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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. [ABSTRACT FROM AUTHOR]

Crowdsourcing is simply the outsourcing of different tasks or work to a diverse group of individuals in an open call for the purpose of utilizing human intelligence. Crowdsourcing nowadays used to support and enhance software engineering in different aspects. In this proposed study, a systematic literature review was conducted for the last 10 years from 2010 to 2019. During the filtering process, a total of 120 relevant studies have been identified, and then the most relevant 70 studies were selected to include as part of the current study. The proposed study shows the effect of task assignment in crowdsourcing, such as if a task is assigned to an appropriate worker or an inappropriate worker, what will be the consequences. The study also highlights crowdsourcing usage in the field of software engineering. All the existing task assignment methods used for assigning the task to make crowdsourcing activity more effective have been analyzed. The study also highlights all the available crowdsourcing platforms used for a variety of task to be performed. The study concludes by identifying the issues regarding the task assignment and to specify the methods for enhancement in the assignment of tasks for future research in crowdsourcing. [ABSTRACT FROM AUTHOR]

Global software development (GSD) refers to developing software with a distributed team spanning multiple locations and time zones. Based on relationships, there are four types of outsourcing: dyadic (one client–one vendor), multi‐vendor (one client–many vendors), co‐sourcing (many clients–one vendor), and complex outsourcing (many clients–many vendors). Compared to the other types of outsourcing contracts, complex outsourcing contracts are the hardest to work on and have the highest risk of project failure. This paper presents a model, the complex outsourcing relationships management model (CORMM), to assist the complex outsourcing stakeholders (both the clients and vendors) in managing their relationships in the context of GSD. This paper aims to develop a CORMM to assist the complex outsourcing relationships management stakeholders in GSD. Also, we are interested in identifying the applicability and effectiveness of the CORMM in the real‐world industry. The research approach follows a structured methodology comprising multiple phases. Initially, it leverages a systematic literature review (SLR) as its primary research method. The second phase involves the validation of the SLR findings via an empirical study. Subsequently, in the third phase, a model is developed. Finally, the proposed research approach is validated by incorporating two industrial case studies to assess the organization's relationship management utilizing the Motorola tool. The case study results show that CORMM can successfully point out relationship management issues in a complex outsourcing context. The feedback received from the participants of both companies indicates several positive and valuable insights about the CORMM and its application in the context of complex outsourcing relationships. The results highlight that CORMM serves as an assessment tool for evaluating an organization's relationship management capability and a means for organizations to enhance their position. Through CORMM, complex outsourcing organizations (many clients–many vendors) can identify strengths and weaknesses in their relationship management practices, enabling targeted improvement efforts. [ABSTRACT FROM AUTHOR]

Crowdsource software development has become more and more popular in recent years in the software industry. Crowdsourcing is an open‐call technique for outsourcing tasks to a broad and undefined crowd. Crowdsourcing provides numerous advantages including reduced costs, fast project completion, talent identification, diversity of solutions, top‐quality, and access to problem‐solving creativity. Despite of the benefits gained from crowdsourcing, there are numerous issues like lack of experienced workers, lack of confidentiality, copyright issues, software sustainability, and so forth. There is also less focus on the long‐term sustainability of software development because of new ideas emerging in crowdsourcing software development. Furthermore, in literature, lack of guidelines towards sustainable software crowdsourcing is highlighted as one of the limitations in the software standards. This study aims to identify the factors that influence sustainability aspects in crowdsourced software development. We have conducted a systematic literature review for identification of these factors. In this paper, we present findings of the systematic literature review in the form of a list of 11 factors extracted from a sample of 45 finally selected papers. Among these factors, six of the factors are ranked as critical factors. These critical factors are "Lack of coding standard in documentation," "Use of popular programming tools," "Crowd Lack of knowledge and awareness about sustainability," "Energy‐efficient coding," "Lack of awareness about sustainable software engineering practices," and "Lack of coordination/communication between client and crowd." [ABSTRACT FROM AUTHOR]

The objective of this study is to analyze the current perspective as regards knowledge related to what causes stress or motivates developers, how these two aspects are related to each other, and how this in turn affects their performance in the sphere of Global Software Development and how these can be controlled. This paper presents the results obtained after conducting a systematic mapping study of literature in order to analyze how stress, motivation, and performance affect the project members in Global Software Development teams. We carried out a systematic mapping of published studies dealing with stress, motivation, and performance in global software engineering. A total of 118 papers dealing with this subject were found. The literature analyzed provided a relatively significant quantity of data referring to the impact that the characteristics of distributed software development projects have on the performance and productivity of teams, along with the actions taken to improve that performance. However, when focusing on the analysis of the impact of this type of projects on team members' motivation, and on the actions that can be taken to improve that motivation, we discovered that the number of works decreases considerably and that works referring to the impact of this kind of development on developers' stress were virtually non‐existent, as were those concerning ways in which to improve that stress. We are, therefore, of the opinion that it is necessary to carry out in‐depth research into the aspects of working in distributed teams that may have a negative impact on developers' levels of motivation and stress, along with what could be beneficial in order to improve levels of motivation and decrease levels of stress. [ABSTRACT FROM AUTHOR]

Context: Interest in global software development (GSD) has led to the publication of numerous studies. Over time, these studies should be updated to verify if their findings and conclusions remain valid. Objective: To update a tertiary study, published in 2014, focused on investigating risks and risk mitigation advice in the context of GSD. Method: We conducted a systematic literature study based on forward snowballing, out of which we identified and selected 25 unique studies. Results: We extracted: (a) 118 risks (75 of which already identified in the original work), and (b) 167 mitigation advice (44 of which were identified in the original work). Out of 43 new risks identified, just 10 are specific to GSD settings; the others are risks of the software process described in the context of GSD. The evidence supporting these risks and mitigation were extracted from 619 and 389 primary sources, respectively. Given the amount of evidence found, we have reported only those with higher empirical support. The raw data is available online as complementary material. Conclusions: Interest in GSD, its potential risks, and mitigation strategies, remains high. This update has allowed increasing the level of empirical support provided by the findings of the original work. [ABSTRACT FROM AUTHOR]

Quality of research and knowledge sources can be a critical factor to judge the quality of the research citing them. The prevailing online dissemination of research via blogs, websites, experience reports, and white papers can be helpful as it minimizes potential publication biases. In this paper, we have (i) identified the body of software engineering (SE) literature that is non‐indexed by Web‐of‐Science but has been used in published SE systematic literature review (SLR) studies (as primary studies); (ii) proposed a checklist‐based method for quality assessment of non‐indexed and grey literature; (iii) empirically evaluated the effectiveness of the proposed method; (iv) searched primary studies retrieved from the SLRs in DBLP and Google Scholar (GS) to verify whether DBLP or Google Scholar can act as viable options to search for non‐indexed Web‐of‐Science (non‐WOS‐indexed) literature. Besides proposing our synthesized checklist method, we found that (i) SE literature reviews use significantly non‐indexed Web‐of‐Science sources; (ii) DBLP only covers a portion of the non‐WOS‐indexed reference, whereas GS has a more complete coverage of the non‐indexed reference in comparison. Preliminary evaluation of the proposed quality assessment point system‐based and checklist‐based methods shows it to be a viable way to assess quality of the non‐indexed and grey literature. [ABSTRACT FROM AUTHOR]

However, if on the one side it is now generally accepted that disciplined procedures and quality standards should be applied in production code development, on the other side, comparable levels of rigor and quality are not demanded for the code written for testing that production code. Recently, both researchers and practitioners have proposed solutions toward this problem by identifying test code smells or test code quality issues and providing techniques to automatically detect and repair test code bugs and flakiness. We are pleased to present the papers selected for inclusion in the special issue devoted to the 1st International Conference on Automation of Software Test, which was held virtually in colocation with the 42nd IEEE/ACM International Conference on Software Engineering (ICSE 2020). [Extracted from the article]

Introduction: Technology transfer aims at supporting the transfer of results from software engineering research from academia to industrial application. Objective: This paper reports on the current state of technology transfer in software engineering. Method: We conducted a systematic literature review, in which we investigated 3070 papers. We identified in total 70 relevant papers, which were subject of a detailed analysis. Results: Many different approaches are proposed to foster technology transfer in software engineering. The majority of these approaches suggest direct collaboration between industry and academia or teaching new technologies in industrial training or university education. In addition, a considerable number of experience reports on technology transfer exist. Hence, a multitude of best practices, success stories, and lessons learned is reported. Among others, empirical evidence, maturity, and adaptability of the technology seem important preconditions for successful transfer, while social and organizational factors seem important barriers to successful technology transfer. Conclusion: Our findings can aid software engineering researchers in determining how best to support the transfer of their research results into practice. Furthermore, analysis of the literature also revealed that no reports exist on the combination of various technology transfer approaches, which could increase advantages of existing approaches while reducing their disadvantages. Technology transfer aims at supporting the transfer of results from software engineering research from academia to industrial application. Results from a systematic literature review of technology transfer research in software engineering show a variety of suggested transfer approaches and point to success factors and barriers for technology transfer in software engineering. Our findings aid software engineering researcher in determining how best to support the transfer of their research results into practice. [ABSTRACT FROM AUTHOR]

This paper proposes an interdisciplinary approach aimed to bring into practice some novel Software Engineering theoretical paradigms. The premise of these news paradigms is based on the idea of "ecologies" as realities that take place in the environment of a Software product. These "ecologies" are understood as tight but flexible interactions between the human, social, and technical components involved, where Software is just another component. However, the theoretical paradigms discussed do not offer a viable way to make sense of these ecologies into the practice of Software design and development. Thus, as a more operationalized approach, this paper proposes a framework based on Networks of Activities, a concept derived from Activity Theory, as a resource to help in the design, development, and evolution of these new emergent complex socio‐technical ecologies. This framework has been conceived as a tool for the design, capture, and, especially, to analyze the evolution of these ecologies. The concepts and notations used by the proposed approach are illustrated through a proof of concept that shows the essential ideas and their use in real scenarios. In this way, the concepts discussed contribute to the improvement of related fields like Requirements Engineering, Human‐Computer Interaction, or Software Architecture, among others. [ABSTRACT FROM AUTHOR]

Summary: In software development, developer turnover is among the primary reasons for project failures, leading to a great void of knowledge and strain for newcomers. Unfortunately, no established methods exist to measure how the problem domain knowledge is distributed among developers. Awareness of how this knowledge evolves and is owned by key developers in a project helps stakeholders reduce risks caused by turnover. To this end, this paper introduces a novel, realistic representation of problem domain knowledge distribution: the ConceptRealm. To construct the ConceptRealm, we employ a latent Dirichlet allocation model to represent textual features obtained from 300 K issues and 1.3 M comments from 518 open‐source projects. We analyze whether the newly emerged issues and developers share similar concepts or how aligned the individual developers' concepts are with the team over time. We also investigate the impact of leaving developers on the frequency of concepts. Finally, we also evaluate the soundness of our approach on a closed‐source software project, thus allowing the validation of the results from a practical standpoint. We find out that the ConceptRealm can represent the problem domain knowledge within a project and can be utilized to predict the alignment of developers with issues. We also observe that projects exhibit many keepers independent of project maturity and that abruptly leaving keepers correlates with a decline of their core concepts as the remaining developers cannot quickly familiarize themselves with those concepts. [ABSTRACT FROM AUTHOR]

We are happy to introduce you to this special issue that presents selected papers from the 15th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2015). SCAM is a leading conference that brings together researchers and practitioners working on theory, techniques, and applications that concern analysis and/or manipulation of the source code of computer systems. While much attention in the wider software engineering community is properly directed towards other aspects of systems development and evolution, such as specification, design, and requirements engineering, it is the source code that contains the only precise description of the behavior of the system. The analysis and manipulation of source code thus remains a pressing concern. SCAM 2015 was held on September 27 to 28, 2015, in Bremen, Germany, together with 31st International Conference on Software Maintenance and Evolution (ICSME). [ABSTRACT FROM AUTHOR]

Global software development (GSD) has become a rising software development model in the last few years. Although much research has been performed in terms of GSD management, GSD governance research is scarce at the present time and presents multiple challenges that need to be addressed. In this paper, a systematic mapping study has been conducted, the aim of which was to discover the main issues dealt with in GSD governance literature, as well as to point out the particular research gaps that are still present in this domain. This allowed us to find out new challenges to be addressed, along with possible solutions to these. The results reveal a lack of research in key aspects such as tools, culture, people, and information, all of which provides a great opportunity for future approaches while also highlighting new opportunities in GSD governance research. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports within the issue on topics including software process, software development and business software.

Blockchain is a cross‐cutting technology allowing interactions among untrusted entities in a distributed manner without the need for involving a trusted third party. Smart contracts (i.e., programs running on the blockchain) enabled organizations to envision and implement solutions to real‐world problems in less cost and time. Given the immutability of blockchain and the lack of best practices for properly designing and developing smart contracts, it is crucial to assure smart contract quality before deployment. With the help of an exploratory survey involving developers and researchers, this paper identifies the practices and tools used to develop, implement, and evaluate smart contracts. The survey received 55 valid responses. Such responses indicate that (i) inefficiencies may occur during the development cycle of a smart contract, especially regarding requirements specification, design, and testing phases, and (ii) the lack of a shared standard to evaluate the functional quality of implemented smart contracts. To start coping with these issues, the adoption of functional suitability assessment measures recommended by the ISO/IEC 25000 standard, widely used in software engineering, is proposed by adapting them to the context of smart contracts. Through some examples, the manuscript also illustrates how to measure the functional completeness and correctness of smart contracts. The proposed procedure to measure smart contract functional suitability brings advantages to both developers and users of decentralized finance or non‐fungible tokens platforms, data marketplaces, or shipping and real estate services, just to mention a few. In particular, it helps (i) better outline the responsibilities of smart contracts, (ii) uncover errors and deficiencies of smart contracts in the early stages, and (iii) ensure that the established requirements are met. [ABSTRACT FROM AUTHOR]

The minimizing Delta Debugging (DDMIN) was among the first algorithms designed to automate the task of reducing test cases. Its popularity is based on the characteristics that it works on any kind of input, without knowledge about the input structure. Several studies proved that smaller outputs can be produced faster with more advanced techniques (e.g., building a tree representation of the input and reducing that data structure); however, if the structure is unknown or changing frequently, maintaining the descriptors might not be resource‐efficient. Therefore, in this paper, we focus on the evaluation of the novel fixed‐point iteration of minimizing Delta Debugging (DDMIN*) on publicly available test suites related to software engineering. Our experiments show that DDMIN* can help reduce inputs further by 48.08% on average compared to DDMIN (using lines as the units of the reduction). Although the effectiveness of the algorithm improved, it comes with the cost of additional testing steps. This study shows how the characteristics of the input affect the results and when it pays off using DDMIN*. [ABSTRACT FROM AUTHOR]

Modern systems are increasingly connected and more integrated with other existing systems, giving rise to systems‐of‐systems (SoS). An SoS consists of a set of independent, heterogeneous systems that interact to provide new functionalities and accomplish global missions through emergent behavior manifested at runtime. The distinctive characteristics of SoS, when contrasted to traditional systems, pose significant research challenges within software engineering. These challenges motivate the need for a paradigm shift and the exploration of novel approaches for designing, developing, deploying, and evolving these systems. The International Workshop on Software Engineering for Systems‐of‐Systems (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the software engineering perspective, becoming the first venue for this purpose. This article presents a study aimed at outlining the evolution and future trajectory of software engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013–2023). The study combined scoping review and scientometric analysis methods to categorize and analyze the research contributions concerning temporal and geographic distribution, topics of interest, research methodologies employed, application domains, and research impact. Based on such a comprehensive overview, this article discusses current and future directions in software engineering for SoS. [ABSTRACT FROM AUTHOR]

Researchers are increasingly recognizing the importance of human aspects in software development. Because qualitative methods are used to explore human behavior in‐depth, we believe that studies using such methods will become more common. Existing qualitative software engineering guidelines do not cover the full breadth of qualitative methods and the knowledge on how to use them like in social sciences. The purpose of this study was to extend the software engineering community's current body of knowledge regarding available qualitative methods and their quality assurance frameworks and to provide recommendations and guidelines for their use. With the support of an epistemological argument and a survey of the literature, we suggest that future research would benefit from (1) utilizing a broader set of research methods, (2) more strongly emphasizing reflexivity, and (3) employing qualitative guidelines and quality criteria. We present an overview of three qualitative methods commonly used in social sciences but rarely seen in software engineering research, namely interpretative phenomenological analysis, narrative analysis, and discourse analysis. Furthermore, we discuss the meaning of reflexivity in relation to the software engineering context and suggest means of fostering it. Our paper will help software engineering researchers better select and then guide the application of a broader set of qualitative research methods. [ABSTRACT FROM AUTHOR]

Software process simulation models (SPSMs) that are based on descriptive process models offer the executability that can demonstrate dynamic changes of software processes over time. Verification and validation (V&V) is critical in SPSMs for guaranteeing the quality and reliability of models. V&V of dynamic software process models is more complex and challenging than for static software process models. This work systematically summarizes and maps V&V studies in SPSM to provide guidelines for future research and practice. Specifically, this study aims at identifying the focus of research on V&V, the methods used for V&V, and how to implement V&V of SPSMs in software engineering research. We conducted a systematic mapping study on studies of SPSMs that report on their V&V activities. Under the guidance of a V&V meta‐model for SPSMs, we study four research questions about V&V process. We identified 107 primary studies from a pool of 313 papers on SPSMs until 2021. There are two main results of our study. The first one presents the relationship between quality aspects of SPSMs and the V&V methods to assure them. The second result reveals the relationships among the modeling process, three modeling steps, five quality aspects, and 10 V&V methods. Generally, researchers do not pay sufficient attention to V&V, as 65.8% ((313−107)/313) failed to mention or elaborate on their V&V process. We systematically summarize and map the state‐of‐the‐art V&V research in software process modeling field to support modelers' practice and improve their V&V process. [ABSTRACT FROM AUTHOR]

Software engineers widely acknowledge the inclusion of security requirements in the early stages of the development process. However, the need to prepare the software for the failure of the implemented security controls and subsequent investigation of the incident is often not discussed. Forensic‐ready software systems represent an evolution of secure systems being designed for the eventual digital forensic investigation. However, their exact properties remain largely unexplored, beyond preliminary high‐level conceptualizations of requirements and capabilities. Further obstacles hindering the adoption of forensic‐ready software systems are the different priorities and goals of involved parties and a gap in the digital forensics expertise of software engineers. In this paper, we conduct an empirical qualitative study identifying the problems and needs of forensic readiness while framing the notion of an ideal forensic‐ready software system and how it should treat potential evidence. To this end, we conducted semisupervised interviews with digital forensics experts on their idea, experience, and suggestions. The results provide insights into the needs of the experts to facilitate the definition of correct requirements towards forensic‐ready software systems to support the anticipated investigations properly. [ABSTRACT FROM AUTHOR]

Requirements collection is difficult and a critical phase of software development life cycle, in particular in a global software development (GSD) environment. In GSD, clients and vendors are physically separated such that there exist challenges such as lack of face‐to‐face communication, language differences, culture variation, and time zone differences. The objective of current research is to identify critical challenges of requirement engineering in GSD. There is no work done yet to empirically analyze all possible challenges via questionnaire survey from software industries. This research paper empirically investigates and analyze the identified challenges from systematic literature review (SLR) based on a questionnaire survey. For this purpose, 50 respondents from different countries are organized. From this research, 13 challenges during requirements implementation in the context of GSD have been identified previously via SLR. These challenges were then evaluated using an empirical approach of questionnaire survey. The results from respondents were analyzed based on type of respondents, level of experience of respondents, and from client–vendor perspective. Finally, the challenges were prioritized based on its frequency of occurrences from the SLR and the questionnaire survey. The relationships between the challenges and the survey results were evaluated using the Spearman's correlation coefficient. The results produced a 0.835 Spearman's correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference. The implication of this research work lies in both fundamental and practical perspective. The prioritized set of challenges was provided based on SLR, and questionnaire survey acts as a knowledge base for both researchers and industrial practitioners. This work will help researchers to identify challenges in GSD projects or other software engineering areas. [ABSTRACT FROM AUTHOR]

Software effort estimation is a vital process in the software industry for successfully administering 5Ds of the software development life cycle (SDLC). The 5Ds stand for demand, development, direction, deployment, and designated cost of the software. Software development effort estimation (SDEE) is an effort prediction mechanism to calculate the effort for the development of the software product in order to minimize the challenges in the software field. Academics and practitioners are striving to identify which machine learning estimation technique yields more accurate results based on evaluation metrics, datasets, and other pertinent aspects. The feature selection techniques impact accuracy by selecting the main and relevant features in the dataset and eliminating the redundant and irrelevant features in the dataset. To achieve accurate estimations, the paper utilizes feature selection algorithms, along with various machine learning techniques, which predict the desired effort and the performance of the model has been measured in terms of prediction accuracy, R2 value, relative error, and mean absolute error. The datasets China and Maxwell are trained with the relevant features by applying feature selection algorithms, and estimation techniques are applied to predict the effort. The performance is compared with the regression models and feature selection techniques utilized by many authors previously. The result of the proposed methodology significantly gives the best performance with the combination of feature selection and estimation models than all regression models when applied alone, to both datasets. From the results, it is perceptible that random forest is performing well with the feature selection techniques and obtains the highest prediction accuracy of 99.33% with the China and 89.47% with the Maxwell datasets. [ABSTRACT FROM AUTHOR]

In this paper an analysis of a technical support data with the goal of identifying process improvement actions for reducing interrupts is presented. A technical support chat is established and used to provide internal developer support to other development teams, which use the software code developed by a core team. The paper shows how data analysis of a 6-month support time helped to identify gaps and action items for improving the technical support process to minimize interrupts from other developer teams. We show that developers are interrupted through the investigated technical support chat multiple times a day and the interruptions come during the peak working hours. We also show that these interruptions can be prevented with the introduction of multiple tools such as a dispatcher service policy, a dispatcher role, and an FAQ page among others. The paper also shows effects (advantages and drawbacks) of the technical support refactor actions taken on the basis of this analysis. [ABSTRACT FROM AUTHOR]

This volume comprises a selected set of high‐quality and extended articles of the 26th Systems, Software and Services Process Improvement (EuroSPI) Conference, held during September 18–20, 2019 in Edinburgh, UK. Conferences were held in Dublin (Ireland) in 1994, in Vienna (Austria) in 1995, in Brighton (UK) in 1996, in Budapest (Hungary) in 1997, in Gothenburg (Sweden) in 1998, in Pori (Finland) in 1999, in Copenhagen (Denmark) in 2000, in Limerick (Ireland) in 2001, in Nuremberg (Germany) in 2002, in Graz (Austria) in 2003, in Trondheim (Norway) in 2004, in Budapest (Hungary) in 2005, in Joensuu (Finland) in 2006, in Potsdam (Germany) in 2007, in Dublin (Ireland) in 2008, in Alcala (Spain) in 2009, in Grenoble (France) in 2010, in Roskilde (Denmark) in 2011, in Vienna (Austria) in 2012, in Dundalk (Ireland) in 2013, in Luxembourg in 2014, in Ankara (Turkey) in 2015, in Graz (Austria) in 2016, in Ostrava (Czech Republic) in 2017, in Bilbao (Spain) in 2018, and in Edinburgh (UK) in 2019. [ABSTRACT FROM AUTHOR]

Enterprise architecture is a growing trend that aims to help deal with the complexity of socio‐technical systems such as human organizations, as well as their information technology and systems areas. Nevertheless, the contribution of enterprise architecture to the field of software engineering remains unclear. The purpose of this systematic literature review is to see how enterprise architecture is used in software development and maintenance practice. To this end, we first carried out a search in the SCOPUS database and then organized the papers according to the Software Engineering Body of Knowledge to determine what areas of software engineering are covered by each research study. To understand how enterprise architecture is used, we established a classification based on ISO 42010 and TOGAF. From the systematic literature review, we noticed that the early stages of development are the most impacted by the enterprise architecture. On the other hand, we observed that enterprise architecture is of assistance in the areas of engineering management, engineering processes, and engineering models and methods; these tasks are carried out by teams or managers using different, often agile, development methods or standards. In turn, we found that the most common categories are architecture descriptions; these are often used to facilitate communication and information‐sharing between different stakeholders, in addition to frameworks, which will help to establish common practices in the organization related to the joint use of enterprise architecture and software development. [ABSTRACT FROM AUTHOR]

Over the past few years, the software engineering (SE) community has widely employed deep learning (DL) techniques in many source code processing tasks. Similar to other domains like computer vision and natural language processing (NLP), the state‐of‐the‐art DL techniques for source code processing can still suffer from adversarial vulnerability, where minor code perturbations can mislead a DL model's inference. Efficiently detecting such vulnerability to expose the risks at an early stage is an essential step and of great importance for further enhancement. This paper proposes a novel black‐box effective and high‐quality adversarial attack method, namely CodeBERT‐Attack (CBA), based on the powerful large pre‐trained model (i.e., CodeBERT) for DL models of source code processing. CBA locates the vulnerable positions through masking and leverages the power of CodeBERT to generate textual preserving perturbations. We turn CodeBERT against DL models and further fine‐tuned CodeBERT models for specific downstream tasks, and successfully mislead these victim models to erroneous outputs. In addition, taking the power of CodeBERT, CBA is capable of effectively generating adversarial examples that are less perceptible to programmers. Our in‐depth evaluation on two typical source code classification tasks (i.e., functionality classification and code clone detection) against the most widely adopted LSTM and the powerful fine‐tuned CodeBERT models demonstrate the advantages of our proposed technique in terms of both effectiveness and efficiency. Furthermore, our results also show (1) that pre‐training may help CodeBERT gain resilience against perturbations further, and (2) certain pre‐training tasks may be beneficial for adversarial robustness. [ABSTRACT FROM AUTHOR]

Cloud computing is characterized as a shared computing and communication infrastructure. It encourages the efficient and effective developmental processes that are carried out in various organizations. Cloud computing offers both possibilities and solutions of problems for outsourcing and management of software developmental operations across distinct geography. Cloud computing is adopted by organizations and application developers for developing quality software. The cloud has the significant impact on utilizing the artificial complexity required in developing and designing quality software. Software developmental organization prefers cloud computing for outsourcing tasks because of its available and scalable nature. Cloud computing is the ideal choice utilized for development modern software as they have provided a completely new way of developing real‐time cost‐effective, efficient, and quality software. Tenants (providers, developers, and consumers) are provided with platforms, software services, and infrastructure based on pay per use phenomenon. Cloud‐based software services are becoming increasingly popular, as observed by their widespread use. Cloud computing approach has drawn the interest of researchers and business because of its ability to provide a flexible and resourceful platform for development and deployment. To determine a cohesive understanding of the analyzed problems and solutions to improve the quality of software, the existing literature resources on cloud‐based software development should be analyzed and synthesized systematically. Keyword strings were formulated for analyzing relevant research articles from journals, book chapters, and conference papers. The research articles published in (2011–2021) various scientific databases were extracted and analyzed for retrieval of relevant research articles. A total of 97 research publications are examined in this SLR and are evaluated to be appropriate studies in explaining and discussing the proposed topic. The major emphasis of the presented systematic literature review (SLR) is to identify the participating entities of cloud‐based software development, challenges associated with adopting cloud for software developmental processes, and its significance to software industries and developers. This SLR will assist organizations, designers, and developers to develop and deploy user‐friendly, efficient, effective, and real time software applications. [ABSTRACT FROM AUTHOR]

A large volume of highly diverse and real‐time data provided by social network sites has revolutionized several data analytical sciences such as stock exchange fluctuation prediction, sentiment analysis, and political affiliations prediction. From a software engineering viewpoint, social network sites have produced an unprecedented opportunity for software development organizations to monitor the opinion of the large population of users of their systems. The extracted data from social network sites can be used to extract information about newly launched systems, and it enable software development organizations to obtain rapid technical and social feedback about their systems. This paper presents on a systematic literature review of the published research articles on how requirements engineering (RE) activities are conducted using social network sites, elaborated in studies published between 2011 and 2019. From the resulting studies, we extracted data to answer our four questions related to social network site (SNS)‐based RE approaches focusing on requirements elicitation, prioritization, and negotiation activities. Thirty‐one relevant studies were identified and analyzed to answer our research questions. Among 31 studies, 62% of the studies reportedly support requirements elicitation activity, and 14% of the studies reportedly support requirements elicitation, prioritization, and negotiation activities. Ten percent studies were about requirements elicitation and prioritization activities, 7% of the studies support both requirements elicitation and negotiation activities, and prioritization activity was supported by 7% of the studies. It was also observed that 58% of the selected studies adopted semi‐automatic approaches to conduct RE activities while 21% of the selected studies proposed automatic tools, and 21% of the selected studies adopted manual approaches to conduct RE activities. The outcomes of this systematic literature review indicate that social network sites can be a major source that can be used successfully to extract and identify user requirements. Moreover, this systematic literature review also presents implications of this study for practitioners and researchers including challenges, unresolved issues, and future research directions. [ABSTRACT FROM AUTHOR]

This volume comprises a selected set of high quality and extended articles of the 25th Systems, Software and Services Process Improvement (EuroSPI) Conference, held during September 5-7, 2018, in Bilbao, Spain. Establishing an annual EuroSPI conference supported by software process improvement networks from different EU countries. 7 Khan AA, Keung J, Niazi M, Hussain S, Shameem M. GSEPIM: A roadmap for software process assessment and improvement in the domain of global software development. 12 Barafort B, Shrestha A, Cortina S, Renault A. A software artefact to support standard-based process assessment: Evolution of the TIPA® framework in a design science research project. [Extracted from the article]

We are living in an age of growing demand for software products. This growing demand creates opportunities for very small entities (VSEs) to not only survive but also flourish. In this context, VSEs need to produce high‐quality products to meet market needs. However, in their quest to produce high‐quality software, VSEs need to overcome the challenge of implementing international standards, which they find difficult to do because of lack of knowledge and practical experience. This paper provides an analysis performed to 13 teams of VSEs, using different life cycles, which achieved the implementation of the ISO/IEC 29110, to analyze the effort each team invests to implement the best practices provided by the standard. Besides, the paper provides an analysis of the difficulties, and the benefits of using the six‐step method are included. This analysis is of interest because software engineering knowledge developed by researchers should be transferred to the industry to reduce the gap between them. The results highlight, on the one hand, the practices representing more effort for teams by life cycle. On the other hand, the results highlight the six‐step method that allowed the 13 teams to achieve a high level of coverage of the ISO/IEC 29110 international standard. [ABSTRACT FROM AUTHOR]

Abstract: Software developing organizations strive to achieve flexibility to maintain a competitive advantage. There is no common understanding of what characterize flexibility for a software organization beyond the scope of the software product. Without a common understanding, it is difficult to evaluate the degrees of flexibility of software development approaches. The aim of this literature review is to collect attributes that characterize flexibility. The collected attributes are consolidated into a flexibility framework with 3 main attributes: properties of change, flexibility perspectives, and flexibility enablers. The resulting flexibility framework is then used to evaluate Agile and Lean practices. The evaluation shows that Agile and Lean practices address many flexibility attributes. However, some attributes are not addressed, such as infrastructure flexibility and strategic flexibility. On the basis of our evaluation, the classifications of flexibility attributes that we present in this paper could be used to aid software organization flexibility evaluation. [ABSTRACT FROM AUTHOR]

Abstract: Over the last decade, the use of agile methods has grown dramatically for software development. Agile methods guarantee to accelerate the delivery of remarkable software with increased user satisfaction and reduced cost. However, in recent years, due to the emergence of green software engineering, software developers are compelled to focus more on green and sustainable aspects of software. Green software engineering aims to design, develop, and use the software with confined energy and computing resources. Recently, software engineers in global software development have adapted agile methods for quick, interactive, and environment‐friendly software development. In this study, we have identified 16 success factors, through systematic literature review (SLR) and applied contrived search criteria derived from the research questions; 80 relevant papers were identified and reviewed. Findings of the SLR study were then empirically validated through questionnaire survey in global software development industry, in which 106 experts from 25 different countries participated. The findings of our industrial survey are mostly in coherence with the SLR findings. However, there is a difference in ranks of the various success factors across the 2 data sets (SLR and industrial survey). [ABSTRACT FROM AUTHOR]

Reverse engineering of sequence diagrams refers to the process of extracting meaningful information about the behavior of software systems in the form of appropriately generated sequence diagrams. This process has become a practical method for retrieving the behavior of software systems, primarily those with inadequate documentation. Various approaches have been proposed in the literature to produce from a given system a series of interactions that can be used for different purposes. The reason for such diversity of approaches is the need to offer sequence diagrams that can cater for the users' specific goals and needs, which can vary widely depending on the users' perception and understandability of visual representations and the target application domains. In this paper, we systematically review existing techniques in this context while focusing on their distinct purposes and potentials of providing more understandable sequence diagrams. In addition, a qualitative evaluation of such techniques is conducted to expose their adequacy and applicability for effective program comprehension. Finally, we list a set of possible extensions to the unified modeling language sequence diagram standard that we anticipate will enhance its versatility and understandability of program control flow, followed by a number of concluding remarks. [ABSTRACT FROM AUTHOR]

Bug assignment is the task of ranking candidate developers in terms of their potential competence to fix a bug report. Numerous methods have been developed to address this task, relying on different methodological assumptions and demonstrating their effectiveness with a variety of empirical studies with numerous data sets and evaluation criteria. Despite the importance of the subject and the attention it has received from researchers, there is still no unanimity on how to validate and comparatively evaluate bug‐assignment methods and, often times, methods reported in the literature are not reproducible. In this paper, we first report on our systematic review of the broad bug‐assignment research field. Next, we focus on a few key empirical studies and review their choices with respect to three important experimental‐design parameters, namely, the evaluation metric(s) they report, their definition of who the real assignee is, and the community of developers they consider as candidate assignees. The substantial variability on these criteria led us to formulate a systematic experiment to explore the impact of these choices. We conducted our experiment on a comprehensive data set of bugs we collected from 13 long‐term open‐source projects, using a simple Tf‐IDf similarity metric. On the basis of our arguments and/or experiments, we provide useful guidelines for performing further bug‐assignment research. We conclude that mean average precision (MAP) is the most informative evaluation metric, the developer community should be defined as "all the project members," and the real assignee should be defined as "any developer who worked toward fixing a bug." [ABSTRACT FROM AUTHOR]

Context Software testing is an important and costly software engineering activity in the industry. Despite the efforts of the software testing research community in the last several decades, various studies show that still many practitioners in the industry report challenges in their software testing tasks. Objective: To shed light on industry's challenges in software testing, we characterize and synthesize the challenges reported by practitioners. Such concrete challenges can then be used for a variety of purposes, eg, research collaborations between industry and academia. Method: Our empirical research method is opinion survey. By designing an online survey, we solicited practitioners' opinions about their challenges in different testing activities. Our dataset includes data from 72 practitioners from eight different countries. Results: Our results show that test management and test automation are considered the most challenging among all testing activities by practitioners. Our results also include a set of 104 concrete challenges in software testing that may need further investigations by the research community. Conclusion: We conclude that the focal points of industrial work and academic research in software testing differ. Furthermore, the paper at hand provides valuable insights concerning practitioners' "pain" points and, thus, provides researchers with a source of important research topics of high practical relevance. [ABSTRACT FROM AUTHOR]

In recent years, due to significant evolution in adopting new technologies and development methodologies in the field of software engineering, the developers and researchers are striving to optimize the accuracy of software effort estimation (SEE). The overestimation and underestimation both are the key challenges for software progress; hence, there is a continuous need for an accurate SEE. This paper highlights a systematic review of studies associated with the best practices of use case point (UCP) and expert judgment–based software development effort estimation techniques. The primary aim and contribution of this paper are to support the researchers through an extensive review to ease to other researcher's search for effort estimation studies. We have performed state‐of‐the‐art review from five viewpoints of reference: (a) review of studies concerning UCPs and expert judgment–based effort estimation, (b) research contribution and future recommendation in different novelties, (c) usage of the dataset, (d) availability of accuracy metrics, and (e) findings of the studies. We have performed a systematic review of studies which are published in the period of 2000 to 2019. We have selected a total of 34 primary studies of UCP and expert judgment–based estimation techniques to report the research questions stated in this review. In expert judgment–based technique, the team's skill level is considered to be the most important factor. The researchers prefer to use industrial dataset due to availability of ease. It was investigated that using diverse evaluation accuracy metrics produces conflicting results. On the basis of our studies, we cannot state the results of the UCP method in algorithmic and machine learning approaches to be the most promising. Nevertheless, the SEE accuracy is dependent on the use of datasets, techniques, and evaluation metrics. [ABSTRACT FROM AUTHOR]

Literature on systems development has been progressively identifying the importance of social aspects in systems development. However, there is often a failure of participants in the recognition, and fulfilment, of ethical duties concerning the concepts of health, safety, and well‐being. A rational appeal can be made to normative defensible ethical rules in order to arrive at a judicious, morally justifiable judgement. In this paper, our first step is to report on the findings of a literature review, which presents the current health and safety issues concerning usage of computers in organisations and the workplace. Building on our earlier research on basic generic deontological and teleological moral principles and theories, we prescribe a set of moral rights and duties that must be exercised and fulfilled by stakeholders in systems development in order for them to exhibit moral behaviour. By identifying, and recommending a set of defensible moral obligations that must be fulfilled in the development and deployment of systems, protagonists—such as, project managers, software engineering teams, systems analysts, and clients—can fulfil their ethical duties, thus increasing the likelihood of deployed systems that are compliant with principles of health, safety, and well‐being of its users. [ABSTRACT FROM AUTHOR]

Summary: The importance of the team, its internal dynamics, and its performance are widely recognized within the software engineering community. While popular frameworks identify wholeness, stability over time, and smallness as important factors, they offer little guidance on how to form teams that achieve these three characteristics. The objective of this study is to investigate how these team characteristics interact in large‐scale software development contexts, particularly focusing on the impact of stable and dynamic teaming approaches. This was done through a multivocal study of literature, followed by individual semi‐structured interviews with 19 engineers from two companies and validation workshops with an additional two companies from unrelated industry segments. The study results show that the question of stable versus dynamic approaches to forming software engineering teams is largely unaddressed in industry, with stable teams representing a habitual default option. Meanwhile, both stable and dynamic teams clearly have respective strengths and weaknesses, calling for careful consideration of the most suitable approach in any given situation. To support such consideration, this paper presents a model of how team stability, wholeness, and smallness interact. This model is found relevant, accurate, generalizable, and useful by practitioners. [ABSTRACT FROM AUTHOR]

Vulnerabilities in the source code of the software are critical issues in the realm of software engineering. Coping with vulnerabilities in software source code is becoming more challenging due to several aspects such as complexity and volume. Deep learning has gained popularity throughout the years as a means of addressing such issues. This paper proposes an evaluation of vulnerability detection performance on source code representations and evaluates how machine learning (ML) strategies can improve them. The structure of our experiment consists of three deep neural networks (DNNs) in conjunction with five different source code representations: abstract syntax trees (ASTs), code gadgets (CGs), semantics‐based vulnerability candidates (SeVCs), lexed code representations (LCRs), and composite code representations (CCRs). Experimental results show that employing different ML strategies in conjunction with the base model structure influences the performance results to a varying degree. However, ML‐based techniques suffer from poor performance on class imbalance handling and dimensionality reduction when used in conjunction with source code representations. [ABSTRACT FROM AUTHOR]

Deep learning has recently experienced explosive growth in use, largely due to advances in neural networks and the availability of large corpora of domain data. Project management activities generate and handle large volumes of data. Software engineering closely relates to project management, so software engineering projects must be prone to the use of neural networks. We seek to obtain an accurate vision of how neural networks are being used in software engineering projects through a systematic mapping study. We confirm that neural networks have already made their way into these projects; however, we show that their current uses are limited to certain repetitive and legacy tasks. Given uncovered ample room for expansion, we point out a few directions the industry and academy can lean toward to in the next years for taking better advantage of neural networks in software engineering projects and immediately advancing the field. We investigate if, how, and to what extent have neural networks been employed to the advancement of software engineering projects. As such, a systematic mapping study was conducted, which led to the conclusion that even though these algorithms have indeed been employed on several software engineering tasks, this employment so far has been shy, mostly relying on legacy types of neural networks. More modern variants, namely, deep learning algorithms, are slowly gaining momentum and should be the trend going forward. [ABSTRACT FROM AUTHOR]

The assessment of software quality is one of the most multifaceted (eg, structural, product, and process quality) and subjective aspects of software engineering, as in most cases, it is substantially based on expert judgement. This special issue aims at providing researchers with the possibility to spread novel ideas and methods to make machine learning actionable for the assessment of software quality artifacts and processes. It is our hope that the papers presented in this special issue will further foster the research community toward the intersection between machine learning and software quality assessment. [Extracted from the article]

The process of writing model transformations is a complex and error-prone one. Thus, efficient techniques and tools for validating model transformations are needed. One of them is model transformation testing. The generation of test cases for model transformations is mainly based on metamodel and rules coverage criteria. In this paper, we propose to treat model transformation testing as a bi-level optimization problem to combine the generation of test cases with mutation testing. In our adaptation, the upper-level problem generates a set of test cases that maximizes the coverage of metamodels and errors introduced by the lower level to the transformation rules. The lower level maximizes the number of generated errors in the rules that cannot be detected by the test cases produced by the upper level. The main advantage of our bi-level formulation is that the evaluation of test cases is not limited to the coverage of metamodels, but it allows evaluating their ability to detect errors. The statistical analysis of our experiments on different transformation mechanisms confirms the outperformance of our bi-level proposal compared with state-of-the-art model transformation testing techniques. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports within the issue on topics including computer software development, software engineering and application software.

Context It has been observed that software process improvement (SPI) initiatives have been around for more than a decade, yet many companies are still facing SPI implementation challenges. Objective In this paper, a systematic literature review (SLR) has been conducted, and the results are compared with the previously collected data of SPI implementation success factors. The overarching objective of this research is to gain an in-depth understanding of success factors that play a positive role in SPI implementation; this will assist SPI managers in better designing and managing SPI programs. We are interested to discover if there are any differences in the success factors identified by the three research strategies used, that is, informal literature review, SLR, and interviews. Method Data were collected and analyzed using the three research strategies. For informal literature review, the snowballing technique was used. The SLR was conducted for formal literature reviews. For an empirical study, interviews were conducted with Australian SPI practitioners. Results Results show that (i) senior management commitment and (ii) staff involvement are the frequently cited common SPI success factors in all three research strategies. The results also uncover similarities and differences among the three research strategies regarding the importance of the SPI success factors: (i) SPI awareness is a frequently cited common SPI success factor in interviews and formal literature review, and the Spearman's rank-order correlation is rs(37) = 0.413, p = 0.011; (ii) reviews are a frequently cited common SPI success factor in formal and informal literature reviews, and the Spearman's rank-order correlation is rs(37) = 0.517, p = 0.001; and (iii) training and mentoring is a frequently cited common SPI success factor in interviews and an informal literature review, and the Spearman's rank-order correlation is rs(37) = 0.195, p = 0.247. Conclusions The understanding of these factors can help both researchers and software development organizations in successfully planning and implementing SPI initiatives. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the Journal of Software: Evolution & Process focused on software refactoring. The issue aims to advance understanding of software refactoring practices and their impact. The selected papers cover a range of topics, including the side effects of refactoring, reducing software hierarchy complexity, detecting code smells using deep learning, the relationship between developer experience and refactoring, the disuse of refactoring tools, the association between refactorings, code smells, and faults, and the severity classification of code smells using machine learning. The editorial also highlights the potential of generative AI and machine learning in automating and enhancing refactoring processes. The authors express their appreciation to the contributors, peer reviewers, and editorial board members for their efforts in assembling the special issue. [Extracted from the article]

Abstract: Evolvability of software lies in intersection of 3 factors: evolving system properties, human factors present in the developer team, and evolution demands. The paper presents 5 recommendations that enhance software evolvability: defined processes of software change, distinction between evolving and stabilized part of the code, analyzable code segments, significant concept encapsulation, and avoidance of wrapping. [ABSTRACT FROM AUTHOR]

The service‐oriented software engineering approach has been increasingly utilized to design and develop complex distributed systems. Exploiting semantic web technologies to increase utilization of the web services at the semantic level leads to create semantic web services (SWSs). This paper proposes a model‐driven architecture (MDA) to model the SWS and to transform it from a high‐level modeling language, such as the Unified Modeling Language (UML), to a low‐level semantic description, such as the Web Service Modeling Language (WSML). To annotate all aspects of the SWS, a UML profile is provided by extending the UML metamodel. Some stereotypes and tagged values are also defined to support WSML. Therefore, the structure aspects of the SWS are modeled with a class diagram based on stereotypes that are defined in the UML profile. The logical and behavioral aspects of the SWS are modeled and expressed using activity diagrams, sequence diagrams, and Object Constraint Language (OCL). Experimental results show that the proposed approach not only increases the level of independence, accuracy, expressiveness, understandability, and machine processability but also significantly reduces the complexity and heterogeneity. Moreover, the proposed method is also evaluated in comparison with the previous methods of the literature, and the results will show that the proposed method outperforms the other methods in terms of expressiveness, understandability, scalability, level of independence, and accuracy. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) models are often used for benchmarking software projects, and traditional DEA models only allow for the use of continuous variables. This study considers the use of DEA for datasets with mixed continuous and discrete variables to ranking software projects. It uses the existing radial DEA model and extends the nonradial DEA model to allow for the use of mixed variables. Further efficiency scores from the two DEA models are averaged in an ensemble DEA score. Using three real‐world software engineering datasets, this study finds that the nonradial DEA and the ensemble DEA models have better discriminating power (lower tied efficiency scores) to rank software projects, and the radial DEA model generates more general ranking distribution (higher entropy) of normalized efficiency scores. The choice between selecting radial and nonradial DEA models for ranking software projects appears to depend on the extent to which managers want to introduce bias into the efficiency score ranking distribution. Radial models appear to have a lower bias than nonradial models. The ensemble DEA model appears to be the best performing DEA model for datasets containing two or more discrete and continuous variables. The paper uses mixed continuous and discrete variables for DEA‐based software project analysis. The analysis is conducted for both radial and nonradial DEA models. Models are averaged into an ensemble DEA score. [ABSTRACT FROM AUTHOR]

Software engineering is decision intensive. Evidence‐based software engineering is suggested for decision‐making concerning the use of methods and technologies when developing software. Software development often includes the reuse of software assets, for example, open‐source components. Which components to use have implications on the quality of the software (e.g., maintainability). Thus, research is needed to support decision‐making for composite software. This paper presents a roadmap for research required to support evidence‐based decision‐making for choosing and integrating assets in composite software systems. The roadmap is developed as an output from a 5‐year project in the area, including researchers from three different organizations. The roadmap is developed in an iterative process and is based on (1) systematic literature reviews of the area; (2) investigations of the state of practice, including a case survey and a survey; and (3) development and evaluation of solutions for asset identification and selection. The research activities resulted in identifying 11 areas in need of research. The areas are grouped into two categories: areas enabling evidence‐based decision‐making and those related to supporting the decision‐making. The roadmap outlines research needs in these 11 areas. The research challenges and research directions presented in this roadmap are key areas for further research to support evidence‐based decision‐making for composite software. [ABSTRACT FROM AUTHOR]