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
Akihisa Yamada,Osamu Mizuno
Approaches to detect fault-prone modules have been studied for a long time. As one of these approaches, we proposed a technique using a text filtering technique. We assume that bugs relate to words and context that are contained in a software module. Our technique treats a module as text information. Based on the dictionary which was learned by classifying modules which induce bugs, the bug inducing prob- ability 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, we aimed at prediction with the finer gran- ularity of the portion which induces a bug. Specifically, we tried to predict bug inducing changes by using source code differences of bug inducing changes and previous changes and a text filtering technique. Similarly, we tried to bug fixing predict by using source code differences of bug fixing changes and previous changes and a text filtering technique. To show the effectiveness of our approach, we conducted two experiments and compared our approach with fault-prone filtering by applying it to two open source projects, and obtained higher accuracy.
59%, 19/32
Akihisa Yamada,Osamu Mizuno
Akihisa Yamada,Osamu Mizuno
Proc. of 12th International Conference on Software Engineering Research, Management and Applications (SERA2014)
686
8
Kitakyushu, Japan
680-686
1
IEEE CPS
A Text Filtering Based Approach to Classify Bug Injected and Fixed Changes
DOI 10.1109/IIAI-AAI.2014.141
2014