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Committee Date Time Place Paper Title / Authors Abstract Paper #
R 2019-06-14
15:30
Tokyo Kikai-Shinko-Kaikan Bldg. Identification comparison of software fault-prone modules using nonlinear logistic regression models
Kazunari Yamanaka, Tadashi Dohi, Hiroyuki Okamura (Hiroshima U.) R2019-12
In this article, we compare several non-linear logistic regression models used in a fault-prone
identification problem... [more]
R2019-12
pp.19-24
KBSE 2009-01-20
13:15
Tokyo Kikai-Shinko-Kaikan Bldg. B3-6 A Comparative Study of Fault-Prone Module Detection Methods -- Fault-proneness filtering and Logistic regression --
Liu Hua Hao, Osamu Mizuno, Tohru Kikuno (Osaka Univ.) KBSE2008-47
Prediction of fault-prone software modules has been one of the most
classical and important area of software engineer... [more]
KBSE2008-47
pp.61-66
KBSE, SS 2008-05-29
15:15
Miyazaki Miyazaki Citizens' Plaza Identifying Fault-Prone Tokens in Source Code Modules with Spam-Filtering Technique
Ryosuke Morii, Osamu Mizuno, Tohru Kikuno (Osaka Univ.) SS2008-4 KBSE2008-4
Prediction of fault-prone(FP) software modules has been one of the important area of software testing and many approache... [more] SS2008-4 KBSE2008-4
pp.19-24
SS 2007-02-01
15:10
Aichi Aichi Prefectural Univ. Prediction of fault prone source code modules using text classifier
Shiro Ikami, Shuya Nakaichi, Osamu Mizuno, Tohru Kikuno (Osaka Univ.)
For the improvement of the quality of product, it is important to detect fault-prone modules in source code at an early ... [more] SS2006-75
pp.25-30
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