite can help developers to address most security issues in microservices. Based on the findings in this paper, the direction for future work is to conduct a dynamic validation of Pomegranate in a live project. © 2023 IEEE.

The microservice architecture enables organizations to shorten development cycles and deliver cloud-native applications rapidly. However, it also brings security concerns that need to be addressed by developers. Therefore, security testing in microservices becomes even more critical. Recent research papers indicate that security testing of microservices is often neglected for reasons such as lack of time, lack of experience in the security domain, and absence of automated test environments. Even though several security scanning tools exist to detect container, containerized workload management (Kubernetes), and network issues, none individually is sufficient to cover all security problems in microservices. Using multiple scanning tools increases the complexity of analyzing findings and mitigating security vulnerabilities. This paper presents a fully automated test tool suite that can help developers address security issues in microservices and resolve them. It targets to reduce time and effort in security activities by encapsulating open-source scanning tools into one suite and providing improved feedback. The developed security scanning suite is named Pomegranate. To develop Pomegranate, we employed Design Science and conducted our investigation in Ericsson. We have evaluated our tool using a static approach. The evaluation results indicate that the Pomegranate could be helpful to developers by providing simplified and classified outputs for security vulnerabilities in microservices. More than half of the practitioners who give us feedback found Pomegranate helpful in detecting and mitigating security problems in microservices. We conclude that a fully automated test tool suite can help developers to address most security issues in microservices. Based on the findings in this paper, the direction for future work is to conduct a dynamic validation of Pomegranate in a live project. © 2023 IEEE.

In the taxonomic trace links concept, the source and target artifacts are connected through knowledge organization structure (e.g., taxonomy). We introduce in this paper a recommender system that recommends labels to requirements artifacts from domain-specific taxonomy to establish taxonomic trace links. The tool exploits the hierarchical nature of taxonomies and uses requirements text and context information as input to the recommender. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

In the taxonomic trace links concept, the source and target artifacts are connected through knowledge organization structure (e.g., taxonomy). We introduce in this paper a recommender system that recommends labels to requirements artifacts from domain-specific taxonomy to establish taxonomic trace links. The tool exploits the hierarchical nature of taxonomies and uses requirements text and context information as input to the recommender. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

In the taxonomic trace links concept, the source and target artifacts are connected through knowledge organization structure (e.g., taxonomy). We introduce in this paper a recommender system that recommends labels to requirements artifacts from domain-specific taxonomy to establish taxonomic trace links. The tool exploits the hierarchical nature of taxonomies and uses requirements text and context information as input to the recommender. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

In the taxonomic trace links concept, the source and target artifacts are connected through knowledge organization structure (e.g., taxonomy). We introduce in this paper a recommender system that recommends labels to requirements artifacts from domain-specific taxonomy to establish taxonomic trace links. The tool exploits the hierarchical nature of taxonomies and uses requirements text and context information as input to the recommender. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

In the taxonomic trace links concept, the source and target artifacts are connected through knowledge organization structure (e.g., taxonomy). We introduce in this paper a recommender system that recommends labels to requirements artifacts from domain-specific taxonomy to establish taxonomic trace links. The tool exploits the hierarchical nature of taxonomies and uses requirements text and context information as input to the recommender. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

In recent years, more attention has been given to internal software startups in practice and in research alike, yet the concept is not fully understood. Nor is it clear whether or not it significantly differs from stand-alone software startup, and if yes, then how. In this position paper, we propose to conceptualize internal software startups as a hybrid of two related concepts: stand-alone software startup and internal corporate venture (ICV). We derive characteristics of the both concepts from the earlier literature and use our previous research on internal software startups to uncover the differences and the similarities across the three concepts. © 2022, The Author(s)., open access

Context: In software engineering, snowball sampling has been used as a supplementary and primary search strategy. The current guidelines recommend using Google Scholar (GS) for snowball sampling. However, the use of GS presents several challenges when using it as a source for citations and references. Objective: To compare the effectiveness and usefulness of two leading citation databases (GS and Scopus) for use in snowball sampling search. Method: We relied on a published study that has used snowball sampling as a search strategy and GS as the citation source. We used its primary studies to compute precision and recall for Scopus. Results: In this particular case, Scopus was highly effective with 95% recall and had better precision of 5.1% compared to GS’s 2.8%. Moreover, Scopus found nine additional relevant papers. On average, one would read approximately 15 extra papers in GS than Scopus to identify one additional relevant paper. Furthermore, Scopus supports batch downloading of both citations and papers’ references, has better quality metadata, and does better source filtering. Conclusion: This study suggests that Scopus seems to be more effective and useful for snowball sampling than GS for systematic secondary studies attempting to identify peer-reviewed literature. EVIE © 2022 The Authors., open access

Context: Writing code that is understandable by other collaborators has become crucial to enhancing collaboration and productivity. Clean Code has become one of the most relevant software craftsmanship practices and has been widely embraced as a synonym for code quality by software developers and software development organizations all over the world. However, very little is known regarding whether developers agree with Clean Code principles and how they apply them in practice. Objectives: In this work, we investigated how developers perceive Clean Code principles, whether they believe that helps reading, understanding, reusing, and modifying Clean Code, and how they keep their code clean. Methods: We conducted a Systematic Literature Review in which we screened 771 research papers to collect Clean Code principles and a survey among 39 practitioners, some of them with more than 20 years of development experience. Results: So far, the results show a shared agreement with Clean Code principles and their potential benefits. They also show that developers tend to write “messy” code to be refactored later. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG., Open access

In recent years, more attention has been given to internal software startups in practice and in research alike, yet the concept is not fully understood. Nor is it clear whether or not it significantly differs from stand-alone software startup, and if yes, then how. In this position paper, we propose to conceptualize internal software startups as a hybrid of two related concepts: stand-alone software startup and internal corporate venture (ICV). We derive characteristics of the both concepts from the earlier literature and use our previous research on internal software startups to uncover the differences and the similarities across the three concepts. © 2022, The Author(s)., open access

In recent years, more attention has been given to internal software startups in practice and in research alike, yet the concept is not fully understood. Nor is it clear whether or not it significantly differs from stand-alone software startup, and if yes, then how. In this position paper, we propose to conceptualize internal software startups as a hybrid of two related concepts: stand-alone software startup and internal corporate venture (ICV). We derive characteristics of the both concepts from the earlier literature and use our previous research on internal software startups to uncover the differences and the similarities across the three concepts. © 2022, The Author(s)., open access

In recent years, more attention has been given to internal software startups in practice and in research alike, yet the concept is not fully understood. Nor is it clear whether or not it significantly differs from stand-alone software startup, and if yes, then how. In this position paper, we propose to conceptualize internal software startups as a hybrid of two related concepts: stand-alone software startup and internal corporate venture (ICV). We derive characteristics of the both concepts from the earlier literature and use our previous research on internal software startups to uncover the differences and the similarities across the three concepts. © 2022, The Author(s)., open access

In recent years, more attention has been given to internal software startups in practice and in research alike, yet the concept is not fully understood. Nor is it clear whether or not it significantly differs from stand-alone software startup, and if yes, then how. In this position paper, we propose to conceptualize internal software startups as a hybrid of two related concepts: stand-alone software startup and internal corporate venture (ICV). We derive characteristics of the both concepts from the earlier literature and use our previous research on internal software startups to uncover the differences and the similarities across the three concepts. © 2022, The Author(s)., open access

In recent years, more attention has been given to internal software startups in practice and in research alike, yet the concept is not fully understood. Nor is it clear whether or not it significantly differs from stand-alone software startup, and if yes, then how. In this position paper, we propose to conceptualize internal software startups as a hybrid of two related concepts: stand-alone software startup and internal corporate venture (ICV). We derive characteristics of the both concepts from the earlier literature and use our previous research on internal software startups to uncover the differences and the similarities across the three concepts. © 2022, The Author(s)., open access

Context: In software engineering, snowball sampling has been used as a supplementary and primary search strategy. The current guidelines recommend using Google Scholar (GS) for snowball sampling. However, the use of GS presents several challenges when using it as a source for citations and references. Objective: To compare the effectiveness and usefulness of two leading citation databases (GS and Scopus) for use in snowball sampling search. Method: We relied on a published study that has used snowball sampling as a search strategy and GS as the citation source. We used its primary studies to compute precision and recall for Scopus. Results: In this particular case, Scopus was highly effective with 95% recall and had better precision of 5.1% compared to GS’s 2.8%. Moreover, Scopus found nine additional relevant papers. On average, one would read approximately 15 extra papers in GS than Scopus to identify one additional relevant paper. Furthermore, Scopus supports batch downloading of both citations and papers’ references, has better quality metadata, and does better source filtering. Conclusion: This study suggests that Scopus seems to be more effective and useful for snowball sampling than GS for systematic secondary studies attempting to identify peer-reviewed literature. EVIE © 2022 The Authors., open access

Context: In software engineering, snowball sampling has been used as a supplementary and primary search strategy. The current guidelines recommend using Google Scholar (GS) for snowball sampling. However, the use of GS presents several challenges when using it as a source for citations and references. Objective: To compare the effectiveness and usefulness of two leading citation databases (GS and Scopus) for use in snowball sampling search. Method: We relied on a published study that has used snowball sampling as a search strategy and GS as the citation source. We used its primary studies to compute precision and recall for Scopus. Results: In this particular case, Scopus was highly effective with 95% recall and had better precision of 5.1% compared to GS’s 2.8%. Moreover, Scopus found nine additional relevant papers. On average, one would read approximately 15 extra papers in GS than Scopus to identify one additional relevant paper. Furthermore, Scopus supports batch downloading of both citations and papers’ references, has better quality metadata, and does better source filtering. Conclusion: This study suggests that Scopus seems to be more effective and useful for snowball sampling than GS for systematic secondary studies attempting to identify peer-reviewed literature. EVIE © 2022 The Authors., open access

Context: In software engineering, snowball sampling has been used as a supplementary and primary search strategy. The current guidelines recommend using Google Scholar (GS) for snowball sampling. However, the use of GS presents several challenges when using it as a source for citations and references. Objective: To compare the effectiveness and usefulness of two leading citation databases (GS and Scopus) for use in snowball sampling search. Method: We relied on a published study that has used snowball sampling as a search strategy and GS as the citation source. We used its primary studies to compute precision and recall for Scopus. Results: In this particular case, Scopus was highly effective with 95% recall and had better precision of 5.1% compared to GS’s 2.8%. Moreover, Scopus found nine additional relevant papers. On average, one would read approximately 15 extra papers in GS than Scopus to identify one additional relevant paper. Furthermore, Scopus supports batch downloading of both citations and papers’ references, has better quality metadata, and does better source filtering. Conclusion: This study suggests that Scopus seems to be more effective and useful for snowball sampling than GS for systematic secondary studies attempting to identify peer-reviewed literature. EVIE © 2022 The Authors., open access

Context: In software engineering, snowball sampling has been used as a supplementary and primary search strategy. The current guidelines recommend using Google Scholar (GS) for snowball sampling. However, the use of GS presents several challenges when using it as a source for citations and references. Objective: To compare the effectiveness and usefulness of two leading citation databases (GS and Scopus) for use in snowball sampling search. Method: We relied on a published study that has used snowball sampling as a search strategy and GS as the citation source. We used its primary studies to compute precision and recall for Scopus. Results: In this particular case, Scopus was highly effective with 95% recall and had better precision of 5.1% compared to GS’s 2.8%. Moreover, Scopus found nine additional relevant papers. On average, one would read approximately 15 extra papers in GS than Scopus to identify one additional relevant paper. Furthermore, Scopus supports batch downloading of both citations and papers’ references, has better quality metadata, and does better source filtering. Conclusion: This study suggests that Scopus seems to be more effective and useful for snowball sampling than GS for systematic secondary studies attempting to identify peer-reviewed literature. EVIE © 2022 The Authors., open access

Context: In software engineering, snowball sampling has been used as a supplementary and primary search strategy. The current guidelines recommend using Google Scholar (GS) for snowball sampling. However, the use of GS presents several challenges when using it as a source for citations and references. Objective: To compare the effectiveness and usefulness of two leading citation databases (GS and Scopus) for use in snowball sampling search. Method: We relied on a published study that has used snowball sampling as a search strategy and GS as the citation source. We used its primary studies to compute precision and recall for Scopus. Results: In this particular case, Scopus was highly effective with 95% recall and had better precision of 5.1% compared to GS’s 2.8%. Moreover, Scopus found nine additional relevant papers. On average, one would read approximately 15 extra papers in GS than Scopus to identify one additional relevant paper. Furthermore, Scopus supports batch downloading of both citations and papers’ references, has better quality metadata, and does better source filtering. Conclusion: This study suggests that Scopus seems to be more effective and useful for snowball sampling than GS for systematic secondary studies attempting to identify peer-reviewed literature. EVIE © 2022 The Authors., open access

Context: The relevance of Requirements Engineering (RE) research to practitioners is vital for a long-term dissemination of research results to everyday practice. Some authors have speculated about a mismatch between research and practice in the RE discipline. However, there is not much evidence to support or refute this perception. Objective: This paper presents the results of a study aimed at gathering evidence from practitioners about their perception of the relevance of RE research and at understanding the factors that influence that perception. Method: We conducted a questionnaire-based survey of industry practitioners with expertise in RE. The participants rated the perceived relevance of 435 scientific papers presented at five top RE-related conferences. Results: The 153 participants provided a total of 2,164 ratings. The practitioners rated RE research as essential or worthwhile in a majority of cases. However, the percentage of non-positive ratings is still higher than we would like. Among the factors that affect the perception of relevance are the paper?s links to industry, the research method used, and respondents? roles. The reasons for positive perceptions were primarily related to the relevance of the problem and the soundness of the solution, while the causes for negative perceptions were more varied. The respondents also provided suggestions for future research, including topics researchers have studied for decades, like elicitation or requirement quality criteria. Conclusions: The study is valuable for both researchers and practitioners. Researchers can use the reasons respondents gave for positive and negative perceptions and the suggested research topics to help make their research more appealing to practitioners and thus more prone to industry adoption. Practitioners can benefit from the overall view of contemporary RE research by learning about research topics that they may not be familiar with, and compare their perception with those of their c, open access

Accurate and reliable effort and cost estimation are still challenging for agile teams in the industry. It is argued that leveraging historical data regarding the actual time spent on similar past projects could be very helpful to support such an activity before companies embark upon a new project. In this paper, we investigate to what extent user story information retrieved from past projects can help developers estimate the effort needed to develop new similar projects. In close collaboration with a software development company, we applied design science and action research principles to develop and evaluate a tool that employs Natural Language Processing (NLP) algorithms to find past similar user stories and retrieve the actual time spent on them. The tool was then used to estimate a real project that was about to start in the company. A focus group with a team of six developers was conducted to evaluate the tool’s efficacy in estimating similar projects. The results of the focus group with the developers revealed that the tool has the potential to complement the existing estimation process and help different interested parties in the company. Our results contribute both towards a new tool-supported approach to help user story estimation based on historical data and with our lessons learned on why, when, and where such a tool and the estimations provided may play a role in agile projects in the industry.

Deriving acceptance tests from high-level, natural language requirements that achieve full coverage is a major manual challenge at the interface between requirements engineering and testing. Conditional requirements (e.g., 'If A or B then C.') imply causal relationships which - when extracted - allow to generate these acceptance tests automatically. This paper presents a tool from the CiRA (Causality In Requirements Artifacts) initiative, which automatically processes conditional natural language requirements and generates a minimal set of test case descriptions achieving full coverage. We evaluate the tool on a publicly available data set of 61 requirements from the requirements specification of the German Corona-Warn-App. The tool infers the correct test variables in 84.5% and correct variable configurations in 92.3% of all cases, which corroborates the feasibility of our approach. © 2023 IEEE.

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.

Software Architecture Courses in Software Engineering curricula often involve project-based group assignments where students have to design and document software architectures and evaluate other architectures. This paper presents the design and implementation of the Architectural Kata workshop conducted in the Software Architecture course as a group exercise to teach designing, documenting, and evaluating software architecture. Feedback on the workshop was collected from students using a survey questionnaire after the conclusion of the course. The results showed that besides acquiring skills to design and evaluate architectural designs, the workshop also supported skills to identify non-functional requirements, elaborate assumptions, and collaborate with their peers. Observations and lessons learned from the workshop about students' feedback, planning, and workshop moderation are shared. Possible future improvements are also suggested. © 2023 Owner/Author.

Onboarding is a process of organizational socialization of the new hires, that includes recruitment, orientation, training, coaching and support. While onboarding individuals into an organization is a rather straightforward task, little is known about 1) onboarding hundreds of developers and 2) doing it on a distance in outsourcing situations. Furthermore, the subject of sustainable growth with respect to organizational capabilities and culture is often overlooked. This paper reports findings from an exploratory multi-case study of two large onboarding campaigns. We collected empirical data from interviews, retrospectives, onboarding documentation and onsite visits. Based on the empirical study, onboarding hundreds of software engineers in a complex agile product development environment which lacks documentation and puts high demands on engineers’ knowledge and skills is a challenging and costly endeavor. To save the costs and for practical reasons, large-scale onboarding is organized in batches with the first batch trained onsite, and the later batches trained internally. We report challenges faced in the two cases and discuss possible solutions. One core finding is that a good plan combined with the organizational agility, i.e., the responsiveness to change, together with organizational maturity determined the success of organizational scaling. The presented cases contribute to the scarce research on knowledge transfer and onboarding in a large-scale agile context. © 2023, The Author(s).

Software is a critical component of nearly every product or service. Improvements in software can lead to substantial competitive advantages. At the same time, software and surrounding engineering teams have become increasingly complex. The adoption of continuous integration and delivery is a recent trend to radically improve software release speed. However, its adoption is far from straightforward. Specifically, rethinking processes, organizational culture, ways of working, and business models require buy-in from diverse stakeholders that may have conflicting objectives. Such situations are explored by organizational conflict research. This paper reports on early lessons from an ongoing research project in continuous software engineering, specifically investigating adoption challenges from an organizational conflict perspective. We identify catalysts, symptoms, and outcomes of organizational conflicts hindering the adoption process. We conclude that predictable conflicts emerge when adopting continuous engineering. Engineers, managers, and other teams can proactively prepare for and allocate resources to resolve them. Proper analysis and management can help avoid wasted time, impeding processes, and frustration. © 2023, The Author(s).

Requirements quality literature abounds with publications presenting artifacts, such as data sets and tools. However, recent systematic studies show that more than 80% of these artifacts have become unavailable or were never made public, limiting reproducibility and reusability. In this work, we report on an attempt to recover those artifacts. To that end, we requested corresponding authors of unavailable artifacts to recover and disclose them according to open science principles. Our results, based on 19 answers from 35 authors (54% response rate), include an assessment of the availability of requirements quality artifacts and a breakdown of authors’ reasons for their continued unavailability. Overall, we improved the availability of seven data sets and seven implementations. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Companies struggle to continuously develop and deploy Artificial Intelligence (AI) models to complex production systems due to AI characteristics while assuring quality. To ease the development process, continuous pipelines for AI have become an active research area where consolidated and in-depth analysis regarding the terminology, triggers, tasks, and challenges is required.This paper includes a Multivocal Literature Review (MLR) where we consolidated 151 relevant formal and informal sources. In addition, nine-semi structured interviews with participants from academia and industry verified and extended the obtained information. Based on these sources, this paper provides and compares terminologies for Development and Operations (DevOps) and Continuous Integration (CI)/Continuous Delivery (CD) for AI, Machine Learning Operations (MLOps), (end-to-end) lifecycle management, and Continuous Delivery for Machine Learning (CD4ML). Furthermore, the paper provides an aggregated list of potential triggers for reiterating the pipeline, such as alert systems or schedules. In addition, this work uses a taxonomy creation strategy to present a consolidated pipeline comprising tasks regarding the continuous development of AI. This pipeline consists of four stages: Data Handling, Model Learning, Software Development and System Operations. Moreover, we map challenges regarding pipeline implementation, adaption, and usage for the continuous development of AI to these four stages.(c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.

Software Architecture Courses in Software Engineering curricula often involve project-based group assignments where students have to design and document software architectures and evaluate other architectures. This paper presents the design and implementation of the Architectural Kata workshop conducted in the Software Architecture course as a group exercise to teach designing, documenting, and evaluating software architecture. Feedback on the workshop was collected from students using a survey questionnaire after the conclusion of the course. The results showed that besides acquiring skills to design and evaluate architectural designs, the workshop also supported skills to identify non-functional requirements, elaborate assumptions, and collaborate with their peers. Observations and lessons learned from the workshop about students' feedback, planning, and workshop moderation are shared. Possible future improvements are also suggested. © 2023 Owner/Author.

Software Architecture Courses in Software Engineering curricula often involve project-based group assignments where students have to design and document software architectures and evaluate other architectures. This paper presents the design and implementation of the Architectural Kata workshop conducted in the Software Architecture course as a group exercise to teach designing, documenting, and evaluating software architecture. Feedback on the workshop was collected from students using a survey questionnaire after the conclusion of the course. The results showed that besides acquiring skills to design and evaluate architectural designs, the workshop also supported skills to identify non-functional requirements, elaborate assumptions, and collaborate with their peers. Observations and lessons learned from the workshop about students' feedback, planning, and workshop moderation are shared. Possible future improvements are also suggested. © 2023 Owner/Author.

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.

Software Architecture Courses in Software Engineering curricula often involve project-based group assignments where students have to design and document software architectures and evaluate other architectures. This paper presents the design and implementation of the Architectural Kata workshop conducted in the Software Architecture course as a group exercise to teach designing, documenting, and evaluating software architecture. Feedback on the workshop was collected from students using a survey questionnaire after the conclusion of the course. The results showed that besides acquiring skills to design and evaluate architectural designs, the workshop also supported skills to identify non-functional requirements, elaborate assumptions, and collaborate with their peers. Observations and lessons learned from the workshop about students' feedback, planning, and workshop moderation are shared. Possible future improvements are also suggested. © 2023 Owner/Author.

Fragile (i.e., non-robust) test execution is a common challenge for automated GUI-based testing of web applications as they evolve. Despite recent progress, there is still room for improvement since test execution failures caused by technical limitations result in unnecessary maintenance costs that limit its effectiveness and efficiency. One of the most reported technical challenges for web-based tests concerns how to reliably locate a web element used by a test script.This paper proposes the novel concept of Visually Overlapping Nodes (VON) that reduces fragility by utilizing the phenomenon that visual web elements (observed by the user) are constructed from multiple web-elements in the Document Object Model (DOM) that overlaps visually.We demonstrate the approach in a tool, VON Similo, which extends the state-of-the-art multi-locator approach (Similo) that is also used as the baseline for an experiment. In the experiment, a ground truth set of 1163 manually collected web element pairs, from different releases of the 40 most popular web applications on the internet, are used to compare the approaches' precision, recall, and accuracy.Our results show that VON Similo provides 94.7% accuracy in identifying a web element in a new release of the same SUT. In comparison, Similo provides 83.8% accuracy.These results demonstrate the applicability of the visually overlapping nodes concept/tool for web element localization in evolving web applications and contribute a novel way of thinking about web element localization in future research on GUI-based testing. © 2023 IEEE.

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.

Software Architecture Courses in Software Engineering curricula often involve project-based group assignments where students have to design and document software architectures and evaluate other architectures. This paper presents the design and implementation of the Architectural Kata workshop conducted in the Software Architecture course as a group exercise to teach designing, documenting, and evaluating software architecture. Feedback on the workshop was collected from students using a survey questionnaire after the conclusion of the course. The results showed that besides acquiring skills to design and evaluate architectural designs, the workshop also supported skills to identify non-functional requirements, elaborate assumptions, and collaborate with their peers. Observations and lessons learned from the workshop about students' feedback, planning, and workshop moderation are shared. Possible future improvements are also suggested. © 2023 Owner/Author.

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.