Quantitative Analysis of Fault and Failure Using Software Metrics
It is very complex to write programs that behave accurately in the program verification tools. Automatic mining techniques suffer from 90–99% false positive rates, because manual specification writing is not easy. Because they can help with program testing, optimization, refactoring, documentation, and most importantly, debugging and repair. To concentrate on 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 probable to be correct as well as code that is less probable to be correct. When used as a preprocessing step for an existing specification miner, our technique identifies which input is most suggestive of correct program behaviour, which allows off-the-shelf techniques to learn the same number of specifications using only 45% of their original input.
