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  1. With the emergence of social coding platforms, collaboration has become a key and dynamic aspect to the success of software projects. In such platforms, developers have to collaborate and deal with issues of collaboration in open-source software development. Although collaboration is challenging, collaborative development produces better software systems than any developer could produce alone. Several approaches have investigated collaboration challenges, for instance, by proposing or evaluating models and tools to support collaborative work. Despite the undeniable importance of the existing efforts in this direction, there are few works on collaboration from perspectives of developers. In this work, we aim tomore »investigate the perceptions of open-source software developers on collaborations, such as motivations, techniques, and tools to support global, productive, and collaborative development. Following an ad hoc literature review, an exploratory interview study with 12 open-source software developers from GitHub, our novel approach for this problem also relies on an extensive survey with 121 developers to confirm or refute the interview results. We found different collaborative contributions, such as managing change requests. Besides, we observed that most collaborators prefer to collaborate with the core team instead of their peers. We also found that most collaboration happens in software development (60%) and maintenance (47%) tasks. Furthermore, despite personal preferences to work independently, developers still consider collaborating with others in specific task categories, for instance, software development. Finally, developers also expressed the importance of the social coding platforms, such as GitHub, to support maintainers, and contributors in making decisions and developing tasks of the projects. Therefore, these findings may help project leaders optimize the collaborations among developers and reduce entry barriers. Moreover, these findings may support the project collaborators in understanding the collaboration process and engaging others in the project.« less
    Free, publicly-accessible full text available October 1, 2022
  2. Data scientists commonly use computational notebooks because they provide a good environment for testing multiple models. However, once the scientist completes the code and finds the ideal model, the data scientist will have to dedicate time to clean up the code in order for others to understand it. In this paper, we perform a qualitative study on how scientists clean their code in hopes of being able to suggest a tool to automate this process. Our end goal is for tool builders to address possible gaps and provide additional aid to data scientists, who can then focus more on theirmore »actual work rather than the routine and tedious cleaning duties.« less
  3. Open source software projects often rely on package management systems that help projects discover, incorporate, and maintain dependencies on other packages, maintained by other people. Such systems save a great deal of effort over ad hoc ways of advertising, packaging, and transmitting useful libraries, but coordination among project teams is still needed when one package makes a breaking change affecting other packages. Ecosystems differ in their approaches to breaking changes, and there is no general theory to explain the relationships between features, behavioral norms, ecosystem outcomes, and motivating values. We address this through two empirical studies. In an interview casemore »study, we contrast Eclipse, NPM, and CRAN, demonstrating that these different norms for coordination of breaking changes shift the costs of using and maintaining the software among stakeholders, appropriate to each ecosystem’s mission. In a second study, we combine a survey, repository mining, and document analysis to broaden and systematize these observations across 18 ecosystems. We find that all ecosystems share values such as stability and compatibility, but differ in other values. Ecosystems’ practices often support their espoused values, but in surprisingly diverse ways. The data provides counterevidence against easy generalizations about why ecosystem communities do what they do.« less
  4. The notion of forking has changed with the rise of distributed ver- sion control systems and social coding environments, like GitHub. Traditionally forking refers to splitting off an independent devel- opment branch (which we call hard forks); research on hard forks, conducted mostly in pre-GitHub days showed that hard forks were often seen critical as they may fragment a community. Today, in so- cial coding environments, open-source developers are encouraged to fork a project in order to contribute to the community (which we call social forks), which may have also influenced perceptions and practices around hard forks. To revisit hardmore »forks, we identify, study, and classify 15,306 hard forks on GitHub and interview 18 owners of hard forks or forked repositories. We find that, among others, hard forks often evolve out of social forks rather than being planned deliberately and that perception about hard forks have indeed changed dramatically, seeing them often as a positive non- competitive alternative to the original project.« less
  5. Open source is ubiquitous and many projects act as critical in- frastructure, yet funding and sustaining the whole ecosystem is challenging. While there are many different funding models for open source and concerted efforts through foundations, donation platforms like PayPal, Patreon, and OpenCollective are popular and low-bar platforms to raise funds for open-source development. With a mixed-method study, we investigate the emerging and largely unexplored phenomenon of donations in open source. Specifically, we quantify how commonly open-source projects ask for donations, statistically model characteristics of projects that ask for and re- ceive donations, analyze for what the requested funds aremore »needed and used, and assess whether the received donations achieve the intended outcomes. We find 25,885 projects asking for donations on GitHub, often to support engineering activities; however, we also find no clear evidence that donations influence the activity level of a project. In fact, we find that donations are used in a multitude of ways, raising new research questions about effective funding.« less
  6. In globally distributed software development, many software developers have to collaborate and deal with issues of collaboration. Although collaboration is challenging, collaborative development produces better software than any developer could produce alone. Unlike previous work which focuses on the proposal and evaluation of models and tools to support collaborative work, this paper presents an interview study aiming to understand (i) the motivations, (ii) how collaboration happens, and (iii) the challenges and barriers of collaborative software development. After interviewing twelve experienced software developers from GitHub, we found different types of collaborative contributions, such as in the management of requests for changes.more »Our analysis also indicates that the main barriers for collaboration are related to non-technical, rather than technical issues.« less
  7. Software developed in different platforms has different characteristics and needs. More specifically, code changes are differently performed in the mobile platform compared to non-mobile platforms (e.g., desktop and Web platforms). Prior works have investigated the differences in specific platforms. However, we still lack a deeper understanding of how code changes evolve across different software platforms. In this paper, we present a study aiming at investigating the frequency of changes and how source code changes, build changes and test changes co-evolve in mobile and non-mobile platforms. We developed linear regression models to explain which factors influence the frequency of changes inmore »different platforms and applied the Apriori algorithm to find types of changes that frequently occur together. Our findings show that non-mobile repositories have a higher number of commits per month compared to mobile and our regression models suggest that being mobile significantly impacts on the number of commits in a negative direction when controlling for confound factors, such as code size. We also found that developers do not usually change source code files together with build files or test files. We argue that our results can provide valuable information for developers on how changes are performed in different platforms so that practices adopted in successful software systems can be followed.« less