Rashina Hoda

Aldeida Aleti, Baishakhi Ray, Rashina Hoda et al.

With the rapid rise of AI coding agents, the fundamental premise of what it means to be a software engineer is in question. In this vision paper, we examine what it means for an AI agent to be considered a software engineer and then critically think about what makes such an agent trustworthy. Grounded in established definitions of SE (SE) and informed by recent research on agentic AI systems, we conceptualise AI software engineers as participants in human-AI SE teams composed of human software engineers and AI agents, and we distinguish trustworthiness as a key property of these systems and actors rather than a subjective human attitude. Extending on historical perspectives and emerging visions, we identify key dimensions that contribute to the trustworthiness of AI software engineers, spanning technical quality, transparency and accountability, epistemic humility, and societal and ethical alignment. Beyond defining these dimensions, we address a critical but underexplored challenge: how trustworthiness can be operationalised in practice. We therefore introduce the notion of evidence-centric inspection, arguing that developers should evaluate selective signals and justifications of trustworthiness rather than raw outputs, and we outline implications for rethinking verification, validation, and code review in human-AI SE teams.

Aastha Pant, Rashina Hoda, Simone V. Spiegler et al.

Ethics in AI has become a debated topic of public and expert discourse in recent years. But what do people who build AI - AI practitioners - have to say about their understanding of AI ethics and the challenges associated with incorporating it in the AI-based systems they develop? Understanding AI practitioners' views on AI ethics is important as they are the ones closest to the AI systems and can bring about changes and improvements. We conducted a survey aimed at understanding AI practitioners' awareness of AI ethics and their challenges in incorporating ethics. Based on 100 AI practitioners' responses, our findings indicate that majority of AI practitioners had a reasonable familiarity with the concept of AI ethics, primarily due to workplace rules and policies. Privacy protection and security was the ethical principle that majority of them were aware of. Formal education/training was considered somewhat helpful in preparing practitioners to incorporate AI ethics. The challenges that AI practitioners faced in the development of ethical AI-based systems included (i) general challenges, (ii) technology-related challenges and (iii) human-related challenges. We also identified areas needing further investigation and provided recommendations to assist AI practitioners and companies in incorporating ethics into AI development.

Mark Swillus, Rashina Hoda, Andy Zaidman

Software testing is crucial for ensuring software quality, yet developers' engagement with it varies widely. Identifying the technical, organizational and social factors that lead to differences in engagement is required to remove barriers and utilize enablers for testing. While much research emphasizes the usefulness of software testing approaches and technical solutions, less is known about why developers do (not) test. This study investigates the first-hand experience of developers with software testing. The study illuminates how developers' opinions about testing and their testing behavior changes. Through analysis of personal evolutions of practice, we explore when and why testing is used. Employing socio-technical grounded theory (STGT), we construct a theory by systematically analyzing data from 19 in-depth, semi-structured interviews with software developers. Allowing interviewees to reflect on how and why they approach software testing, we explore perspectives that are rooted in their contextual experiences. We develop eleven categories of circumstances that act as conditions for the application and adaptation of testing practices and introduce three concepts that we then use to present a theory of emerging testing strategies (ETS) that explains why developers do (not) use testing practices. This study reveals a new perspective on the connection between testing artifacts and collective reflection of practitioners, and it embraces. It has direct implications for practice %and contributes to the groundwork of socio-technical research which embraces testing as an experience in which human- and social aspects are entangled with organizational and technical circumstances.

Samuel Ferino, Rashina Hoda, John Grundy et al.

How software developers interact with Artificial Intelligence (AI)-powered tools, including Large Language Models (LLMs), plays a vital role in how these AI-powered tools impact them. While overreliance on AI may lead to long-term negative consequences (e.g., atrophy of critical thinking skills); underreliance might deprive software developers of potential gains in productivity and quality. Based on twenty-two interviews with software developers on using LLMs for software development, we propose a preliminary reliance-control framework where the level of control can be used as a way to identify AI overreliance and underreliance. We also use it to recommend future research to further explore the different control levels supported by the current and emergent LLM-driven tools. Our paper contributes to the emerging discourse on AI overreliance and provides an understanding of the appropriate degree of reliance as essential to developers making the most of these powerful technologies. Our findings can help practitioners, educators, and policymakers promote responsible and effective use of AI tools.

Christoph Treude, Christopher M. Poskitt, Rashina Hoda

Peer review in software engineering research operates under tight time constraints, while generative AI has substantially reduced the human effort required to produce polished research narratives. Reviewer attention is often spent on aspects of submissions such as writing quality or literature positioning that have become relatively less effort-intensive to address, rather than on evaluating the scientific substance of a paper. At the same time, assessing whether methods are implemented correctly, analyses are sound, and claims are supported by evidence remains effort-intensive and dependent on human expertise. In software engineering research, this substance is frequently embodied in artifacts, including code, data, evidence and analysis samples, and experimental infrastructure. In this position paper, we argue that artifact evaluation should be treated as a first-class component of peer review. We frame peer review as an attention allocation problem, examine how generative AI weakens narrative quality as a signal of rigor, and argue that artifact evaluation should play a more prominent role in peer review decisions.

Samuel Ferino, Rashina Hoda, John Grundy et al.

Background: Large Language Models emerged with the potential of provoking a revolution in software development (e.g., automating processes, workforce transformation). Although studies have started to investigate the perceived impact of LLMs for software development, there is a need for empirical studies to comprehend how to balance forward and backward effects of using LLMs. Objective: We investigated how LLMs impact software development and how to manage the impact from a software developer's perspective. Method: We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis, between October (2024) and September (2025). We employed socio-technical grounded theory (STGT) for data analysis to rigorously analyse interview participants' responses. Results: We identified the benefits (e.g., maintain software development flow, improve developers' mental model, and foster entrepreneurship) and disadvantages (e.g., negative impact on developers' personality and damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as best practices on how to adopt LLMs. Conclusion: Critically, we present the trade-offs that software practitioners, teams, and organisations face in working with LLMs. Our findings are particularly useful for software team leaders and IT managers to assess the viability of LLMs within their specific context.

Samuel Ferino, Rashina Hoda, John Grundy et al.

Following the rise of large language models (LLMs), many studies have emerged in recent years focusing on exploring the adoption of LLM-based tools for software development by novice developers: computer science/software engineering students and early-career industry developers with two years or less of professional experience. These studies have sought to understand the perspectives of novice developers on using these tools, a critical aspect of the successful adoption of LLMs in software engineering. To systematically collect and summarise these studies, we conducted a systematic literature review (SLR) following the guidelines by Kitchenham et al. on 80 primary studies published between April 2022 and June 2025 to answer four research questions (RQs). In answering RQ1, we categorised the study motivations and methodological approaches. In RQ2, we identified the software development tasks for which novice developers use LLMs. In RQ3, we categorised the advantages, challenges, and recommendations discussed in the studies. Finally, we discuss the study limitations and future research needs suggested in the primary studies in answering RQ4. Throughout the paper, we also indicate directions for future work and implications for software engineering researchers, educators, and developers. Our research artifacts are publicly available at https://github.com/Samuellucas97/SupplementaryInfoPackage-SLR.

Wei Zhou, Rashina Hoda, Joycelyn Ling

AI applications are increasingly being introduced into digital health. While technical performance has advanced rapidly, successful deployment mainly depends on consumer attitudes, especially to patient-facing applications. However, most existing research examines consumer attitudes towards healthcare AI at an abstract level rather than in response to concrete artefacts. We report a mixed-methods survey study in Australia (N=275) examining consumer readiness, acceptance, trust, and risk perceptions of healthcare AI, combined with a scenario-based evaluation of an AI-generated versus clinician-written consultation summary. Participants expressed moderate optimism and strong perceived usefulness and ease of use, but also substantial concerns about accuracy, safety, and data use. In the scenario task, the AI-generated summary was strongly preferred for quality, empathy, and overall usefulness, yet identification of the AI summary was near chance. Findings show that consumers judge AI through concrete communication quality and visible human governance, underscoring the need for clinically supervised deployment frameworks beyond technical performance alone.

Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn et al.

The rise in the use of AI/ML applications across industries has sparked more discussions about the fairness of AI/ML in recent times. While prior research on the fairness of AI/ML exists, there is a lack of empirical studies focused on understanding the perspectives and experiences of AI practitioners in developing a fair AI/ML system. Understanding AI practitioners' perspectives and experiences on the fairness of AI/ML systems are important because they are directly involved in its development and deployment and their insights can offer valuable real-world perspectives on the challenges associated with ensuring fairness in AI/ML systems. We conducted semi-structured interviews with 22 AI practitioners to investigate their understanding of what a 'fair AI/ML' is, the challenges they face in developing a fair AI/ML system, the consequences of developing an unfair AI/ML system, and the strategies they employ to ensure AI/ML system fairness. We developed a framework showcasing the relationship between AI practitioners' understanding of 'fair AI/ML' system and (i) their challenges in its development, (ii) the consequences of developing an unfair AI/ML system, and (iii) strategies used to ensure AI/ML system fairness. By exploring AI practitioners' perspectives and experiences, this study provides actionable insights to enhance AI/ML fairness, which may promote fairer systems, reduce bias, and foster public trust in AI technologies. Additionally, we also identify areas for further investigation and offer recommendations to aid AI practitioners and AI companies in navigating fairness.

Andy Li, Wei Zhou, Rashina Hoda et al.

This study evaluates how well large language models (LLMs) and traditional machine translation (MT) tools translate medical consultation summaries from English into Arabic, Chinese, and Vietnamese. It assesses both patient, friendly and clinician, focused texts using standard automated metrics. Results showed that traditional MT tools generally performed better, especially for complex texts, while LLMs showed promise, particularly in Vietnamese and Chinese, when translating simpler summaries. Arabic translations improved with complexity due to the language's morphology. Overall, while LLMs offer contextual flexibility, they remain inconsistent, and current evaluation metrics fail to capture clinical relevance. The study highlights the need for domain-specific training, improved evaluation methods, and human oversight in medical translation.

Rashina Hoda

Agentic AI is poised to usher in a seismic paradigm shift in Software Engineering (SE). As technologists rush head-along to make agentic AI a reality, SE researchers are driven to establish agentic SE as a research area. While early visions of agentic SE are primarily focused on code-related activities, early empirical evidence calls for a consideration of a range of socio-technical concerns to make it work in practice. This paper contributes to the emerging community vision by: (a) recommending an expansion of its scope beyond code, toward a 'whole of process' vision, grounding it in SE foundations and evolution and emerging agentic SE frameworks, (b) proposing a preliminary set of values and principles to guide efforts, and (c) sharing guidance on designing/using well-defined vocabulary for agentic SE. It is hoped that these ideas will encourage community collaborations and steer the SE community towards laying strong foundations of agentic SE so its not only inevitable but also deliberate and desirable in the long run.

Lakshana Iruni Assalaarachchi, Zainab Masood, Rashina Hoda et al.

Software practitioners are discussing GenAI transformations in software project management openly and widely. To understand the state of affairs, we performed a grey literature review using 47 publicly available practitioner sources including blogs, articles, and industry reports. We found that software project managers primarily perceive GenAI as an "assistant", "copilot", or "friend" rather than as a "PM replacement", with support of GenAI in automating routine tasks, predictive analytics, communication and collaboration, and in agile practices leading to project success. Practitioners emphasize responsible GenAI usage given concerns such as hallucinations, ethics and privacy, and lack of emotional intelligence and human judgment. We present upskilling requirements for software project managers in the GenAI era mapped to the Project Management Institute's talent triangle. We share key recommendations for both practitioners and researchers.

Harsha Perera, Rashina Hoda, Rifat Ara Shams et al.

Human values, or what people hold important in their life, such as freedom, fairness, and social responsibility, often remain unnoticed and unattended during software development. Ignoring values can lead to values violations in software that can result in financial losses, reputation damage, and widespread social and legal implications. However, embedding human values in software is not only non-trivial but also generally an unclear process. Commencing as early as during the Requirements Engineering (RE) activities promises to ensure fit-for-purpose and quality software products that adhere to human values. But what is the impact of considering human values explicitly during early RE activities? To answer this question, we conducted a scenario-based survey where 56 software practitioners contextualised requirements analysis towards a proposed mobile application for the homeless and suggested values-laden software features accordingly. The suggested features were qualitatively analysed. Results show that explicit considerations of values can help practitioners identify applicable values, associate purpose with the features they develop, think outside-the-box, and build connections between software features and human values. Finally, drawing from the results and experiences of this study, we propose a scenario-based values elicitation process -- a simple four-step takeaway as a practical implication of this study.

Kashumi Madampe, Zainab Masood, Rashina Hoda

Background: Scrum is the most commonly used agile software development method, and the role of the Scrum Master (SM) in a Scrum environment is vital. Therefore, through an industry based university course for final year undergraduate and masters students, we aimed to give students both theoretical and practical understanding of the role of SM via hands-on experience on playing the role in real-world Scrum contexts. Method: We asked them (92 students) to share their experiences and learnings on the role of SM through reflective surveys and essays. Students participated in reflective surveys (311 survey responses) over 5 weeks in the course, and they submitted essays (92 essays) at the end of the course. We used a mixed-methods approach using Socio-Technical Grounded Theory analysis techniques and trend and regression based statistical analysis to analyse the survey responses and the essays. Findings: We identified the key responsibilities and duties of the SM, common challenges faced by the SM and the team due to the role of the SM, root causes of the challenges, strategies used by the SM and the team to overcome the challenges, and the overall experience of the students. Based on the results, we present recommendations for educators.

Dulaji Hidellaarachchi, John Grundy, Rashina Hoda et al.

Requirements Engineering (RE) requires the collaboration of various roles in SE, such as requirements engineers, stakeholders and other developers, and it is thus a highly human dependent process in software engineering (SE). Identifying how human aspects such as personality, motivation, emotions, communication, gender, culture and geographic distribution might impact RE would assist us in better supporting successful RE. The main objective of this paper is to systematically review primary studies that have investigated the effects of various human aspects on RE. A systematic literature review (SLR) was conducted and identified 474 initial primary research studies. These were eventually filtered down to 74 relevant, high-quality primary studies. Among the studies, the effects of communication have been considered in many RE studies. Other human aspects such as personality, motivation and gender have mainly been investigated to date related to SE studies including RE as one phase. Findings show that studying more than one human aspect together is beneficial, as this reveals relationships between various human aspects and how they together impact the RE process. However, the majority of these studied combinations of human aspects are unique. From 56.8% of studies that identified the effects of human aspects on RE, 40.5% identified the positive impact, 30.9% negative, 26.2% identified both impacts whereas 2.3% mentioned that there was no impact. This implies that a variety of human aspects positively or negatively affects the RE process and a well-defined theoretical analysis on the effects of different human aspects on RE remains to be defined and practically evaluated. Findings of this SLR help researchers who are investigating the impact of various human aspects on RE by identifying well-studied research areas, and highlight new areas that should be focused on in future research.

Dulaji Hidellaarachchi, John Grundy, Rashina Hoda et al.

Requirements Engineering (RE)-related activities require high collaboration between various roles in software engineering (SE), such as requirements engineers, stakeholders, developers, etc. Their demographics, views, understanding of technologies, working styles, communication and collaboration capabilities make RE highly human dependent. Identifying how "human aspects" such as motivation, domain knowledge, communication skills, personality, emotions, culture, etc. might impact RE-related activities would help us improve the RE and SE in general. This study aims to better understand current industry perspectives on the influence of human aspects on RE-related activities, specifically focusing on motivation and personality by targeting software practitioners involved in RE-related activities. Our findings indicate that software practitioners consider motivation, domain knowledge, attitude, communication skills and personality as highly important human aspects when involved in RE-related activities. A set of factors were identified as software practitioners motivational factors when involved in RE-related activities and identified important personality characteristics to have when involved in RE. We also identified factors that made individuals less effective when involved in RE-related activities and obtained an initial idea on measuring individuals performance when involved in RE. The findings from our study suggest various areas needing more investigation, and we summarise a set of key recommendations for further research.

Kashumi Madampe, Rashina Hoda, John Grundy

Background: A preliminary study we conducted showed that software practitioners respond to requirements changes(RCs) with different emotions, and that their emotions vary at stages of the RC handling life cycle, such as receiving, developing, and delivering RCs. Objective: We wanted to study more comprehensively how practitioners emotionally respond to RCs. Method: We conducted a world-wide survey with the participation of 201 software practitioners. In our survey, we used the Job-related Affective Well-being Scale (JAWS) and open-ended questions to capture participants emotions when handling RCs in their work and query about the different circumstances when they feel these emotions. We used a combined approach of statistical analysis, JAWS, and Socio-Technical Grounded Theory (STGT) for Data Analysis to analyse our survey data. Findings: We identified (1) emotional responses to RCs, i.e., the most common emotions felt by practitioners when handling RCs; (2) different stimuli -- such as the RC, the practitioner, team, manager, customer -- that trigger these emotions through their own different characteristics; (3)emotion dynamics, i.e., the changes in emotions during the project and RC handling life cycles; (4) distinct events where particular emotions are triggered:project milestones, and RC stages; (5) and time related matters that regulate the emotion dynamics. Conclusion: Practitioners are not pleased with receiving RCs all the time. Last minute RCs introduced closer to a deadline especially violate emotional well-being of practitioners. We present some practical recommendations for practitioners to follow, including a dual-purpose emotion-centric decision guide to help decide when to introduce or accept an RC, and some future key research directions.

Zainab Masood, Rashina Hoda, Kelly Blincoe

Self-assignment, where software developers choose their own tasks, is a common practice in agile teams. However, it is not known why developers select certain tasks. It is important for managers to be aware of these reasons to ensure sustainable self-assignment practices. We investigated developers' preferences while they are choosing tasks for themselves. We collected data from 42 participants working in 25 different software companies. We applied Grounded Theory procedures to study and analyse factors for self-assigning tasks, which we grouped into three categories: task-based, developer-based, and opinion-based. We found that developers have individual preferences and not all factors are important to every developer. Managers share some common and varying perspectives around the identified factors. Most managers want developers to give higher priority to certain factors. Developers often need to balance between task priority and their own individual preferences, and managers facilitate this through a variety of strategies. More risk-averse managers encourage expertise-based self-assignment to ensure tasks are completed quickly. Managers who are risk-balancing encourage developers to choose tasks that provide learning opportunities only when there is little risk of delays or reduced quality. Finally, growth-seeking managers regularly encourage team members to pick tasks outside their comfort zone to encourage growth opportunities. Our findings will help managers to understand what developers consider when self-assigning tasks and help them empower their teams to practice self-assignment in a sustainable manner.

Mohammed-Amr Abd El-Migid, Damon Cai, Thomas Niven et al.

In recent years, Agile methods have continued to grow into a popular means of modulating team productivity, even garnering a presence in non-software development related industries. The uptake of Agile methods has been driven by their flexibility, making them more suitable for many teams when compared to traditional approaches. However, an inevitable expectation for an Agile workflow is a higher level of change and uncertainty regarding requirements and tasks, which can ultimately have impacts on team member emotional states. The extent of such emotion impacts has motivated our research into the manner in which emotional states evolve in an Agile setting, along with whether such emotions can be accurately measured. To this end, we have developed Emotimonitor, a Trello power-up designed to capture information on emotions of team members as they relate to their technical tasks through a user-friendly interface. Emotimonitor will better enable team members to express their emotional states through emoji reactions on Trello cards, while also providing team leaders with a dashboard summarising these reactions as visualisations and statistical data. It is extensible and potentially provides an outlet for team members operating in Agile environments to better express their emotional states.

Zainab Masood, Rashina Hoda, Kelly Blincoe

Scrum, the most popular agile method and project management framework, is widely reported to be used, adapted, misused, and abused in practice. However, not much is known about how Scrum actually works in practice, and critically, where, when, how and why it diverges from Scrum by the book. Through a Grounded Theory study involving semi-structured interviews of 45 participants from 30 companies and observations of five teams, we present our findings on how Scrum works in practice as compared to how it is presented in its formative books. We identify significant variations in these practices such as work breakdown, estimation, prioritization, assignment, the associated roles and artefacts, and discuss the underlying rationales driving the variations. Critically, we claim that not all variations are process misuse/abuse and propose a nuanced classification approach to understanding variations as standard, necessary, contextual, and clear deviations for successful Scrum use and adaptation

Rashina Hoda

Grounded Theory (GT), a sociological research method designed to study social phenomena, is increasingly being used to investigate the human and social aspects of software engineering (SE). However, being written by and for sociologists, GT is often challenging for a majority of SE researchers to understand and apply. Additionally, SE researchers attempting ad hoc adaptations of traditional GT guidelines for modern socio-technical (ST) contexts often struggle in the absence of clear and relevant guidelines to do so, resulting in poor quality studies. To overcome these research community challenges and leverage modern research opportunities, this paper presents Socio-Technical Grounded Theory (STGT) designed to ease application and achieve quality outcomes. It defines what exactly is meant by an ST research context and presents the STGT guidelines that expand GT's philosophical foundations, provide increased clarity and flexibility in its methodological steps and procedures, define possible scope and contexts of application, encourage frequent reporting of a variety of interim, preliminary, and mature outcomes, and introduce nuanced evaluation guidelines for different outcomes. It is hoped that the SE research community and related ST disciplines such as computer science, data science, artificial intelligence, information systems, human computer/robot/AI interaction, human-centered emerging technologies (and increasingly other disciplines being transformed by rapid digitalisation and AI-based augmentation), will benefit from applying STGT to conduct quality research studies and systematically produce rich findings and mature theories with confidence.

Waqar Hussain, Mojtaba Shahin, Rashina Hoda et al.

Agile methods are predominantly focused on delivering business values. But can Agile methods be adapted to effectively address and deliver human values such as social justice, privacy, and sustainability in the software they produce? Human values are what an individual or a society considers important in life. Ignoring these human values in software can pose difficulties or risks for all stakeholders (e.g., user dissatisfaction, reputation damage, financial loss). To answer this question, we selected the Scaled Agile Framework (SAFe), one of the most commonly used Agile methods in the industry, and conducted a qualitative case study to identify possible intervention points within SAFe that are the most natural to address and integrate human values in software. We present five high-level empirically-justified sets of interventions in SAFe: artefacts, roles, ceremonies, practices, and culture. We elaborate how some current Agile artefacts (e.g., user story), roles (e.g., product owner), ceremonies (e.g., stand-up meeting), and practices (e.g., business-facing testing) in SAFe can be modified to support the inclusion of human values in software. Further, our study suggests new and exclusive values-based artefacts (e.g., legislative requirement), ceremonies (e.g., values conversation), roles (e.g., values champion), and cultural practices (e.g., induction and hiring) to be introduced in SAFe for this purpose. Guided by our findings, we argue that existing Agile methods can account for human values in software delivery with some evolutionary adaptations.

Kashumi Madampe, Rashina Hoda, John Grundy

Agile processes are now widely practiced by software engineering (SE) teams, and the agile manifesto claims that agile methods support responding to changes well. However, no study appears to have researched whether this is accurate in reality. Requirements changes (RCs) are inevitable in any software development environment, and we wanted to acquire a holistic picture of how RCs occur and are handled in agile SE teams in practice. We also wanted to know whether responding to changes is the only or a main reason for software teams to use agile in their projects. To do this we conducted a mixed-methods research study which comprised of interviews of 10 agile practitioners from New Zealand and Australia, a literature review, and an in-depth survey with the participation of 40 agile practitioners world-wide. Through this study we identified different types of RCs, their origination including reasons for origination, forms, sources, carriers, and events at which they originate, challenging nature, and finally whether agile helps to respond to changes or not. We also found that agile teams seem to be reluctant to accept RCs, and therefore, they use several mitigation strategies. Additionally, as they accept the RCs, they use a variety of techniques to handle them. Furthermore, we found that agile allowing better response to RCs is only a minor reason for practicing agile. Several more important reasons included being able to deliver the product in a shorter period and increasing team productivity. Practitioners stated this improves the agile team environment and thus are the real motivators for teams to practice agile. Finally, we provide a set of practical recommendations that can be used to better handle RCs effectively in agile software development environments.

Paul Ralph, Nauman bin Ali, Sebastian Baltes et al.

Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for research methods commonly used in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, will improve research quality and make peer review more effective, reliable, transparent and fair.

Paul Ralph, Sebastian Baltes, Gianisa Adisaputri et al.

Context. As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions. Objective. This study investigates the effects of the pandemic on developers' wellbeing and productivity. Method. A questionnaire survey was created mainly from existing, validated scales and translated into 12 languages. The data was analyzed using non-parametric inferential statistics and structural equation modeling. Results. The questionnaire received 2225 usable responses from 53 countries. Factor analysis supported the validity of the scales and the structural model achieved a good fit (CFI = 0.961, RMSEA = 0.051, SRMR = 0.067). Confirmatory results include: (1) the pandemic has had a negative effect on developers' wellbeing and productivity; (2) productivity and wellbeing are closely related; (3) disaster preparedness, fear related to the pandemic and home office ergonomics all affect wellbeing or productivity. Exploratory analysis suggests that: (1) women, parents and people with disabilities may be disproportionately affected; (2) different people need different kinds of support. Conclusions. To improve employee productivity, software companies should focus on maximizing employee wellbeing and improving the ergonomics of employees' home offices. Women, parents and disabled persons may require extra support.

Viktoria Stray, Nils Brede Moe, Rashina Hoda

According to the principles articulated in the agile manifesto, motivated and empowered software developers relying on technical excellence and simple designs, create business value by delivering working software to users at regular short intervals. These principles have spawned many practices. At the core of these practices is the idea of autonomous, self-managing, or self-organizing teams whose members work at a pace that sustains their creativity and productivity. This article summarizes the main challenges faced when implementing autonomous teams and the topics and research questions that future research should address.