Conference proceeding
Automatic identification of informative code in stack overflow posts
Proceedings of the 1st International Workshop on Natural Language-based Software Engineering, pp 21-24
21 May 2022
Abstract
Despite Stack Overflow's popularity as a resource for solving coding problems, identifying relevant information from an individual post remains a challenge. The overload of information in a post can make it difficult for developers to identify specific and targeted code fixes. In this paper, we aim to help users identify informative code segments, once they have narrowed down their search to a post relevant to their task. Specifically, we explore natural language-based approaches to extract problematic and suggested code pairs from a post. The goal of the study is to investigate the potential of designing a browser extension to draw the readers' attention to relevant code segments, and thus improve the experience of software engineers seeking help on Stack Overflow.
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Details
- Title
- Automatic identification of informative code in stack overflow posts
- Creators
- Preetha Chatterjee - Drexel University
- Publication Details
- Proceedings of the 1st International Workshop on Natural Language-based Software Engineering, pp 21-24
- Conference
- ICSE '22: 44th International Conference on Software Engineering
- Series
- ACM Conferences
- Publisher
- ACM
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000853491600004
- Scopus ID
- 2-s2.0-85135165319
- Other Identifier
- 991021883914604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Software Engineering