Masanari Kondo,Cor-Paul Bezemer,Yasutaka Kamei,Ahmed E. Hassan,Osamu Mizuno
Masanari Kondo,Cor-Paul Bezemer,Yasutaka Kamei,Ahmed E. Hassan,Osamu Mizuno
Masanari Kondo,Cor-Paul Bezemer,Yasutaka Kamei,Ahmed E. Hassan,Osamu Mizuno
782
4.457 (2019)
Empirical Software Engineering
0
4
1925–1963
0
The Impact of Feature Reduction Techniques on Defect Prediction Models
https://doi.org/10.1007/s10664-018-9679-5
24
2019
Osamu Mizuno,Hideaki Hata
In order to assure the quality of software product, early detection of fault-prone products is necessary. Fault-prone module detection is one of the major and traditional area of software engineering. However, comparative study using the fair environment rarely conducted so far because there is little data publicly available. This paper tries to conduct a comparative study of fault-prone module detection approaches.
Osamu Mizuno,Hideaki Hata
Osamu Mizuno,Hideaki Hata
Proc. of 34th Annual IEEE Computer Software and Applications Conference (COMPSAC2010)
615
7
Seoul, Korea
248-249
1
An Empirical Comparison of Fault-prone Module Detection Approaches: Complexity Metrics and Text Feature Metrics
2010
Osamu Mizuno,Hideaki Hata
Earlydetectionoffault-proneproductsisnecessarytoassurethequal- ity of software product. Therefore, fault-prone module detection is one of the major and traditional area of software engineering. Although there are many ap- proaches to detect fault-prone modules, they have their own pros and cons. Conse- quently, it is recommended to use appropriate approach on the various situations. This paper tries to show an integrated approach using two different fault-prone module detection approaches.
To do so, we prepare two approaches of fault-prone module detection: a text feature metrics based approach using naive Bayes classifier and a complexity metrics based approach using logistic regression. The former one is proposed by us and the latter one is widely used approach. For the data for application, we used data obtained from Eclipse, which is publicly available.
From the result of pre-experiment, we find that each approach has the pros and cons. That is, the text feature based approach has high recall, and complexity metrics based approach has high precision. In order to use their merits effectively, we proposed an integrated approach to apply these two approaches for fault-prone module detection. The result of experiment shows that the proposed approach shows better accuracy than each approach.
Osamu Mizuno,Hideaki Hata
Osamu Mizuno,Hideaki Hata
Proc. of 2010 International Conference on Advanced Science and Technology (AST2010)
610
6
Miyazaki, Japan
457-468
1
An Integrated Approach to Detect Fault-Prone Modules using Complexity and Text Feature Metrics
LNCS 6059
2010
Masanari Kondo,Cor-Paul Bezemer,Yasutaka Kamei,Ahmed E. Hassan,Osamu Mizuno
Masanari Kondo,Cor-Paul Bezemer,Yasutaka Kamei,Ahmed E. Hassan,Osamu Mizuno
Masanari Kondo,Cor-Paul Bezemer,Yasutaka Kamei,Ahmed E. Hassan,Osamu Mizuno
Proc. of International Conference on Software Engineering (ICSE2020), Journal first track
881
9
12
The Impact of Feature Reduction Techniques on Defect Prediction Models
2020
Masanari Kondo
Masanari Kondo
Masanari Kondo
911
12
7
Kyoto Institute of Technology
An Empirical Study of Feature Engineering on Software Defect Prediction
2020
Masanari Kondo
Masanari Kondo
Masanari Kondo
798
2
7
Graduate School of Science and Technology, Kyoto Institute of Technology
The Impact of Feature Reduction Techniques on Defect Prediction Models
The Impact of Feature Reduction Techniques on Defect Prediction Models
2019