Deep reinforcement learning (DRL) is increasingly applied to de novo molecular design, but choices in data, rewards, and evaluation can yield uneven performance across disease areas and chemotypes. Despite this, there is no concise synthesis of how fairness is defined, measured, and tested in DRL-based drug discovery. In this rapid evidence review, we synthesize fairness definitions and metrics for DRL-driven molecule generation in healthcare. We focus on three questions: (i) how dataset composition and split strategies, especially scaffold versus random splits, affect evaluation and distribution shift; (ii) how reward design (e.g., QED, docking, toxicity, synthetic accessibility) can create or mitigate bias, with emphasis on cancer targets; and (iii) which measurable metrics best capture fairness. This includes parity across cancer versus non-cancer indications and across cancer subtypes. It also includes distributional balance in key physicochemical descriptors, scaffold/chemotype diversity, groupwise validity, toxicity, and synthetic accessibility. From 2017 onward, we searched major biomedical, computer science, and engineering literature databases and used arXiv for horizon scanning. Records were screened using PRISMA-style procedures and analyzed via content coding to link reported parity outcomes to dataset and reward choices. Our review provides a concise set of fairness definitions and metrics for DRL molecule generation. It offers practical guidance for reporting distribution parity and outcome parity. It also summarizes how dataset and reward choices relate to observed parity effects and identifies open gaps relevant to trustworthy, cancer-relevant DRL generation.

AI coding agents contribute thousands of pull requests daily to open-source repositories, yet practitioners lack empirical guidance on optimal contribution sizing. We adapt the therapeutic index framework from pharmacology to characterise the relationship between pull request size and integration outcomes. Analysing 33,078 agent-authored pull requests from the AIDev dataset across five AI coding agents, we model dose-response relationships for efficacy (merge probability) and toxicity (review friction). All agents exhibit statistically significant negative relationships between size and merge probability (p < 0.001), differing substantially in baseline performance (47.5% to 82.6%) and dose sensitivity. Task type substantially moderates these relationships: bug fixes exhibit ED50 of 1,467 lines [95% CI: 1,025–2,216] with therapeutic index of 35.2, whilst features show ED50 of 18,234 lines [95% CI: 12,337–28,390] with therapeutic index of 2.3. This 15-fold difference, confirmed by non-overlapping confidence intervals, indicates that bug fixes exhibit wider therapeutic windows whilst feature implementations exhibit narrower windows.

Software engineering (SE) faces significant diversity challenges in both academia and industry, with underrepresented students encountering hostile environments, limited representation, and systemic biases that hinder their academic and professional success. Despite significant research on the exclusion experienced by students from underrepresented groups in SE education, there is limited understanding of the specific motivations, influences, and role models that drive underrepresented students to pursue and persist in the field. This study explores the motivations and influences shaping the career aspirations of students from underrepresented groups in SE, and it investigates how role models and mentorship impact their decisions to stay in the field. We conducted a cross-sectional survey with undergraduate SE students and related fields, focusing on their motivations, influences, and the impact of mentorship and role models on their career paths. We identified eight motivations for pursuing SE, with career advancement, technological enthusiasm, and personal growth being the most common. Family members, tech influencers, teachers, and friends were key influences, though 64 % of students reported no specific individual influence. Role models, particularly tech influencers and family members play a critical role in sustaining interest in the field, especially for underrepresented groups. This study provides insights into the varied motivations and influences that guide underrepresented students' decisions to pursue SE. It emphasizes the importance of role models and highlights the need for intersectional approaches to better support diversity in the field.

Context: Diversity can impact team communication, productivity, cohesiveness, and creativity. Analyzing the existing knowledge about diversity in open source software (OSS) projects can provide directions for future research and raise awareness about barriers and biases against underrepresented groups in OSS. Objective: This study aims to analyze the knowledge about minority groups in OSS projects. We investigated which groups were studied in the OSS literature, the study methods used, their implications, and their recommendations to promote the inclusion of minority groups in OSS projects. Method: To achieve this goal, we performed a systematic literature review study that analyzed 42papers that directly study underrepresented groups in OSS projects. Results: Most papers focus on gender (62.3%), while others like age or ethnicity are rarely studied. The neurodiversity dimension, have not been studied in the context of OSS. Our results also reveal that diversity in OSS projects faces several barriers but brings significant benefits, such as promoting safe and welcoming environments. Conclusion: Most analyzed papers adopt a myopic perspective that sees gender as strictly binary. Dimensions of diversity that affect how individuals interact and function in an OSS project, such as age, tenure, and ethnicity, have received very little attention.

As software systems continue to grow in complexity, testing has become a fundamental part of ensuring the quality and reliability of software products. Yet, software testing is still often perceived, both in industry and academia, as a repetitive, low-skill activity. This perception fails to recognize the creativity, problem-solving, and adaptability required in testing work. Tasks such as designing complex test cases, automating testing processes, and handling shifting requirements illustrate the challenges testing professionals regularly face. To better understand these experiences, we conducted a study with software testing professionals to explore the nature of challenging tasks in software testing and how they affect these professionals. Our findings show that tasks involving creativity, ongoing learning, and time pressure are often seen as motivating and rewarding. On the other hand, a lack of challenge or overwhelming demands can lead to frustration and disengagement. These findings demonstrate the importance of balancing task complexity to sustain motivation and present software testing as a dynamic and intellectually engaging field.

Software testing ensures that a system functions correctly, meets specified requirements, and maintains high quality. As artificial intelligence and machine learning (ML) technologies become integral to software systems, testing has evolved to address their unique complexities. A critical advancement in this space is fairness testing, which identifies and mitigates biases in AI applications to promote ethical and equitable outcomes. Despite extensive academic research on fairness testing-including test input generation, test oracle identification, and component testing-practical adoption remains limited. Industry practitioners often lack clear guidelines and effective tools to integrate fairness testing into real-world AI development. This study investigates how software professionals test AI-powered systems for fairness through interviews with 22 practitioners working on AI and ML projects. Our findings highlight a significant gap between theoretical fairness concepts and industry practice. While fairness definitions continue to evolve, they remain difficult for practitioners to interpret and apply. The absence of industry-aligned fairness testing tools further complicates adoption, necessitating research into practical, accessible solutions. Key challenges include data quality and diversity, time constraints, defining effective metrics, and ensuring model interoperability. These insights emphasize the need to bridge academic advancements with actionable strategies and tools, enabling practitioners to systematically address fairness in AI systems.

Technology is a cornerstone of modern life, yet the software engineering field struggles to reflect the diversity of contemporary society. This lack of diversity and inclusivity within the software industry can be traced back to limited representation in software engineering academic settings, where students from underrepresented groups are often stigmatized despite the field's rich history of contributions from scientists from diverse backgrounds. Over the years, studies have revealed that women, LGBTQIA+ individuals, and Black students frequently encounter unwelcoming environments in software engineering programs. However, similar to other fields, increasing awareness of notable individuals from marginalized backgrounds could inspire students and foster a more inclusive environment. This study reports the findings from a replicated global survey with undergraduate software engineering students, exploring their knowledge of distinguished scientists from underrepresented groups. These findings show that students have limited awareness of these figures and their contributions, highlighting the need to improve diversity awareness and develop educational practices that celebrate the achievements of historically marginalized groups in software engineering.

Background: Software systems powered by large language models are becoming a routine part of everyday technologies, supporting applications across a wide range of domains. In software engineering, many studies have focused on how LLMs support tasks such as code generation, debugging, and documentation. However, there has been limited focus on how full systems that integrate LLMs are tested during development. Aims: This study explores how LLM-powered systems are tested in the context of real-world application development. Method: We conducted an exploratory case study using 99 individual reports written by students who built and deployed LLM-powered applications as part of a university course. Each report was independently analyzed using thematic analysis, supported by a structured coding process. Results: Testing strategies combined manual and automated methods to evaluate both system logic and model behavior. Common practices included exploratory testing, unit testing, and prompt iteration. Reported challenges included integration failures, unpredictable outputs, prompt sensitivity, hallucinations, and uncertainty about correctness. Conclusions: Testing LLM-powered systems required adaptations to traditional verification methods, blending source-level reasoning with behavior-aware evaluations. These findings provide evidence on the practical context of testing generative components in software systems.

Context. The increasing deployment of artificial intelligence in decision-making processes has highlighted biases embedded in algorithms, leading to discriminatory outcomes that impact society. This phenomenon has led to the proposal of fairness debt, a type of software debt that emerges when biases are not addressed early in development and accumulate over time, creating costly, complex issues that can perpetuate societal inequities. Goal. This study aimed to explore the concept of fairness debt within algorithm-driven decision-making systems, identifying how deferred fairness considerations lead to accumulated biases and affect society. Method. We conducted a mapping study, analyzing 86 papers on algorithmic discrimination to classify root causes, effects, and instances of discrimination associated with fairness debt. Findings. Our findings indicate that several root causes, such as training bias, historical bias, and design bias, contribute to the accumulation of fairness debt, with manifestations in issues like sexism, racism, ableism, and ageism within systems. These accumulated biases can exacerbate social inequalities, limit algorithmic reliability, and perpetuate stereotypes, demonstrating fairness debt’s extensive societal impact. Discussions. Our study demonstrates that fairness debt extends beyond technical issues, encompassing broader societal consequences that demand proactive mitigation strategies, emphasizing the risks associated with deferring fairness considerations and highlighting the importance of fairness as a foundational principle in modern software systems, in particular AI systems. Conclusion. We explore algorithm discrimination through the lens of fairness debt, showcasing e that social concerns of algorithms can be interpreted as main component of software engineering practice, considering the socio-technical factors embedded in it and helping researchers and developers exploring discrimination in their systems and practices.

Ensuring fairness in software systems has become a critical concern in software engineering. Motivated by this challenge, this article explores the multifaceted nature of bias in software systems, providing a comprehensive understanding of its origins, manifestations, and impacts. Through a scoping study, we identified the primary causes of fairness deficiencies in software development and highlighted their adverse effects on individuals and communities, including instances of discrimination and the perpetuation of inequalities. Our investigation culminated in the introduction of the concept of software fairness debt. In addition to defining fairness debt, we propose a socio-technical roadmap that addresses broader aspects of fairness in AI-driven systems. This roadmap is structured around six goals: bridging the gap between research and real-world applications, developing a framework for fairness debt, equipping practitioners with tools and knowledge, improving bias mitigation, integrating fairness tools into industry practice, and enhancing explainability and transparency in AI systems. This roadmap provides a holistic approach to managing biases in software systems through software fairness debt, offering actionable steps for both research and practice. By guiding researchers and practitioners, our roadmap aims to foster the development of more equitable and socially responsible software systems, ensuring fairness is embedded throughout the software lifecycle.

Context: The increasing integration of artificial intelligence and machine learning into software systems has highlighted the critical importance of ensuring fairness in these technologies. Bias in software can lead to inequitable outcomes, making fairness testing essential. However, the current landscape of fairness testing tools remains underexplored, particularly regarding their practical applicability and usability for software development practitioners. Goal: This study aimed to evaluate the practical applicability of existing fairness testing tools for software development practitioners, assessing their usability, documentation, and overall effectiveness in real-world industry settings. Method: We identified 41 fairness testing tools from the literature and conducted a heuristic evaluation and documentary analysis of their installation processes, user interfaces, supporting documentation, and update frequencies. Technical analysis included assessing configurability for diverse datasets. The analysis focused on identifying strengths and deficiencies to determine their suitability for industry use. Findings: Our findings revealed that most fairness testing tools show significant deficiencies, particularly in user-friendliness, detailed documentation, and configurability. These limitations restrict their practical use in industry settings. The tools also lack regular updates and possess a narrow focus on specific datasets, which constrains their versatility and scalability. Despite some strengths, such as cost-effectiveness and compatibility with several environments, the overall landscape of fairness testing tools requires substantial improvements to meet industry needs. Conclusion: There is a pressing need to develop fairness testing tools that align more closely with industry requirements, offering enhanced usability, comprehensive documentation, and greater configurability to effectively support software development practitioners.

Context: This study explores how software professionals identify and address biases in AI systems within the software industry, focusing on practical knowledge and real-world applications. Goal: We focused on understanding the strategies employed by practitioners to manage bias and their implications for fairness debt. Method: We employed a qualitative research method, gathering insights from industry professionals through interviews and using thematic analysis to explore the collected data. Findings: Professionals identify biases through discrepancies in model outputs, demographic inconsistencies, and training data issues. They address these biases using strategies such as enhanced data management, model adjustments, crisis management, improving team diversity, and ethical analysis. Conclusion: Our paper presents initial evidence on addressing fairness debt and lays the groundwork for developing structured guidelines to manage fairness-related issues in AI systems.

The rapid adoption of remote and hybrid work models in response to the COVID-19 pandemic has brought significant changes to communication and coordination within software development teams, affecting how various activities are executed. Nowadays, these changes are shaping the new post-pandemic environments and continue to impact software teams. In this context, our study explores the characteristics and challenges of remote communication between software developers and software testers. We investigated how these professionals have adapted to the unique circumstances imposed by COVID-19, especially because many of them have now become permanent in the software industry. In this process, we explored their communication practices and interaction dynamics and how they potentially affect software evolution and quality. Our findings reveal that the transition to remote and hybrid work has resulted in notable changes in communication patterns and task coordination, which could potentially affect the overall quality of project deliverables. Additionally, we highlight the importance of adapting existing workflows, introducing new management practices, and investing in technology to facilitate remote interaction among developers and testers.

A Large Language Model (LLM) represents a cutting-edge artificial intelligence model that generates content, including grammatical sentences, human-like paragraphs, and syntactically code snippets. LLMs can play a pivotal role in soft-ware development, including software testing. LLMs go beyond traditional roles such as requirement analysis and documentation and can support test case generation, making them valuable tools that significantly enhance testing practices within the field. Hence, we explore the practical application of LLMs in software testing within an industrial setting, focusing on their current use by professional testers. In this context, rather than relying on existing data, we conducted a cross-sectional survey and collected data within real working contexts-specifically, engaging with practitioners in industrial settings. We applied quantitative and qualitative techniques to analyze and synthesize our collected data. Our findings demonstrate that LLMs effectively enhance testing documents and significantly assist testing professionals in programming tasks like debugging and test case automation. LLMs can support individuals engaged in manual testing who need to code. However, it is crucial to emphasize that, at this early stage, software testing professionals should use LLMs with caution while well-defined methods and guidelines are being built for the secure adoption of these tools.

Testing is an indispensable part of software development. However, a career in software testing is reported to be unpopular among technology students. This can potentially create a shortage of testers in the software industry in the future. The question is, whether the perception that undergraduate students have about software testing is accurate and whether it differs from the experience reported by those who work in testing activities in the software development industry. We obtained 63 answers from practitioners in the software industry, and previous surveys analyzed the perceptions of 648 students from software engineering. This investigation demonstrates that a career in software testing is more exciting and rewarding, as reported by professionals working in the field, than students may believe. Therefore, in order to guarantee a workforce focused on software quality, the academy and the software industry need to work together to better inform students about software testing and its essential role in software development. In particular, courses in testing automation are expected to increase the popularity of the area among students.

Context. Algorithmic racism is the term used to describe the behavior of technological solutions that constrains users based on their ethnicity. Lately, various data-driven software systems have been reported to discriminate against Black people, either for the use of biased data sets or due to the prejudice propagated by software professionals in their code. As a result, Black people are experiencing disadvantages in accessing technology-based services, such as housing, banking, and law enforcement. Goal. This study aims to explore algorithmic racism from the perspective of software professionals. Method. A survey questionnaire was applied to explore the understanding of software practitioners on algorithmic racism, and data analysis was conducted using descriptive statistics and coding techniques. Results. We obtained answers from a sample of 73 software professionals discussing their understanding and perspectives on algorithmic racism in software development. Our results demonstrate that the effects of algorithmic racism are well-known among practitioners. However, there is no consensus on how the problem can be effectively addressed in software engineering. In this paper, some solutions to the problem are proposed based on the professionals' narratives. Conclusion. Combining technical and social strategies, including training on structural racism for software professionals, is the most promising way to address the algorithmic racism problem and its effects on the software solutions delivered to our society.

Due to the high demand for mobile applications, given the exponential growth of users of this type of technology, testing professionals are frequently required to invest time in studying testing tools, in particular, because nowadays, several different tools are available. A variety of tools makes it difficult for testing professionals to choose the one that best fits their goals and supports them in their work. In this sense, we conducted a comparative analysis among five open-source tools for mobile testing: Appium, Robotium, Espresso, Frank, and EarGrey. We used the documentary analysis method to explore the official documentation of each above-cited tool and developed various comparisons based on technical criteria reported in the literature about characteristics that mobile testing tools should have. Our findings are expected to help practitioners understand several aspects of mobile testing tools.

Background. The COVID-19 pandemic triggered a widespread transition to hybrid work models (combinations of co-located and remote work) as software professionals’ demanded more flexibility and improved work-life balance. However, hybrid work models reduce the spontaneous, informal face-to-face interactions that promote group maturation, cohesion, and resilience. Little is known about how software companies can successfully transition to a hybrid workforce or the factors that influence the resilience of hybrid software development teams. Goal. The purpose of this study is to explore the relationship between hybrid work and team resilience in the context of software development. Method. Constructivist Grounded Theory was used, based on interviews of 26 software professionals. This sample included professionals of different genders, ethnicities, sexual orientations, and levels of experience. Interviewees came from eight different companies, 22 different projects, and four different countries. Consistent with grounded theory methodology, data collection, and analysis were conducted iteratively, in waves, using theoretical sampling, constant comparison, and initial, focused, and theoretical coding. Results. Software Team Resilience is the ability of a group of software professionals to continue working together effectively under adverse conditions. Resilience depends on the group’s maturity. The configuration of a hybrid team (who works where and when) can promote or hinder group maturity depending on the level of intra-group interaction it supports. Conclusion. This paper presents the first study on the resilience of hybrid software teams. Software teams need resilience to maintain their performance in the face of disruptions and crises. Software professionals strongly value hybrid work; therefore, team resilience is a key factor to be considered in the software industry.

Technology plays a crucial role in people’s lives. However, software engineering discriminates against individuals from underrepresented groups in several ways, either through algorithms that produce biased outcomes or for the lack of diversity and inclusion in software development environments and academic courses focused on technology. This reality contradicts the history of software engineering, which is filled with outstanding scientists from underrepresented groups who changed the world with their contributions to the field. Ada Lovelace, Alan Turing, and Clarence Ellis are only some individuals who made significant breakthroughs in the area and belonged to the population that is so underrepresented in undergraduate courses and the software industry. Previous research discusses that women, LGBTQIA+ people, and nonwhite individuals are examples of students who often feel unwelcome and ostracized in software engineering. However, do they know about the remarkable scientists that came before them and that share background similarities with them? Can we use these scientists as role models to motivate these students to continue pursuing a career in software engineering? In this study, we present the preliminary results of a survey with 128 undergraduate students about this topic. Our findings demonstrate that students’ knowledge of computer scientists from underrepresented groups is limited. This creates opportunities for investigations on fostering diversity in software engineering courses using strategies exploring computer science’s history.

Modern society is diverse, multicultural, and multifaceted. Because of these characteristics, we are currently observing an increase in the debates about equity, diversity, and inclusion in different areas, especially because several groups of individuals are underrepresented in many environments. In computer science and software engineering, it seems counterintuitive that these areas, which are responsible for creating technological solutions and systems for billions of users around the world, do not reflect the diversity of the society to which it serves. In trying to solve this diversity crisis in the software industry, researchers started to investigate strategies that can be applied to increase diversity and improve inclusion in academia and the software industry. However, the lack of diversity in computer science and related courses, including software engineering, is still a problem, in particular when some specific groups are considered. LGBTQIA+ students, for instance, face several challenges to fit into technology courses, even though most students in universities right now belong to Generation Z, which is described as open-minded to aspects of gender and sexuality. In this study, we aimed to discuss the state-of-art of publications about the inclusion of LGBTQIA+ students in computer science education. Using a mapping study, we identified eight studies published in the past six years that focused on this public. We present strategies developed to adapt curricula and lectures to be more inclusive to LGBTQIA+ students and discuss challenges and opportunities for future research.

Nowadays, software testing professionals are commonly required to develop coding skills to work on test automation. One essential skill required from those who code is the ability to implement code refactoring, a valued quality aspect of software development; however, software developers usually encounter obstacles in successfully applying this practice. In this scenario, the present study aims to explore how software testing professionals (e.g., software testers, test engineers, test analysts, and software QAs) deal with code refactoring to understand the benefits and limitations of this practice in the context of software testing. We followed the guidelines to conduct surveys in software engineering and applied three sampling techniques, namely convenience sampling, purposive sampling, and snowballing sampling, to collect data from testing professionals. We received answers from 80 individuals reporting their experience refactoring the code of automated tests. We concluded that in the context of software testing, refactoring offers several benefits, such as supporting the maintenance of automated tests and improving the performance of the testing team. However, practitioners might encounter barriers in effectively implementing this practice, in particular, the lack of interest from managers and leaders. Our study raises discussions on the importance of having testing professionals implement refactoring in the code of automated tests, allowing them to improve their coding abilities.

While the long-term effects of the COVID-19 pandemic on software professionals and organizations are difficult to predict, it seems likely that working from home, remote-first teams, distributed teams, and hybrid (part-remote/part-office) teams will be more common. It is therefore important to investigate the challenges that software teams and organizations face with new remote and hybrid work. Consequently, this paper reports a year-long, participant-observation, constructivist grounded theory study investigating the impact of working from home on software development. This study resulted in a theory of software team coordination. Briefly, shifting from in-office to at-home work fundamentally altered coordination within software teams. While group cohesion and more effective communication appear protective, coordination is undermined by distrust, parenting and communication bricolage. Poor coordination leads to numerous problems including misunderstandings, help requests, lower job satisfaction among team members, and more ill-defined tasks. These problems, in turn, reduce overall project success and prompt professionals to alter their software development processes (in this case, from Scrum to Kanban). Our findings suggest that software organizations with many remote employees can improve performance by encouraging greater engagement within teams and supporting employees with family and childcare responsibilities.

Background. Testing is an essential activity in the software development life cycle. Nowadays, testing activities are widely spread along the software development process, since software products are continuously tested to meet the user's expectations and to compete in global markets. In this context, internationalization testing is defined as the practice focused on determining that a software works properly in a specific language and in a particular region. Aims. This study aims to explore the particularities of internationalization testing in the software industry and discuss the importance of this practice from the point of view of professionals working in this context. Method. We developed an exploratory qualitative study and conducted interviews with professionals from an international software company, in order to understand three aspects of internationalization testing: general characteristics and importance of this practice, particularities of the process, and the role of test automation in this context. Results. An amount of 13 professionals participated in this study. Results demonstrated that internationalization testing is mostly related to aspects of graphical user interfaces. In this context, truncation and mistranslations are the main faults observed, and test automation might be difficult to implement and maintain due to amount validations that are human-dependent. Conclusion. Internationalization testing is an important practice to guarantee the quality of software products developed for global markets. However, this aspect of software testing remains unpopular or unfamiliar among professionals. This study is a step forward in the process of informing and enlightening academic researchers and practitioners in industry about this theme.

Context. Over the last several decades, computer games started to have a significant impact on society. However, although a computer game is a type of software, the process to conceptualize, produce and deliver a game could involve unusual features. In software testing, for instance, studies demonstrated the hesitance of professionals to use automated testing techniques with games, due to the constant changes in requirements and design, and pointed out the need for creating testing tools that take into account the flexibility required for the game development process. Goal. This study aims to improve the current body of knowledge regarding these theme and point out the existing particularities observed in software testing considering the development of a computer game. Method. A mixed-method approach based on a case study and a survey was applied to collect quantitative and qualitative data from practitioners regarding the particularities of software testing in game development. Results. We analyzed over 70 messages posted on three well-established network of question-and-answer communities and received answers of 38 practitioners, and identified important aspects to be observed in the process of planning, performing and reporting tests games. Conclusion. Considering computer games, software testing must focus not only on the common aspects of a general software, but also, track and investigate issues that could be related to game balance, game physics and entertainment related-aspects to guarantee the quality of computer games and a successful testing process.

Context. Considering the importance of software testing to the development of high quality and reliable software systems, this paper aims to investigate how can work-related factors influence the motivation of software testers. Method. We applied a questionnaire that was developed using a previous theory of motivation and satisfaction of software engineers to conduct a survey-based study to explore and understand how professional software testers perceive and value work-related factors that could influence their motivation at work. Results. With a sample of 80 software testers we observed that software testers are strongly motivated by variety of work, creative tasks, recognition for their work, and activities that allow them to acquire new knowledge, but in general the social impact of this activity has low influence on their motivation. Conclusion. This study discusses the difference of opinions among software testers, regarding work-related factors that could impact their motivation, which can be relevant for managers and leaders in software engineering practice.

Context. Member checking can be defined as a research phase performed during a qualitative research in which the researcher compares her interpretations and understanding obtained from the data analysis with the view-points of participants to increase accuracy and consistency of results. This is an important step for any qualitative research. However, considering a sample of 66 case studies developed and published in the context of software engineering, only 10 studies briefly described the use of this technique. Method. In this article, we present a set of lessons learned obtained from planning and performing member checking to validate the results of an industrial case study performed in a large software company. Results. Member checking was effective to validate the findings obtained from the qualitative case study and was also useful to reveal important information not observed in the data analysis process. It has also shown to be effective to observe divergences among different groups of participants. Conclusion. We described how the member checking can be performed, and discussed seven lessons learned in this process. We expect that our experience can be useful to software engineering researchers while performing this research phase in case studies.

Questions and Answers (Q&A) websites maintain a long history of needs, problems, and challenges that software developers face. In contrast to Q&A websites, which are strongly tied to practitioners' needs, there are systematic reviews (SRs), which, according to recent studies, lack a connection with software engineering practice. In this paper, we investigate this claim by assessing to what extent systematic reviews help to solve questions posted on Q&A websites. To achieve this goal, we propose and evaluate a coverage method. We applied this method to a set of more than 600 questions related to agile software development. Results suggest that 12% of the related questions were covered. When considering specific agile methods, the majority of them have coverage below 50% or were not covered at all. We also identified 27 recurrent questions.

Context. Job Rotation is an organizational practice whereby individuals are regularly moved among jobs or projects in the same organization. Goal: To identify and discuss evidence about job rotation, in order to understand the use, the benefits, and the limitations of this practice in software organizations. Method: A systematic literature review protocol was used to identify and select empirical studies previously published in the software engineering literature, and then coding techniques were used to analyse and synthesize their findings. Results: This review identified 18 empirical papers presenting evidence of 17 distinct studies about job rotation in software engineering. These studies revealed that in software organizations job rotation has been used to enhance communication, organizational understanding, knowledge exchange, and task variety. However, its use also requires extra effort and sometimes complex planning. Conclusion: The research about job rotation in software engineering is restricted, with only one study focusing on this topic and 16 presenting non-intentional evidence about the theme. Our review synthesized evidence that could inform research and practice. However, due to the specific nature of software development tasks and jobs, empirical evidence is still needed to guide the effective application of job rotation in practice.

Context: Work Design refers to the different ways in which a given work or task can be designed and performed. The study of work design is important because every decision related to how the work is performed can affect the outcomes of individuals and the effectiveness of teamwork. Goal: To investigate work design characteristics of software engineering work and identify areas for future research. Method: We performed a survey with professional software engineers working in different commercial software companies in Brazil. We sent invitations to just over 150 professionals and received 77 valid answers from participants working in 35 distinct software companies. We measured 21 work design constructs, as well as job burnout, role conflict, role ambiguity, and two constructs related to job rotation. Results: Comparisons of our sample with other sample consisting of a diversity of professions showed that software engineering work has distinct characteristics, but some results require further investigation. Conclusion: We identified relevant characteristics of software engineering work and areas for further research. In particular, longitudinal studies are needed to address the temporal variations impossible to identify in cross sectional studies.

Job Rotation is an organizational practice in which individuals are frequently moved from a job (or project) to another in the same organization. Studies in other areas have found that this practice has both negative and positive effects on individuals' work. However, there are only few studies addressing this issue in software engineering so far. The goal of our study is to investigate the effects of job rotation on work related factors in software engineering by performing a qualitative case study on a large software organization that uses job rotation as an organizational practice. We interviewed senior managers, project managers, and software engineers that had experienced this practice. Altogether, 48 participants were involved in all phases of this research. Collected data was analyzed using qualitative coding techniques and the results were checked and validated with participants through member checking. Our findings suggest that it is necessary to find balance between the positive effects on work variety and learning opportunities, and negative effects on cognitive workload and performance. Further, the lack of feedback resulting from constant movement among projects and teams may have a negative impact on performance feedback. We conclude that job rotation is an important organizational practice with important positive results. However, managers must be aware of potential negative effects and deploy tactics to balance them. We discuss such tactics in this article.

Context: Current empirical research highlight the need for replications of empirical studies because replications plays an important role in the construction of scientific knowledge. Objective: Considering the importance of replications in the consolidation of the knowledge produced in the software engineering research, this study aims to update and extend the results produced in a previous mapping study seeking to discuss the current state of the replication work of empirical studies performed in software engineering research between 2011 and 2012. Method: We applied the systematic review method to search and select published papers, to extract, and synthesize data from reported replications. Results: This study analyzed more than 7,000 articles, from which 39 articles that published replications between 2011 and 2012 were selected. Data extracted from these studies were used to update the information about the replications work in software engineering. Conclusion: The number of replications increased significantly in the period, when compared to the previous mapping study. In particular, the percentage of external replications also increased, with respect to internal ones. However, several other limitations identified in the previous mapping studies are still observed in this new set of replications.

Context. A recent mapping study intended to verify the current state of replication of empirical studies in Software Engineering (SE) identified two sets of studies: the empirical studies reporting actually replications and a second group of studies that were concerned with concepts, classifications, guidelines, and other themes about replication. Objective. The purpose of this research is to analyze and discuss the content of the second set of studies about replications published between 1996 and 2013. Method. This is a mapping study, in which the primary studies were collected by two previous mapping studies covering the period 1996-2012 complemented by a non-systematic search that collected some articles published in 2013. Results. We analyzed 36 papers reporting empirical and non-empirical studies about replications in SE published in the last 17 years. These papers explored different topics related to concepts and classifications, presented guidelines, and discussed theoretical issues that are relevant for our understanding of replication in our field.

Context. A decade ago, Kitchenham, Dybå and Jørgensen argued that software engineering could benefit from an evidence-based research approach similar that that used in medicine, introducing the basis for Evidence Based Software Engineering (EBSE). Objective. Our main goal is to understand the evolution of the use of systematic reviews as the main research method in EBSE, as proposed by Kitchenham et al., by investigating primary and tertiary studies that explore any aspect, theory, or concept around the use of systematic reviews in software engineering. Method. A systematic mapping study protocol was used to find and selected studies about EBSE and systematic reviews in SE, published between 2004 and 2013. Results. We selected 52 unique papers classified as non-empirical studies (12), empirical studies (31), and tertiary studies (9). Conclusion. SLR has become an important component of software engineering research with nearly 200 unique reviews catalogued by the tertiary studies. Most important limitations are related to the industrial relevance and application of the results of reviews and the poor use of synthesis method to aggregate evidence

Context: Systematic literature reviews (SLRs) are a particular type of secondary study used as the main research method in evidence based research and practice. The starting point of a SLR should be a problem from the practice and the results should, somehow, have an impact on improving practice. Objective: To investigate the motivation of software engineering researchers to conduct a SLR and how the results of their reviews are potentially impacting the software engineering practice. Method: We conducted a cross-sectional survey with authors of 120 SLRs published between 2004 and 2010 identified by three tertiary studies previously published. Results: Forty-four authors of SLR participate in the survey. The motivation behind the vast majority of the SLRs was to gather knowledge about a particular field of study. However, only six participants affirmed that their reviews actually had a direct impact in industrial practice. Conclusions: The use of SLR has increased over the years but they were mostly focused on academic problems and had very little impact on industrial practice. Therefore, the full potential benefits of evidence-based software engineering are not being achieved.