Software Defect Prediction by Online Learning Considering Defect Overlooking

被引：0

作者：

Yamasaki, Yuta ^{[1
]}

Fedorov, Nikolay ^{[2
]}

Tsunoda, Masateru ^{[1
]}

Monden, Akito ^{[3
]}

Tahir, Amjed ^{[4
]}

Bennin, Kwabena Ebo ^{[5
]}

Toda, Koji ^{[6
]}

Nakasai, Keitaro ^{[7
]}

机构：

[1] Kindai Univ, Higashiosaka, Osaka, Japan

[2] Dubna State Univ, Dubna, Russia

[3] Okayama Univ, Okayama, Japan

[4] Massey Univ, Palmerston North, New Zealand

[5] Wageningen UR, Wageningen, Netherlands

[6] Fukuoka Inst Technol, Fukuoka, Japan

[7] OMU Coll Technol, Osaka, Japan

来源：

2023 IEEE 34TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING WORKSHOPS, ISSREW | 2023年

关键词：

defect prediction; cross-version defect prediction;

D O I：

10.1109/ISSREW60843.2023.00044

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Building defect prediction models based on online learning can enhance prediction accuracy. It continuously rebuilds a new prediction model when adding a new data point. However, predicting a module as "non-defective" (i.e., negative prediction) can result in fewer test cases for such modules. Therefore, defects can be overlooked during testing, even when the module is defective. The erroneous test results are used as learning data by online learning, which could negatively affect prediction accuracy. In our experiment, we demonstrate this negative influence on prediction accuracy.

引用

页码：43 / 44

页数：2

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