Open source is increasingly present in all software delivered today, including that which is entirely commercial in origin. Thus, ensuring sustainability of open source projects becomes increasingly important. The situation is not helped by that the fact that historically the level of female participation in open source projects has been abysmally low, as are challenges to participation that can be experienced by those with different ethnic backgrounds. It has been suggested that as the involvement of commercial organizations in open source has increased, the level of female participation and ethnic diversity has increased. We investigated this issue with ten male and female practitioners from different geographic locations who have extensive experience in open source communities. Drawing on concepts from digital impression management, we investigated the manner in which these impression management techniques were used to participate in open source communities, and the differences between usage by male and female participants.

Large Language Models (LLMs) are now ubiquitous in software engineering (SE) research and practice, yet their non-determinism, opaque training data, and rapidly evolving models threaten the reproducibility and replicability of empirical studies. We address this challenge through a collaborative effort of 22 researchers, presenting a taxonomy of seven study types that organizes the landscape of LLM involvement in SE research, together with eight guidelines for designing and reporting such studies. Each guideline distinguishes requirements (must) from recommended practices (should) and is contextualized by the study types it applies to. Our guidelines recommend that researchers: (1) declare LLM usage and role; (2) report model versions, configurations, and customizations; (3) document the tool architecture beyond the model; (4) disclose prompts, their development, and interaction logs; (5) validate LLM outputs with humans; (6) include an open LLM as a baseline; (7) use suitable baselines, benchmarks, and metrics; and (8) articulate limitations and mitigations. We complement the guidelines with an applicability matrix mapping guidelines to study types and a reporting checklist for authors and reviewers. We maintain the study types and guidelines online as a living resource for the community to use and shape (llm-guidelines.org).

Open source has revolutionized how software development is carried out, with a growing number of individuals and organizations contributing to open source projects. As the importance of open source continues to grow, companies also expect to grow thriving and sustainable open source communities with continued contributions and better collaborations. In this study, we applied the contribution leaderboard to seven open source projects initiated by Alibaba. We conducted a case study to investigate the perceptions and facts regarding how to motivate collaboration through gamification. Specifically, we employed a social network algorithm, OpenRank, to evaluate and steer developers' contributions. We validated the effectiveness of OpenRank by comparing it with other evaluation metrics and surveying developers. Through semi-structured interviews and project metric analysis, we found that the OpenRank Leaderboard can promote transparent communication environments, a better community atmosphere, and improved collaboration behavior.

The World Health Organization (WHO) highlights the significant threat that unsustainably long working hours pose to our mental and physical well-being. Aligning with this concern, an Agile principle emphasizes that “Agile processes promote sustainable development.” However, previous work in 2023 debunked this notion of inherent stability in Agile systems, such as the Scrum and Kanban frameworks. In this study, we aim to analyse the relationship between system stability and the tendency of teams to work outside reasonable office hours. We inspect 295 historic Agile projects completed in intive, a software development company. We assess the percentage of late-night, early-morning, or weekend hours where a Product Backlog Item (PBI) was created or resolved and compare this percentage of Unsustainable Hours metric to the Stability Metric and the number of Inventory Days remaining. The analysis showed that almost no correlation exists between the Unsustainable Hours worked and either the system stability or outstanding inventory. These findings indicate that, while working unconventional and potentially excessive hours is a concern, it does not appear to be linked to the stability of Agile systems. This highlights the need for a deeper understanding of individual and team motivations to foster long-term sustainable work practices.

Agile systems, like the Kanban and Scrum frameworks, are built on assumptions of sustainability and stability, however, there is little empirical evidence on whether such systems are stable in practice or not. Therefore, in this study we aim to inspect the stability of Agile systems by leveraging the concept of stability described in Queueing Theory. We define a novel metric, the Stability Metric, as a way of assessing queueing systems, especially Agile systems. We inspect 926 Jira projects in 14 organizations with over 1.6 million product backlog items using this metric. The analysis showed that 72.89% of these Jira projects were not stable and stable systems, on average, had product backlog sizes 10 times shorter than unstable ones. These results suggest that while the goal of Agile is to create a sustainable, stable way of working, this is not guaranteed, and a better understanding of systems and queues may be required to help design, create, coach, and maintain optimal Agile systems.

This paper reports on the progress in the project COVIGILANT, which is aimed at developing an evaluation taxonomy for Contact Tracing Applications (CTAs) for COVID-19 Specifically, this article describes the development of Usability, one pillar of the COVIGILANT taxonomy, discussing the classification and decision-making processes, and the initial model validation The validation process was undertaken in two stages First, we validated how the Usability pillar could be used to evaluate the Irish Health Services Executive (HSE) COVID-19 CTA While this supported many of the attributes that we had within the Usability pillar, it also identified issues We made amendments based on these, and undertook a second study, this time evaluating 4 CTAs used in other countries This has led to the completion of the Usability pillar, which can now be used to evaluate global CTAs Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda All rights reserved

Background: Contact tracing remains a critical part of controlling the spread of COVID-19. Many countries have developed novel software applications (Apps) in an effort to augment traditional contact tracing methods. Aim: To conduct a national survey of the Irish population to examine barriers and levers to the use of a contact tracing App.Methods: Adult participants were invited to respond via an online survey weblink sent via email and messaging Apps and posted on our university website and on popular social media platforms. Results: A total of 8,088 responses were received, with all 26 counties of the Republic of Ireland represented. 54% of respondents said they would definitely download a contact tracing App, while 30% said they would probably download a contact tracing App. 95% of respondents identified at least one reason for them to download the App, with the most common reasons being the potential for the App to help family members and friends and a sense of responsibility to the wider community. 59% identified at least one reason not to download the App, with the most common reasons being fear that technology companies or the government might use the App technology for greater surveillance after the pandemic.Conclusion: Irish citizens surveyed express high levels of willingness to download a public health-backed App to augment contact tracing. Concerns raised regarding privacy and data security will be critical if the App is to achieve the large-scale adoption and ongoing use required for its effective operation.

Free/Libre and Open Source Software (FLOSS) communities are composed, in part, of volunteers, many of whom contribute infrequently. However, these infrequent volunteers contribute to the sustainability of FLOSS projects, and should ideally be encouraged to continue participating, even if they cannot be persuaded to contribute regularly. Infrequent contributions are part of a trend which has been widely observed in other sectors of volunteering, where it has been termed “episodic volunteering” (EV). Previous FLOSS research has focused on the Onion model, differentiating core and peripheral developers, with the latter considered as a homogeneous group. We argue this is too simplistic, given the size of the periphery group and the myriad of valuable activities they perform beyond coding. Our exploratory qualitative survey of 13 FLOSS communities investigated what episodic volunteering looks like in a FLOSS context. EV is widespread in FLOSS communities, although not specifically managed. We suggest several recommendations for managing EV based on a framework drawn from the volunteering literature. Also, episodic volunteers make a wide range of value-added contributions other than code, and they should neither be expected nor coerced into becoming habitual volunteers.

Large-scale open source communities, such as the Linux kernel, have gone through decades of development, substantially growing in scale and complexity. In the traditional workflow, maintainers serve as "gatekeepers" for the subsystems that they maintain. As the number of patches and authors significantly increases, maintainers come under considerable pressure, which may hinder the operation and even the sustainability of the community. A few subsystems have begun to use new workflows to address these issues. However, it is unclear to what extent these new workflows are successful, or how to apply them. Therefore, we conduct an empirical study on the multiple-committer model (MCM) that has provoked extensive discussion in the Linux kernel community. We explore the effect of the model on the i915 subsystem with respect to four dimensions: pressure, latency, complexity, and quality assurance. We find that after this model was adopted, the burden of the i915 maintainers was significantly reduced. Also, the model scales well to allow more committers. After analyzing the online documents and interviewing the maintainers of i915, we propose that overloaded subsystems which have trustworthy candidate committers are suitable for adopting the model. We further suggest that the success of the model is closely related to a series of measures for risk mitigation---sufficient precommit testing, strict review process, and the use of tools to simplify work and reduce errors. We employ a network analysis approach to locate candidate committers for the target subsystems and validate this approach and contextual success factors through email interviews with their maintainers. To the best of our knowledge, this is the first study focusing on how to scale open source communities. We expect that our study will help the rapidly growing Linux kernel and other similar communities to adapt to changes and remain sustainable.

Contact Tracing (CT) is seen as a key tool in reducing the propagation of viruses, such as Covid-19. Given near ubiquitous societal usage of mobile devices, governments globally are choosing to augment manual CT with CT applications (CTAs) on smart phones. While a plethora of solutions have been spawned, their overall effectiveness is based on majority population uptake. Unfortunately, their rapid deployment and the nature of the information they gather has prompted a variety of user concerns such as information privacy and Data Protection (DP). Therefore selecting an optimal solution to maximise user trust and uptake is crucial. In this work, we present our initial deliberations towards a CTA evaluation taxonomy for societal concerns. This is a subset of a larger taxonomy which is being developed as part of the Science Foundation Ireland project - COVIGILANT, which will ultimately be utilized to evaluate and compare numerous CTAs to select the optimal solution for a given population. In this paper we present our preliminary CTAs with respect to the societal concerns of security, data protection and transparency. We then elaborate on these CTAs by means of two illustrative examples in order to promote discussion, evaluation and refinement.

Successful Free/Libre and Open Source Software (FLOSS) projects incorporate both habitual and infrequent, or episodic, contributors. Using the concept of episodic volunteering (EV) from the general volunteering literature, we derive a model consisting of five key constructs that we hypothesize affect episodic volunteers' retention in FLOSS communities. To evaluate the model we conducted a survey with over 100 FLOSS episodic volunteers. We observe that three of our model constructs (social norms, satisfaction and community commitment) are all positively associated with volunteers' intention to remain, while the two other constructs (psychological sense of community and contributor benefit motivations) are not. Furthermore, exploratory clustering on unobserved heterogeneity suggests that there are four distinct categories of volunteers: satisfied, classic, social and obligated. Based on our findings, we offer suggestions for projects to incorporate and manage episodic volunteers, so as to better leverage this type of contributors and potentially improve projects' sustainability.

The rapid pace with which software needs to be built, together with the increasing need to evaluate changes for end users both quantitatively and qualitatively calls for novel software engineering approaches that focus on short release cycles, continuous deployment and delivery, experiment-driven feature development, feedback from users, and rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support flexible data-driven decision-making in the development lifecycle. Most importantly, deep synergies are needed between software engineers, managers, and data scientists. This paper reports on the results of the joint 5th International Workshop on Rapid Continuous Software Engineering (RCoSE 2019) and the 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (DDrEE 2019), which focuses on the challenges and potential solutions in the area of continuous data-driven software engineering.

Empirical research studies are the principal mechanism through which the software engineering research community studies and learns from software engineering practice. The focus on empirical studies has increased significantly in the past decade, more or less coinciding with the emergence of evidence-based software engineering, an idea that was proposed in 2004. As a consequence, the software engineering community is familiar with a range of empirical methods. However, while several overviews exist of popular empirical research methods, such as case studies and experiments, we lack a 'holistic' view of a more complete spectrum of research methods. Furthermore, while researchers will readily accept that all methods have inherent limitations, methods such as case study are still frequently critiqued for the lack of control that a researcher can exert in such a study, their use of qualitative data, and the limited generalizability that can be achieved. Controlled experiments are seen by many as yielding stronger evidence than case studies, but these can also be criticized due to the limited realism of the context in which they are conducted. We identify a holistic set of research methods and indicate their strengths and weaknesses in relation to various research elements.

This book collects the proceedings of the Second International Conference on Open Software - OSS 2006, held in Como, Italy in June, 2006, where researchers from all over the world discussed how OSS is produced, its huge potential for innovative applications and in groundbreaking OSS business models. The book takes an important step toward appreciation of the OSS phenomenon, presenting 20 refereed full papers and 12 more in shorter form.