In software engineering, benchmarks are widely used to evaluate and compare the performance, functionality, and reliability of analysis tools. Despite the prevalence of benchmarks in areas such as databases, machine learning, and programming languages, there is a notable absence of publicly available benchmarks for REST APIs, a cornerstone of modern web-based systems. While existing research papers occasionally employ similar REST APIs in their evaluations, opportunistic API selection hampers comparison. Moreover, these studies often rely on API documentation and structural characteristics. Without a reliable benchmark, API data used in evaluations may be outdated or inaccurate, compromising reliability and reproducibility. Hence, this paper addresses a gap in the literature by providing a comprehensive and Public REST API Benchmark (PRAB), to be utilized by researchers in their evaluations. The benchmark contains documentation and structural characteristics of 60 publicly available REST APIs. First, we conduct a systematic mapping study to discover the available and public REST APIs that are utilized in the academic literature. Then, by analyzing the resulting APIs, we report their structural characteristics (e.g., routes, query parameters, HTTP methods, authentication). Finally, we provide their documentation (i.e., OpenAPI Specification, Postman Collection) in a publicly available GitHub repository, to help with future evaluations of REST API studies.

Conducting socio-technical software engineering research on closed-source software is difficult as most organizations do not want to give access to their code repositories. Most experiments and publications therefore focus on open-source projects, which only provides a partial view of software development communities. Yet, closing the gap between open and closed source software industries is essential to increase the validity and applicability of results stemming from socio-technical software engineering research. We contribute to this effort by sharing our work in a large company counting 4,800 employees. We mined 101 repositories and produced the GDED dataset containing socio-technical information about 106,216 commits, 470,940 file modifications and 3,471,556 method modifications from 164 developers during the last 13 years, using various programming languages. For that, we used GitDelver, an open-source tool we developed on top of Pydriller, and anonymized and scrambled the data to comply with legal and corporate requirements. Our dataset can be used for various purposes and provides information about code complexity, self-admitted technical debt, bug fixes, as well as temporal information. We also share our experience regarding the processing of sensitive data to help other organizations making datasets publicly available to the research community.