Bug Report Enrichment with Application of Automated Fixer Recommendation

For large open source projects (e.g., Eclipse, Mozilla), developers usually utilize bug reports to facilitate software maintenance tasks such as fixer assignment. However, there are a large portion of short reports in bug repositories. We find that 78.1% of bug reports only include less than 100 words in Eclipse and require bug fixers to spend more time on resolving them due to limited informative contents. To address this problem, in this paper, we propose a novel approach to enrich bug reports. Concretely, we design a sentence ranking algorithm based on a new textual similarity metric to select the proper contents for bug report enrichment. For the enriched bug reports, we conduct a user study to assess whether the additional sentences can provide further help to fixer assignment. Moreover, we assess whether the enriched versions can improve the performance of automated fixer recommendation. In particular, we perform three popular automated fixer recommendation approaches on the enriched bug reports of Eclipse, Mozilla, and GNU Compiler Collection (GCC). The experimental results show that enriched bug reports improve the average F-measure scores of the automated fixer recommendation approaches by up to 10% for DRETOM, and 8% for DevRec when top-10 bug fixers are recommended.