Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.

The intersection of ageism and sexism can create a hostile environment for veteran software developers belonging to marginalized genders. In this study, we conducted 14 interviews to examine the experiences of people at this intersection, primar-ily women, in order to discover the strategies they employed in order to successfully remain in the field. We identified 283 codes, which fell into three main categories: Strategies, Experiences, and Perception. Several strategies we identified, such as (Deliberately) Not Trying to Look Younger, were not previously described in the software engineering literature. We found that, in some compa-nies, older women developers are recognized as having particular value, further strengthening the known benefits of diversity in the workforce. Based on the experiences and strategies, we suggest organizations employing software developers to consider the benefits of hiring veteran women software developers. For example, companies can draw upon the life experiences of older women developers in order to better understand the needs of customers from a similar demographic. While we recognize that many of the strategies employed by our study participants are a response to systemic issues, we still consider that, in the short-term, there is benefit in describing these strategies for developers who are experiencing such issues today.

Almost all software, open or closed, builds on open source software and therefore needs to comply with the license obligations of the open source code. Not knowing which licenses to comply with poses a legal danger to anyone using open source software. This article investigates the extent of inconsistencies between licenses declared by an open source project at the top level of the repository and the licenses found in the code. We analyzed a sample of 1,000 open source GitHub repositories. We find that about half of the repositories did not fully declare all licenses found in the code. Of these, approximately 10% represented a permissive vs. copyleft license mismatch. Furthermore, existing tools cannot fully identify licences. We conclude that users of open source code should not just look at the declared licenses of the open source code they intend to use, but rather examine the software to understand its actual licenses.

Abstract Using qualitative data analysis (QDA) to perform domain analysis and modeling has shown great promise. Yet, the evaluation of such approaches has been limited to single-case case studies. While these exploratory cases are valuable for an initial assessment, the evaluation of the efficacy of QDA to solve the suggested problems is restricted by the common single-case case study research design. Using our own method, called QDAcity-RE, as the example, we present an in-depth empirical evaluation of employing qualitative data analysis for domain modeling using a controlled experiment design. Our controlled experiment shows that the QDA-based method leads to a deeper and richer set of domain concepts discovered from the data, while also being more time efficient than the control group using a comparable non-QDA-based method with the same level of traceability.

We draw on the concept of episodic volunteering (EV) from the general volunteering literature to identify practices for managing EV in free/libre/open source software (FLOSS) communities. Infrequent but ongoing participation is widespread, but the practices that community managers are using to manage EV, and their concerns about EV, have not been previously documented. We conducted a policy Delphi study involving 24 FLOSS community managers from 22 different communities. Our panel identified 16 concerns related to managing EV in FLOSS, which we ranked by prevalence. We also describe 65 practices for managing EV in FLOSS. Almost three-quarters of these practices are used by at least three community managers. We report these practices using a systematic presentation that includes context, relationships between practices, and concerns that they address. These findings provide a coherent framework that can help FLOSS community managers to better manage episodic contributors.

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.

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.

Episodic volunteers, who prefer short term engagement to habitual contributions, are present in Free/Libre and Open Source Software (FLOSS) communities. Little is known about how they are viewed within their communities, how they view their communities, how community managers are managing them, or even how many episodic volunteers contribute to FLOSS projects and how much they contribute. Knowing more about the prevalence and management of episodic volunteers in FLOSS will help community managers make better decisions for engaging and utilizing these volunteers. My dissertation addresses these questions, providing a picture of what episodic volunteering looks like in the context of FLOSS communities.

Free/libre and open source software are frequently described as a single community or movement. The difference between free software and open source ideology may influence founders, resulting in different types of companies being created. Specifically, the relationship between free/libre software ideology and social entrepreneurships is investigated. This paper presents seven case studies of businesses, five of which were founded by people who identify with the free/libre software movement. The result is a theory that small businesses founded by free/libre software advocates have three characteristics of social entrepreneurships. First, social benefit is prioritized over wealth creation. Second, the business's social mission is not incidental but is furthered through its for-profit activities, rather than supported by the company's profits. Third, the company's success is defined in part by the success of its social mission. Free/libre software entrepreneurs who recognize their activities as social entrepreneurships can benefit from the existing literature on the unique challenges faced by socially-oriented businesses.

The importance of volunteers in open source has led to the position of community manager becoming more common in foundations and projects. Yet the advice for volunteer management and retention is fragmented, incomplete, contradictory, and has not been empirically examined. Our aim is to fill this gap by creating a comprehensive guidebook of best practices drawing from open source practitioner guides and general literature on volunteering, and to subject a subset of practices to empirical study. A method for evaluating volunteer attrition in terms of value to the organization will also be developed.