Effort-Aware semi-Supervised just-in-Time defect prediction

2020 ◽  
Vol 126 ◽  
pp. 106364
Author(s):  
Weiwei Li ◽  
Wenzhou Zhang ◽  
Xiuyi Jia ◽  
Zhiqiu Huang
Keyword(s):  
Defect Prediction ◽  
Just In Time

scholarly journals Fine-grained just-in-time defect prediction

2019 ◽  
Vol 150 ◽  
pp. 22-36 ◽  
Author(s):  
Luca Pascarella ◽  
Fabio Palomba ◽  
Alberto Bacchelli
Keyword(s):  
Defect Prediction ◽  
Just In Time ◽  
Fine Grained

A Novel Effort Measure Method for Effort-Aware Just-in-Time Software Defect Prediction

2021 ◽  
Vol 31 (08) ◽  
pp. 1145-1169
Author(s):  
Liqiong Chen ◽  
Shilong Song ◽  
Can Wang
Keyword(s):  
Prediction Model ◽  
Defect Prediction ◽  
Software Systems ◽  
Support Vector ◽  
Just In Time ◽  
Software Defect Prediction ◽  
Fine Grained ◽  
Software Defect ◽  
Code Changes ◽  
The Cost

Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology, which aims to identify the defective code changes in software systems. Effort-aware software defect prediction is a software defect prediction technology that takes into consideration the cost of code inspection, which can find more defective code changes in limited test resources. The traditional effort-aware defect prediction model mainly measures the effort based on the number of lines of code (LOC) and rarely considers additional factors. This paper proposes a novel effort measure method called Multi-Metric Joint Calculation (MMJC). When measuring the effort, MMJC takes into account not only LOC, but also the distribution of modified code across different files (Entropy), the number of developers that changed the files (NDEV) and the developer experience (EXP). In the simulation experiment, MMJC is combined with Linear Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machine and Neural Network, respectively, to build the software defect prediction model. Several comparative experiments are conducted between the models based on MMJC and baseline models. The results show that indicators ACC and [Formula: see text] of the models based on MMJC are improved by 35.3% and 15.9% on average in the three verification scenarios, respectively, compared with the baseline models.

The impact of context metrics on just-in-time defect prediction

2019 ◽  
Vol 25 (1) ◽  
pp. 890-939 ◽  
Author(s):  
Masanari Kondo ◽  
Daniel M. German ◽  
Osamu Mizuno ◽  
Eun-Hye Choi
Keyword(s):  
Defect Prediction ◽  
Just In Time ◽  
The Impact
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