Automatic static analysis tools help developers to automatically spot code issues in their software. They can be of extreme value in languages with dynamic characteristics, such as JavaScript, where developers can easily introduce mistakes which can go unnoticed for a long time, e.g., a simple syntactic or spelling mistake. Although research has already shown how developers perceive such tools for strongly-typed languages such as Java, little is known about their perceptions when it comes to dynamic languages. In this paper, we investigate what motivates and how developers make use of such tools in JavaScript projects. To that goal, we apply a qualitative research method to conduct and analyze a series of 15 interviews with developers responsible for the linter configuration in reputable OSS JavaScript projects that apply the most commonly used linter, ESLint. The results describe the benefits that developers obtain when using ESLint, the different ways one can configure the tool and prioritize its rules, and the existing challenges in applying linters in the real world. These results have direct implications for developers, tool makers, and researchers, such as tool improvements, and a research agenda that aims to increase our knowledge about the usefulness of such analyzers.

Background: Projects of all sizes and impact are leveraging the services of the social coding platform GitHub to collaborate. Since users' information and actions are recorded, GitHub has been mined for over 6 years now to investigate aspects of the collaborative open source software (OSS) development paradigm. Aim: In this research, we use this data to investigate the relation between project growth as a proxy for success, and social diversity. Method: We first categorize active OSS projects into a five-star rating using a benchmarking system we based on various project growth metrics; then we study the relation between this rating and the reported social diversities for the team members of those projects. Results: Our findings highlight a statistically significant relation; however, the effect is small. Conclusions: Our findings suggest the need for further research on this topic; moreover, the proposed benchmarking method may be used in future work to determine OSS project success on collaboration platforms such as GitHub.