Two VERSEN members, Ivano Malavolta and Alexander Serebrenik are among the co-editors of the upcoming special issue of Empirical Software Engineering on collective knowledge in software engineering.
Knowledge-sharing platforms, such as Stack Overflow, GitHub, Twitter, and Slack, have changed how developers share knowledge and seek information on the web. These platforms store a significant amount of collective knowledge that is contributed by a large and rapidly-evolving group of participants. For instance, Stack Overflow provides a question and answer (Q&A) platform for developers to share programming-related knowledge. As of 2020, more than 12 million developers from around the world have contributed to Stack Overflow. Through such a collective model, Stack Overflow has accumulated a tremendous amount of knowledge, including more than 19 million questions, 29 million answers, and 74 million comments. As another example, GitHub has accumulated more than 61 million software project repositories that are developed by more than 20 million developers. Developers collectively contribute various types of knowledge such as source code and issue reports that provide rich information to assist future software development. In addition to Stack Overflow and GitHub, developers also commonly share their knowledge on social media and communication platforms, such as Twitter and Slack. This massive amount of collective knowledge could be leveraged to significantly benefit the software engineering community. In fact, many recent studies leverage such collective knowledge (e.g., from Stack Overflow, GitHub, and Twitter) to uncover empirical evidence or develop research techniques to further improve the software development process and quality assurance practice. The empirical findings and techniques have shown great success in addressing various software engineering problems (e.g., code generation, code recommendation, debugging and repair, and API documentation enhancement). Therefore, collective knowledge in software engineering has attracted great attention in the software engineering and other research communities (e.g., data mining and AI).
We invite the submission of high-quality papers describing original and significant work in all areas of collective knowledge in software engineering, including but not limited to:
- Improving the sharing and management of collective knowledge;
- Approaches and techniques for knowledge discovery from collective software engineering knowledge; and
- Leveraging collective knowledge to facilitate software engineering tasks.
Further information about the special issue, evaluation criteria and submission guidelines can be found on https://www.springer.com/journal/10664/updates/18201592