关键词: 缺陷定位, 多变量Logistic分析, 软件度量, 软件测试

Abstract:

Fault localization plays an important role in software development. Combining both construction features and behavior characteristics of program can benefit fault locating. A framework based on multivariate logistic regress model for locating fault in evolving software is proposed. Firstly, the feature data set is constructed by statically analyzing and tracing the program that runs with a set of designed metrics of program construction features and behavior characteristics. Meanwhile, the fault information of old version is obtained from the bug tracking system. Secondly, a univariate analysis is performed to select the metrics that are significantly related to fault, and then we train the multivariate Logistic model on the selected metrics with the constructed feature data set and the tracked fault information. Finally, based on the trained Logistic model, we conduct the multivariate logistic analysis on the feature data set of a new version of evolved software, and predict the faulty class methods. We also conduct an empirical study on a set of benchmarks. The results indicate that the multivariate Logistic model can improve the effectiveness of fault localization.

Key words: fault localization;multivariate logistic analysis;software measurement;software testing