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Measuring Code Quality to Improve Specification Mining
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DOI：10.1109/TSE.2011.5.png)
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Abstract
En 中文
Formal specifications can help with program testing, optimization, refactoring, documentation, and, most importantly, debugging and repair. However, they are difficult to write manually, and automatic mining techniques suffer from 90-99 percent false positive rates. To address this problem, we propose to augment a temporal-property miner by incorporating code quality metrics. We measure code quality by extracting additional information from the software engineering process and using information from code that is more likely to be correct, as well as code that is less likely to be correct. When used as a preprocessing step for an existing specification miner, our technique identifies which input is most indicative of correct program behavior, which allows off-the-shelf techniques to learn the same number of specifications using only 45 percent of their original input. As a novel inference technique, our approach has few false positives in practice (63 percent when balancing precision and recall, 3 percent when focused on precision), while still finding useful specifications (e.g., those that find many bugs) on over 1.5 million lines of code.
Keywords:
Specification mining
machine learning
software engineering
code metrics
program understanding
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