Abstract Context Quantum software technologies (QSTs) are emerging as a promising field, offering developers powerful tools to push the boundaries of software innovation. However, a technology that is not adopted remains nothing more than an untapped potential. Despite increasing interest in integrating quantum computing into software development, significant barriers still hinder its widespread adoption. While much of the existing research has focused on technical advancements, the socio-technical factors influencing adoption have been largely overlooked. Understanding these factors could provide managers and engineers with actionable insights to facilitate adoption, as developers’ perceptions and attitudes can be shaped and managed more readily than technological advancements. Objective This study addresses this gap by statistically analyzing the determinants of QST adoption among software professionals. Method Leveraging established adoption theories (UTAUT2 and Diffusion of Innovation) and incorporating perceived trust, data were collected using validated questionnaires and analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). Results Findings indicate that compatibility, personal innovativeness, and performance expectancy are key drivers of adoption, while financial concerns negatively influence it. Moreover, workplace support and habitual exposure emerged as critical enablers of actual usage, emphasizing the importance of organizational interventions. Conclusions These insights offer practical guidance for companies developing and maintaining QSTs into their workflows while identifying new research directions, including barriers to adoption, long-term engagement, and financial considerations.

Abstract Fairness is a critical concern in the integration of machine learning and AI—e.g., Generative AI, LLMs, and Agents—into decision-making, yet the adoption of fairness toolkits by software practitioners remains limited. This gap hinders efforts to operationalize fairness, especially when cultural and ethical values are overlooked. Given that fairness is socially constructed, individual cultural values may significantly influence how practitioners perceive and adopt fairness tools. The objective of this study is, therefore, to investigate whether and how individual cultural values influence software practitioners’ intention to adopt and actual use of fairness toolkits. Specifically, we integrate the UTAUT2 model with Hofstede’s cultural dimensions to examine both direct and moderating cultural effects within the adoption process. A survey of 181 software professionals was conducted, and data were analyzed using Partial Least Squares Structural Equation Modeling. Findings show that cultural values—specifically Power Distance, Collectivism, and Long-Term Orientation—not only directly affect adoption intention and behavior but also moderate key relationships in the adoption process. For example, collectivist individuals were more likely to act on their intention to use fairness tools, highlighting the importance of shared team goals. Overall, our findings indicate that fairness toolkit adoption is shaped not only by technology-related perceptions but also by culturally grounded value orientations. These results provide actionable insights for promoting fairness tool adoption through culturally aware strategies in software development environments.

As a socio-technical activity, software development involves the close interconnection of people and technology. The integration of Large Language Models (LLMs) into this process exemplifies the socio-technical nature of software development. Although LLMs influence the development process, software development remains fundamentally human-centric, necessitating an investigation of the human factors in this adoption. Thus, with this study we explore the factors influencing the adoption of LLMs in software development, focusing on the role of professionals’ cultural values. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT2) and Hofstede’s cultural dimensions, we hypothesized that cultural values moderate the relationships within the UTAUT2 framework. Using Partial Least Squares-Structural Equation Modelling and data from 188 software engineers, we found that habit and performance expectancy are the primary drivers of LLM adoption, while cultural values do not significantly moderate this process. These findings suggest that, by highlighting how LLMs can boost performance and efficiency, organizations can encourage their use, no matter the cultural differences. Practical steps include offering training programs to demonstrate LLM benefits, creating a supportive environment for regular use, and continuously tracking and sharing performance improvements from using LLMs.

Effective software development relies on managing both collaboration and technology, but socio-technical challenges can harm team dynamics and increase technical debt. Although teams working on ML-enabled systems are interdisciplinary, research has largely focused on technical issues, leaving their socio-technical dynamics underexplored. This study aims to address this gap by examining the prevalence, evolution, and interrelations of “community smells”, in open-source ML projects. We conducted an empirical study on 188 repositories from the NICHE dataset using the CADOCS tool to identify and analyze community smells. Our analysis focused on their prevalence, interrelations, and temporal variations. We found that certain smells—such as Prima Donna Effects and Sharing Villainy—are more prevalent and fluctuate over time compared to others like Radio Silence or Organizational Skirmish. These insights might provide valuable support for ML project managers in addressing socio-technical issues and improving team coordination.

Large Language Models (LLMs) have rapidly transformed software development, especially in code generation. However, their inconsistent performance, prone to hallucinations and quality issues, complicates program comprehension and hinders maintainability. Research indicates that prompt engineering—the practice of designing inputs to direct LLMs toward generating relevant outputs—may help address these challenges. In this regard, researchers have introduced prompt patterns, structured templates intended to guide users in formulating their requests. However, the influence of prompt patterns on code quality has yet to be thoroughly investigated. An improved understanding of this relationship would be essential to advancing our collective knowledge on how to effectively use LLMs for code generation, thereby enhancing their understandability in contemporary software development. This paper empirically investigates the impact of prompt patterns on code quality, specifically maintainability, security, and reliability, using the Dev-GPT dataset. Results show that Zero-Shot prompting is most common, followed by Zero-Shot with Chain-of-Thought and Few-Shot. Analysis of 7583 code files across quality metrics revealed minimal issues, with Kruskal-Wallis tests indicating no significant differences among patterns, suggesting that prompt structure may not substantially impact these quality metrics in ChatGPT-assisted code generation.

ABSTRACT Software development is a multifaceted endeavor, requiring a profound grasp of both social dynamics and technical intricacies. Poor collaboration often leads to the accumulation of social debt , manifesting as unforeseen project costs due to sub‐optimal team interactions. Community smells have emerged as indicators of these socio‐technical inefficiencies and potential social debt. While previous research has focused on automated detection of community smells through analyzing developer communication patterns, our study offers a complementary approach. We emphasize the critical role of project managers in assessing socio‐technical dynamics and propose a novel, tool‐supported catalog of symptoms. This catalog can be used for manual inspections to identify early signs of community smells at the individual level, allowing managers to address issues before they escalate. Using a mixed‐method design that leveraged an existing literature review and a user survey, we cataloged symptoms related to four community smell types. Additionally, we developed TOAST, a tool that operationalizes this catalog, and assessed its usability and practical usefulness through an experiment involving project managers. The study showed that even participants unfamiliar with the term “community smells” were able to interpret the tool's output, reflect on team dynamics, and recognize problematic behavioral patterns when supported by structured symptom‐based information. The paper concludes by shedding light on the potential impact of our work and its contribution to advancing the detection and analysis of community smells.

The video game industry, an important sector within software development, relies on multidisciplinary teams to develop products that integrate a variety of media and technical elements. This complexity, coupled with high stress, highlights the importance of emotional intelligence (EI)—the ability to recognize, manage, and utilize emotions effectively. EI is crucial for leadership, team dynamics, and workplace well-being, yet variability in EI within teams can exacerbate workplace challenges. Despite its significance, research on EI remains limited. To address this gap, this study investigates EI across various job roles within the video game industry, examining both horizontal (role-specific) and vertical (hierarchical) differences. A survey using self-rated EI scales was administered to video game professionals. Statistical methods were used to analyze EI differences across roles and hierarchical levels. The analysis showed minimal differences in EI across horizontal roles. However, leaders exhibited higher scores in dimensions, such as managing one’s own emotions, indicating that EI is more pronounced in leadership roles. The findings suggest that, while the EI varies little by role, it is particularly relevant to leaders. Such a disparity between leaders and team members should not be taken lightly, as it could create an “emotional hierarchy,” potentially adding stress to leadership positions.

Successful software development hinges on effective communication and collaboration, which are significantly influenced by human and social dynamics. Poor management of these elements can lead to the emergence of ‘community smells’, i.e., negative patterns in socio-technical interactions that gradually accumulate as ‘social debt’. This issue is particularly pertinent in machine learning-enabled systems, where diverse actors such as data engineers and software engineers interact at various levels. The unique collaboration context of these systems presents an ideal setting to investigate community smells and their impact on development communities. This article addresses a gap in the literature by identifying the types, causes, effects, and potential mitigation strategies of community smells in machine learning-enabled systems. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), we developed hypotheses based on existing literature and interviews, and conducted a questionnaire-based study to collect data. Our analysis resulted in the construction and validation of five models that represent the causes, effects, and strategies for five specific community smells. These models can help practitioners identify and address community smells within their organizations, while also providing valuable insights for future research on the socio-technical aspects of machine learning-enabled system communities.

Software development is essentially a collaborative, socio-technical endeavor where the interplay between stakeholders and technical elements is integral. This synergy becomes particularly crucial in the context of geographically dispersed teams, a practice that is becoming more prevalent. Despite the ubiquity of this nature, the current body of research in Global Software Development (GSD) encounters limitations, rendering the attained results less accessible for practical implementation by industry professionals. Moreover, the role of social debt, the additional cost derived by adopting socio-technical anti-patterns, in GSD still needs to be deepened. This Ph.D. research project aims to surmount these challenges by constructing a robust theoretical foundation for effectively managing socio-technical aspects—particularly in the form of factors related to social debt—in software development, with a keen focus on their correlation with cultural differences within software teams. The framework systematically captures and examines cultural differences, investigating their ramifications on various facets of software development while exploring practical strategies employed by practitioners to navigate these influences. Furthermore, the project aspires to make substantial contributions to the professional software development realm by translating research findings into tangible tools for practitioners. This framework is designed not only for immediate application but also to facilitate project success through heightened cultural awareness and adaptability. Ultimately, it strives to enhance the well-being of developers working in inclusive and culturally diverse environments.

To provide a measurable representation of social issues in software teams, the research community defined a set of anti-patterns that may lead to the emergence of both social and technical debt, i.e., “community smells”. Researchers have investigated community smells from different perspectives; in particular, they have analyzed how product-related aspects of software development, such as architecture and introducing a new language, could influence community smells. However, how technical project characteristics may be in relation to the emergence of community smells is still unknown. Different from those works, we aim to investigate how adopting specific programming languages might influence the socio-technical alignment and congruence of the development community, possibly inducing their overall ability to communicate and collaborate, leading to the emergence of social anti-patterns, i.e., community smells. We studied the relationship between the most used programming languages and the community smells in 100 open-source projects on G ITHub. Key results of the study show a low statistical correlation for specific community smells like Prima Donna Effects, Solution Defiance, and Organizational Skirmish, highlighting the fact that for some programming languages, its adoption could not be an indicator of the presence or absence of community smells.

The term metaverse refers to a persistent, virtual, three-dimensional environment where individuals may communicate, engage, and collaborate. One of the most multifaceted and challenging use cases of the metaverse is education, where educators and learners may require multiple technical, social, psychological, and interaction instruments to accomplish their learning objectives. While the characteristics of the metaverse might nicely fit the problem’s needs, our research points out a noticeable lack of knowledge into (1) the specific requirements that an educational metaverse should actually fulfill to let educators and learners successfully interact towards their objectives and (2) how to design an appropriate educational metaverse for both educators and learners. In this paper, we aim to bridge this knowledge gap by proposing SENEM, a novel software engineering-enabled educational metaverse. We first elicit a set of functional requirements that an educational metaverse should fulfill. In this respect, we conduct a literature survey to extract the currently available knowledge on the matter discussed by the research community, and afterward, we assess and complement such knowledge through semi-structured interviews with educators and learners. Upon completing the requirements elicitation stage, we then build our prototype implementation of SENEM, a metaverse that makes available to educators and learners the features identified in the previous stage. Finally, we evaluate the tool in terms of learnability, efficiency, and satisfaction through a Rapid Iterative Testing and Evaluation research approach, leading us to the iterative refinement of our prototype. Through our survey strategy, we extracted nine requirements that guided the tool development that the study participants positively evaluated. Our study reveals that the target audience appreciates the elicited design strategy. Our work has the potential to form a solid contribution that other researchers can use as a basis for further improvements.

Estimating and understanding software development productivity represent crucial tasks for researchers and practitioners. Although different works focused on evaluating the impact of human factors on productivity, a few explored the influence of cultural/geographical diversity in software development communities. More particularly, all previous treatise addresses cultural aspects as abstract concepts without providing a quantitative representation. Improved knowledge of these matters might help project managers to assemble more productive teams and tool vendors to design software analytics toolkits that may better estimate productivity. This paper has the goal of enlarging the existing body of knowledge on the factors affecting productivity by focusing on cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it. To reach this goal, we performed a mixed-method empirical study. First, we built a statistical model relating dispersion metrics with the productivity of 25 open-source communities on Github. Then, we performed a confirmatory survey with 140 practitioners. The key results of our study indicate that cultural and geographical dispersion considerably impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle. We conclude our paper by elaborating on the main insights from our analyses and instilling implications that may drive further research.

Modern software systems are increasingly complex and the risk of falling into security concerns is high if these systems are not developed with a proper security mindset. Despite the empirical studies and security-oriented approaches proposed by researchers and tool vendors, we still point out a lack of knowledge on the security testing processes applied by companies to reduce risks connected to software security. In this paper, we aim to bridge this gap of knowledge by performing an interview-based study with 19 security experts to understand how companies arrange security testing and how the process of security testing is actually performed in practice. Our results highlight that some companies incorporated the figure of the security tester in the software life cycle, yet practitioners reported a lack of standardized guidelines for security testing. From a management perspective, our results suggest that the introduction of formal communication between development and security testing teams may lead to better performance.

The software industry’s complexity and scale have increased rapidly, leading to challenges in managing information and tasks among developer teams, often resulting in inefficiencies, misunderstandings, and delays. The extensive search for automated tasks led to using chatbots—conversational agents—in software development. However, despite their positive contributions, their adoption has numerous issues, notably the lack of full working context, making their support sometimes useless. To address such a limitation, we propose C4SE, a chatbot designed to assist software engineers and managers in performing various tasks by gathering information helpful for better support. We use the GPT 3.5 model, and a specialized data store based on a vector database for long-term memory, to understand users’ intentions and maintain contextual information. Our prototype C4SE can perform code suggestions, reviews, GitHub API operations, and generate unit and acceptance test cases. Preliminary evaluation reports encouraging results, showing potential to increase productivity in the software development lifecycle.

Estimating and understanding productivity still represents a crucial task for researchers and practitioners. Researchers spent significant effort identifying the factors that influence software developers’ productivity, providing several approaches for analyzing and predicting such a metric. Although different works focused on evaluating the impact of human factors on productivity, little is known about the influence of cultural/geographical diversity in software development communities. Indeed, in previous studies, researchers treated cultural aspects like an abstract concept without providing a quantitative representation. This work provides an empirical assessment of the relationship between cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it—and its productivity. To reach our aim, we built a statistical model that contained product and socio-technical factors as independent variables to assess the correlation with productivity, i.e., the number of commits performed in a given time. Then, we ran our model considering data of 25 open-source communities on GitHub. Results of our study indicate that cultural and geographical dispersion impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle.

Software engineering is a human-centered activity involving various stakeholders with different backgrounds that have to communicate and collaborate to reach shared objectives. The emergence of conflicts among stakeholders may lead to undesired effects on software maintainability, yet it is often unavoidable in the long run. Community smells, i.e., sub-optimal communication and collaboration practices, have been defined to map recurrent conflicts among developers. While some community smell detection tools have been proposed in the recent past, these can be mainly used for research purposes because of their limited level of usability and user engagement. To facilitate a wider use of community smell-related information by practitioners, we present CADOCS, a client-server conversational agent that builds on top of a previous community smell detection tool proposed by Almarini et al. to (1) make it usable within a well-established communication channel like Slack and (2) augment it by providing initial support to software analytics instruments useful to diagnose and refactor community smells. We describe the features of the tool and the preliminary evaluation conducted to assess and improve robustness and usability.

Test smells represent sub-optimal design or implementation solutions applied when developing test cases. Previous research has shown that these smells may decrease both maintainability and effectiveness of tests and, as such, researchers have been devising methods to automatically detect them. Nevertheless, there is still a lack of tools that developers can use within their integrated development environment to identify test smells and refactor them. In this paper, we present DARTS (Detection And Refactoring of Test Smells), an Intellij plug-in which (1) implements a state-of-the-art detection mechanism to detect instances of three test smell types, i.e., General Fixture, Eager Test, and Lack of Cohesion of Test Methods, at commit-level and (2) enables their automated refactoring through the integrated APIs provided by Intellij.