The Content Quality of Crowdsourced Knowledge on Stack Overflow- A Systematic Mapping Study

Shahrour, Gheida; De Quincey, Ed; Lal, Sangeeta

Abstract

Community Question Answering (CQA) forums such as Stack Overflow (SO) are a form of crowdsourced knowledge for software engineers who seek solutions to development and programming challenges. While such a forum provides valuable support to engineers, it often contains low quality content that impacts users' experience and the longevity of new users. Past research shows that most of the low-quality content comes from violating general Netiquette Rules (NRs). In the past, several researchers have worked on analysing the content of SO and suggested approaches to increase its quality. However, to the best of our knowledge, there is no previous work that has reviewed the scale of scientific attention that is given to this cause and the recommendations that have been made. We have conducted a Systematic Mapping Study (SMS) using five relevant databases, reviewing 1,489 papers and selecting 18 that are relevant to help to address this gap. We have found that SO has attracted increasing research interest on reducing NRs violations to improve the quality of communication on SO. Interestingly, the majority of papers used manual qualitative and quantitative analysis approaches to investigate this area. We have found that further research is required to identify more violation features, generalisable sources of data and that the use of computational analysis approaches are still needed in this area.

Citation

Shahrour, G., De Quincey, E., & Lal, S. (2023, November). The Content Quality of Crowdsourced Knowledge on Stack Overflow- A Systematic Mapping Study. Presented at ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Kusadasi, Turkiye

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
• Start Date: Nov 6, 2023
• End Date: Nov 9, 2023
• Acceptance Date: Nov 6, 2023
• Online Publication Date: Mar 15, 2024
• Publication Date: Mar 15, 2024
• Deposit Date: Apr 12, 2024
• Journal: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• ISBN: 979-8-4007-0409-3
• DOI: https://doi.org/10.1145/3625007.3627729
• Public URL: https://keele-repository.worktribe.com/output/786947
• Publisher URL: https://dl.acm.org/doi/10.1145/3625007.3627729
• Related Public URLs: https://dl.acm.org/doi/proceedings/10.1145/3625007