Akihisa Yamada,Osamu Mizuno
Approaches to detect fault-prone modules have been studied for a long time. As one of these approaches, the authors proposed a technique using a text filtering technique. They assume that bugs relate to words and context that are contained in a software module. The technique treats a module as text information. Based on the dictionary which was learned by classifying modules which induce bugs, the bug inducing probability over a target module is calculated, and it judges whether the given module is a fault-prone module. The predictive granularity of this technique is a module. In this study, the authors aimed at prediction with the finer granularity of the portion that induces a bug. Specifically, they tried to predict bug-inducing changes by using source code differences of bug inducing changes and previous changes and a text filtering technique. Similarly, the authors tried to predict bug fixing by using source code differences of bug fixing changes and previous changes and a text filtering technique. To show the effectiveness of the approach, the authors conducted two experiments and compared their approach with fault-prone filtering by applying it to two open source projects, and obtained higher accuracy.
Akihisa Yamada,Osamu Mizuno
Akihisa Yamada,Osamu Mizuno
689
ACIS International Journal of Software Innovation
1
1
50-62
0
Classification of Bug Injected and Fixed Changes Using a Text Discriminator
DOI: 10.4018/ijsi.2015010104
3
2015
Osamu Mizuno,Tohru Kikuno
This paper describes a novel approach for detecting fault-prone modules using a spam filtering
technique. Fault-prone module detection in source code is important for the assurance of software
quality. Most previous fault-prone detection approaches have been based on using software metrics.
Such approaches, however, have difficulties in collecting the metrics and constructing mathematical
models based on the metrics. Because of the increase in the need for spam e-mail detection, the
spam filtering technique has progressed as a convenient and effective technique for text mining. In
our approach, fault-prone modules are detected in such a way that the source code modules are
considered text files and are applied to the spam filter directly. To show the applicability of our
approach, we conducted experimental applications using source code repositories of Java based open
source developments. The result of experiments shows that our approach can correctly predict 78% of
actual fault-prone modules as fault-prone.
(C) 2008, IEICE.
Osamu Mizuno,Tohru Kikuno
Osamu Mizuno,Tohru Kikuno
524
0.369 (2008)
IEICE Trans. on Information and Systems
4
4
888-896
0
Prediction of Fault-Prone Software Modules Using a Generic Text Discriminator
http://www.ieice.org/jpn/trans_online/index.html
E91-D
2008