Can Faulty Modules Be Predicted by Warning Messages of Static Code Analyzer?

Osamu Mizuno,Michi Nakai

doi:10.1155/2012/924923

Abstract

We have proposed a detection method of fault-prone modules based on the spam filtering technique, “Fault-prone filtering.” Fault-prone filtering is a method which uses the text classifier (spam filter) to classify source code modules in software. In this study, we propose an extension to use warning messages of a static code analyzer instead of raw source code. Since such warnings include useful information to detect faults, it is expected to improve the accuracy of fault-prone module prediction. From the result of experiment, it is found that warning messages of a static code analyzer are a good source of fault-prone filtering as the original source code. Moreover, it is discovered that it is more effective than the conventional method (that is, without static code analyzer) to raise the coverage rate of actual faulty modules.

Highlights

Machine learning approaches have been widely used for fault-proneness detection [1]
We construct a large metrics set representing the frequency of words in source code modules
Since less effort or cost needed to collect text feature metrics than other software metrics, it may be applied to software development projects

Summary

Introduction

Machine learning approaches have been widely used for fault-proneness detection [1]. We have introduced a text feature-based approach to detect fault-prone modules [2]. In this approach, we extract text features from the frequency information of words in source code modules. We construct a large metrics set representing the frequency of words in source code modules. Once the text features are obtained, the Bayesian classifier is constructed from text features. In the fault-prone module detection of new modules, we extract text features from source code modules, and Bayesian model classifies modules into either faultprone or nonfault-prone. Since less effort or cost needed to collect text feature metrics than other software metrics, it may be applied to software development projects

Methods

Results

Conclusion