Harvesting Fix Hints in the History of Bugs
Abstract
In software development, fixing bugs is an important task that is time consuming and cost-sensitive. While many approaches have been proposed to automatically detect and patch software code, the strategies are limited to a set of identified bugs that were thoroughly studied to define their properties. They thus manage to cover a niche of faults such as infinite loops. We build on the assumption that bugs, and the associated user bug reports, are repetitive and propose a new approach of fix recommendations based on the history of bugs and their associated fixes. In our approach, once a bug is reported, it is automatically compared to all previously fixed bugs using information retrieval techniques and machine learning classification. Based on this comparison, we recommend top-{\em k} fix actions, identified from past fix examples, that may be suitable as hints for software developers to address the new bug.
Keywords
Cite
@article{arxiv.1507.05742,
title = {Harvesting Fix Hints in the History of Bugs},
author = {Tegawendé F. Bissyandé},
journal= {arXiv preprint arXiv:1507.05742},
year = {2015}
}
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6 pages