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.

The COVID-19 pandemic triggered an unprecedented transformation in the educational landscape, requiring universities to swiftly pivot from in-person to online instruction. This rapid transition left many educators navigating the complexities of remote teaching for the first time. Now that we have moved past the pandemic, we present a critical retrospective study to analyze and assess the remote teaching practices employed during this challenging period. By conducting a cross-sectional analysis of 300 computer science students who experienced a full year of online education during the lockdown, we discovered that while remote teaching practices had a moderate impact on learning outcomes, they significantly influenced student satisfaction. Importantly, these trends were not isolated; they reflect a shared experience across various demographics, including country, gender, and educational background. This research delivers vital evidence-based recommendations that can guide educational strategies in the event of future challenges. By applying these insights, we can enhance both student satisfaction and the effectiveness of learning in online settings, ensuring that we are better prepared for whatever lies ahead. • Investigation of the impact of remote teaching practices on computer science students during COVID-19. • Remote teaching practices moderately influenced learning outcomes and significantly enhanced satisfaction. • Consistent effects were found across demographics, including country, gender, and educational level. • Comprehensive analysis of teaching practices using PLS-SEM with data from 300 students. • Offers actionable recommendations to guide future hybrid and remote teaching strategies.

As GenAI becomes embedded in developer toolchains and practices, and routine code is increasingly generated, human creativity will be increasingly important for generating competitive advantage. This article uses the McLuhan tetrad alongside scenarios of how GenAI may disrupt software development more broadly, to identify potential impacts GenAI may have on creativity within software development. The impacts are discussed along with a future research agenda comprising five connected themes that consider how individual capabilities, team capabilities, the product, unintended consequences, and society can be affected.

Abstract With the prevalent use of Agile methodologies, organizations are grappling with the challenge of scaling development across numerous teams. This has led to the emergence of diverse scaling strategies, from complex ones such as “SAFe", to more simplified methods e.g., “LeSS", with some organizations devising their unique approaches. While there have been multiple studies exploring the organizational challenges associated with different scaling approaches, so far, no one has compared these strategies based on empirical data derived from a uniform measure. This makes it hard to draw robust conclusions about how different scaling approaches affect Agile team effectiveness. Thus, the objective of this study is to assess the effectiveness of Agile teams across various scaling approaches, including “SAFe", “LeSS", “Scrum of Scrums", and custom methods, as well as those not using scaling. This study focuses initially on responsiveness, stakeholder concern, continuous improvement, team autonomy, management approach, and overall team effectiveness, followed by an evaluation based on stakeholder satisfaction regarding value, responsiveness, and release frequency. To achieve this, we performed a comprehensive survey involving 15,078 members of 4,013 Agile teams to measure their effectiveness, combined with satisfaction surveys from 1,841 stakeholders of 529 of those teams. We conducted a series of inferential statistical analyses, including Analysis of Variance and multiple linear regression, to identify any significant differences, while controlling for team experience and organizational size. The findings of the study revealed some significant differences, but their magnitude and effect size were considered too negligible to have practical significance. In conclusion, the choice of Agile scaling strategy does not markedly influence team effectiveness, and organizations are advised to choose a method that best aligns with their previous experiences with Agile, organizational culture, and management style.

This paper presents an analysis of different interaction techniques used in interactive data visualisations to support end-users in visual analytics tasks. Our selection of interaction techniques is based on prior work and consists of the interaction techniques SELECT, EXPLORE, RECONFIGURE, ENCODE, FILTER, ABSTRACT/ELABORATE, and CONNECT. Through a within-subject study, we assessed participants’ abilities to utilise these techniques when faced with three distinct types of data-driven tasks; lookup, comparison, and Relation-seeking. Our research investigates the impact of these interaction techniques on the correctness, confidence, perceived difficulty, and cognitive load of N = 80 self-identified data scientists and N = 80 non-experts. We find that interaction technique significantly impacts answer correctness and participant confidence. Participants performed best across those interaction techniques that allow for information that is deemed least relevant to be concealed, which is reflected in lower intrinsic and extraneous cognitive load. Interestingly, participants’ expertise affected their confidence but not their accuracy. Our results provide insights useful for a more targeted and informed design and usage of interactive data visualisations.

This article explores the adoption of Generative Artificial Intelligence (AI) tools within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a questionnaire survey with 100 software engineers, drawing upon the Technology Acceptance Model, the Diffusion of Innovation Theory, and the Social Cognitive Theory as guiding theoretical frameworks. Employing the Gioia methodology, we derived a theoretical model of AI adoption in software engineering: the Human-AI Collaboration and Adaptation Framework. This model was then validated using Partial Least Squares–Structural Equation Modeling based on data from 183 software engineers. Findings indicate that at this early stage of AI integration, the compatibility of AI tools within existing development workflows predominantly drives their adoption, challenging conventional technology acceptance theories. The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected. The study provides crucial insights for future AI tool design and offers a framework for developing effective organizational implementation strategies.

This Replicated Computational Results (RCR) report complements the study “Navigating the Complexity of Generative AI Adoption in Software Engineering,” which examines the factors influencing the integration of AI tools in software engineering practices. Employing a mixed-methods approach grounded in the Technology Acceptance Model, Diffusion of Innovation Theory, and Social Cognitive Theory, the study introduces the Human-AI Collaboration and Adaptation Framework (HACAF), validated through PLS-SEM analysis. The replication package detailed herein includes survey instruments, raw data, and analysis scripts essential for reproducing the study's findings. By providing these artifacts, the RCR report aims to support transparency, enable replication, and encourage further research on effective AI tool adoption strategies in software engineering.

The COVID-19 pandemic has brought significant and enduring shifts in various aspects of life, including increased flexibility in work arrangements. In a longitudinal study, spanning 24 months with six measurement points from April 2020 to April 2022, we explore changes in well-being, productivity, social contacts, and needs of software engineers during this time. Our findings indicate systematic changes in various variables. For example, well-being and quality of social contacts increased while emotional loneliness decreased as lockdown measures were relaxed. Conversely, people’s boredom and productivity remained stable. Furthermore, a preliminary investigation into the future of work at the end of the pandemic revealed a consensus among developers for a preference of hybrid work arrangements. We also discovered that prior job changes and low job satisfaction were consistently linked to intentions to change jobs if current work conditions do not meet developers’ needs. This highlights the need for software organizations to adapt to various work arrangements to remain competitive employers. Building upon our findings and the existing literature, we introduce the Integrated Job Demands-Resources and Self-Determination (IJARS) Model as a comprehensive framework to explain the well-being and productivity of software engineers during the COVID-19 pandemic.

The artifact accompanying the paper “Understanding Developers Well-Being and Productivity: A 2-year Longitudinal Analysis during the COVID-19 Pandemic” provides a comprehensive set of tools, data, and scripts that were utilized in the longitudinal study. Spanning 24 months, from April 2020 to April 2022, the study delves into the shifts in well-being, productivity, social contacts, needs, and several other variables of software engineers during the COVID-19 pandemic. The artifact facilitates the reproduction of the study’s findings, offering a deeper insight into the systematic changes observed in various variables, such as well-being, quality of social contacts, and emotional loneliness. By providing access to the evidence-generating mechanisms and the generated data, the artifact ensures transparency and reproducibility and allows researchers to use our rich dataset to test their own research question. This Replicated Computational Results report aims to detail the contents of the artifact, its relevance to the main paper, and guidelines for its effective utilization.

As researchers today, we are witnessing a fundamental change in our technologically-enabled world due to the advent and diffusion of highly disruptive technologies such as generative Artificial Intelligence (AI), Augmented Reality (AR) and Virtual Reality (VR). In particular, software engineering has been profoundly affected by the transformative power of disruptive innovations for decades, with a significant impact of technical advancements on social dynamics due to its socio-technical nature. In this article, we reflect on the importance of formulating and addressing research problems in software engineering through a socio-technical lens, thus ensuring a holistic understanding of the complex phenomena in this field. We propose a research playbook with the aim of providing a guide to formulate compelling and socially relevant research questions and to identify the appropriate research strategies for empirical investigations, with an eye on the long-term implications of technologies or their use. We showcase how to apply the research playbook. Firstly, we show how it can be used retrospectively to reflect on a prior disruptive technology, Stack Overflow, and its impact on software development. Secondly, we show how it can be used to question the impact of two current disruptive technologies: AI and AR/VR. Finally, we introduce a specialized GPT model to support the researcher in framing future investigations. We conclude by discussing the broader implications of adopting the playbook for both researchers and practitioners in software engineering and beyond.

Team diversity can be seen as a double-edged sword. It brings additional cognitive resources to teams at the risk of increased conflict. Few studies have investigated how different types of diversity impact software teams. This study views diversity through the lens of the categorization-elaboration model (CEM). We investigated how diversity in gender, age, role, and cultural background impacts team effectiveness and conflict, and how these associations are moderated by psychological safety. Our sample consisted of 1,118 participants from 161 teams and was analysed with Covariance-Based Structural Equation Modeling (CB-SEM). We found a positive effect of age diversity on team effectiveness and gender diversity on relational conflict. Psychological safety contributed directly to effective teamwork and less conflict but did not moderate the diversity-effectiveness link. While our results are consistent with the CEM theory for age and gender diversity, other types of diversity did not yield similar results. We discuss several reasons for this, including curvilinear effects, moderators such as task interdependence, or the presence of a diversity mindset. With this paper, we argue that a dichotomous nature of diversity is oversimplified. Indeed, it is a complex relationship where context plays a pivotal role. A more nuanced understanding of diversity through the lens of theories, such as the CEM, may lead to more effective teamwork.

The paper we present [1] is an analysis of Cooperative Thinking, a model of team-based computational problem-solving that extends Computational Thinking with Agile Values.

Following the onset of the COVID-19 pandemic and subsequent lockdowns, the daily lives of software engineers were heavily disrupted as they were abruptly forced to work remotely from home. To better understand and contrast typical working days in this new reality with work in pre-pandemic times, we conducted one exploratory ( N = 192) and one confirmatory study ( N = 290) with software engineers recruited remotely. Specifically, we build on self-determination theory to evaluate whether and how specific activities are associated with software engineers’ satisfaction and productivity. To explore the subject domain, we first ran a two-wave longitudinal study. We found that the time software engineers spent on specific activities (e.g., coding, bugfixing, helping others) while working from home was similar to pre-pandemic times. Also, the amount of time developers spent on each activity was unrelated to their general well-being, perceived productivity, and other variables such as basic needs. Our confirmatory study found that activity-specific variables (e.g., how much autonomy software engineers had during coding) do predict activity satisfaction and productivity but not by activity-independent variables such as general resilience or a good work-life balance. Interestingly, we found that satisfaction and autonomy were significantly higher when software engineers were helping others and lower when they were bugfixing. Finally, we discuss implications for software engineers, management, and researchers. In particular, active company policies to support developers’ need for autonomy, relatedness, and competence appear particularly effective in a WFH context.

With the COVID-19 pandemic, Scrum teams had to switch abruptly from a traditional working setting into an enforced working from home one. This abrupt switch had an impact on software projects. Thus, it is necessary to understand how potential future disruptive events will impact Agile software teams' ability to deliver successful projects while working from home. To investigate this problem, we used a two-phased Multi-Method study. In the first phase, we uncover how working from home impacted Scrum practitioners through semi-structured interviews. Then, in the second phase, we propose a theoretical model that we test and generalize using Partial Least Squares-Structural Equation Modeling (PLS-SEM) surveying 138 software engineers who worked from home within Scrum projects. We concluded that all the latent variables identified in our model are reliable, and all the hypotheses are significant. This paper emphasizes the importance of supporting the three innate psychological needs of autonomy, competence, and relatedness in the home working environment. We conclude that the ability of working from home and the use of Scrum both contribute to project success, with Scrum acting as a mediator.

Scrum teams are at the heart of the Scrum framework. Nevertheless, an integrated and systemic theory that can explain what makes some Scrum teams more effective than others is still missing. To address this gap, we performed a 7-year-long mixed-methods investigation composed of two main phases. First, we induced a theoretical model from 13 exploratory field studies. Our model proposes that the effectiveness of Scrum teams depends on five high-level factors (responsiveness, stakeholder concern, continuous improvement, team autonomy, and management support) and 13 lower-level factors. In the second phase of our study, we validated our model with a covariance-based structural equation modeling analysis using data from about 5,000 developers and 2,000 Scrum teams that we gathered with a custom-built survey. Results suggest a very good fit of the empirical data in our theoretical model ( CFI = 0.959, RMSEA = 0.038, SRMR = 0.035). Accordingly, this research allowed us to (1) propose and validate a generalizable theory for effective Scrum teams and (2) formulate clear recommendations for how organizations can better support Scrum teams.

There is considerable anecdotal evidence suggesting that software engineers enjoy engaging in solving puzzles and other cognitive efforts. A tendency to engage in and enjoy effortful thinking is referred to as a person's 'need for cognition.' In this article we study the relationship between software engineers' personality traits and their need for cognition. Through a large-scale sample study of 483 respondents we collected data to capture the six 'bright' personality traits of the HEXACO model of personality, and three 'dark' personality traits. Data were analyzed using several methods including a multiple Bayesian linear regression analysis. The results indicate that ca. 33% of variation in developers' need for cognition can be explained by personality traits. The Bayesian analysis suggests four traits to be of particular interest in predicting need for cognition: openness to experience, conscientiousness, honesty-humility, and emotionality. Further, we also find that need for cognition of software engineers is, on average, higher than in the general population, based on a comparison with prior studies. Given the importance of human factors for software engineers' performance in general, and problem solving skills in particular, our findings suggest several implications for recruitment, working behavior, and teaming.

There is a growing body of gender studies in software engineering to understand diversity and inclusion issues, as diversity is recognized to be a key issue to healthy teams and communities. A second factor often linked to team performance is personality, which has received far more attention. Very few studies, however, have focused on the intersection of these two fields. Hence, we set out to study gender differences in personality traits of software engineers. Through a survey study we collected personality data, using the HEXACO model, of 483 software engineers. The data were analyzed using a Bayesian independent sample t-test and network analysis. The results suggest that women score significantly higher in Openness to Experience, Honesty-Humility, and Emotionality than men. Further, men show higher psychopathic traits than women. Based on these findings, we develop a number of propositions that can guide future research.

The uptake of artificial intelligence-based applications raises concerns about the fairness and transparency of AI behaviour. Consequently, the Computer Science community calls for the involvement of the general public in the design and evaluation of AI systems. Assessing the fairness of individual predictors is an essential step in the development of equitable algorithms. In this study, we evaluate the effect of two common visualisation techniques (text-based and scatterplot) and the display of the outcome information (i.e., ground-truth) on the perceived fairness of predictors. Our results from an online crowdsourcing study (N = 80) show that the chosen visualisation technique significantly alters people’s fairness perception and that the presented scenario, as well as the participant’s gender and past education, influence perceived fairness. Based on these results we draw recommendations for future work that seeks to involve non-experts in AI fairness evaluations.

The COVID-19 pandemic has forced governments worldwide to impose movement restrictions on their citizens. Although critical to reducing the virus’ reproduction rate, these restrictions come with far-reaching social and economic consequences. In this paper, we investigate the impact of these restrictions on an individual level among software engineers who were working from home. Although software professionals are accustomed to working with digital tools, but not all of them remotely, in their day-to-day work, the abrupt and enforced work-from-home context has resulted in an unprecedented scenario for the software engineering community. In a two-wave longitudinal study ( N = 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity. Examples include anxiety, distractions, coping strategies, psychological and physical needs, office set-up, stress, and work motivation. This design allowed us to identify the variables that explained unique variance in well-being and productivity. Results include (1) the quality of social contacts predicted positively, and stress predicted an individual’s well-being negatively when controlling for other variables consistently across both waves; (2) boredom and distractions predicted productivity negatively; (3) productivity was less strongly associated with all predictor variables at time two compared to time one, suggesting that software engineers adapted to the lockdown situation over time; and (4) longitudinal analyses did not provide evidence that any predictor variable causal explained variance in well-being and productivity. Overall, we conclude that working from home was per se not a significant challenge for software engineers. Finally, our study can assess the effectiveness of current work-from-home and general well-being and productivity support guidelines and provides tailored insights for software professionals.

Organizations are increasingly adopting Agile frameworks for their internal software development. Cost reduction, rapid deployment, requirements and mental model alignment are typical reasons for an Agile transformation. This article presents an in-depth field study of a large-scale Agile transformation in a mission-critical environment, where stakeholders’ commitment was a critical success factor. The goal of such a transformation was to implement mission-oriented features, reducing costs and time to operate in critical scenarios. The project lasted several years and involved over 40 professionals. We report how a hierarchical and plan-driven organization exploited Agile methods to develop a Command & Control (C2) system. Accordingly, we first abstract our experience, inducing a success model of general use for other comparable organizations by performing a post-mortem study. The goal of the inductive research process was to identify critical success factors and their relations. Finally, we validated and generalized our model through Partial Least Squares - Structural Equation Modelling, surveying 200 software engineers involved in similar projects. We conclude the article with data-driven recommendations concerning the management of Agile projects.

Computational Thinking (CT) and Agile Values (AV) focus respectively on the individual capability to think algorithmically, and on the principles of collaborative software development. Although these two dimensions of software engineering education complement each other, very few studies explored their interaction. In this paper we use an exploratory Structural Equation Modeling technique to introduce and analyze Cooperative Thinking (CooT), a model of team-based computational problem solving. We ground our model on the existing literature and validate it through Partial Least Square modeling. Cooperative Thinking is new competence which aim is to support cooperative problem solving of technical contents suitable to deal with complex software engineering problems. This article suggests to tackle the CooT construct as an education goal, to train students of software development to improve both their individual and teaming performances.

Developing high quality software is a major industry concern, since programs that "just work" may not be suitable to contemporary technological challenges. Agile practices, such as Test-First development (TFD), may help in this direction. However, in our experience this technique is introduced late (if ever), when programmers' habits are already set and difficult to change. Early exposure to TFD in formal education could be an answer to that, but putting the principle into practice poses unexpected challenges. In this work we examine the short-and long-term impact of young programmers' exposure to TFD, highlighting its limits and proposing a reinforced teaching approach.

In this paper, we propose the Computational Thinking concept, which is obtained by enhancing by merging the values of Computational Thinking and Agile. We analyze four existing teaching models for training Cooperative Thinkers, supported by experimental data, and propose an educational path that can promote the early development of this complex skill.