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.

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.

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.

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.

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.

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