MULTI: Multi-objective effort-aware just-in-time software defect prediction

2018 ◽  
Vol 93 ◽  
pp. 1-13 ◽  
Author(s):  
Xiang Chen ◽  
Yingquan Zhao ◽  
Qiuping Wang ◽  
Zhidan Yuan
Keyword(s):  
Defect Prediction ◽  
Just In Time ◽  
Software Defect Prediction ◽  
Multi Objective ◽  
Software Defect

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.

A Multi-objective Bat Algorithm for Software Defect Prediction

2020 ◽  
pp. 269-283
Author(s):  
Di Wu ◽  
Jiangjiang Zhang ◽  
Shaojin Geng ◽  
Xingjuan Cai ◽  
Guoyou Zhang
Keyword(s):  
Bat Algorithm ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Multi Objective ◽  
Software Defect

An empirical study on pareto based multi-objective feature selection for software defect prediction

2019 ◽  
Vol 152 ◽  
pp. 215-238 ◽  
Author(s):  
Chao Ni ◽  
Xiang Chen ◽  
Fangfang Wu ◽  
Yuxiang Shen ◽  
Qing Gu
Keyword(s):  
Feature Selection ◽  
Empirical Study ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Multi Objective ◽  
Software Defect ◽  
Selection For

Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction

2018 ◽  
Vol 9 (6) ◽  
pp. 547
Author(s):  
Jianwei Li ◽  
Xingjuan Cai ◽  
Zeyu Tian ◽  
Maoqing Zhang ◽  
Yun Niu
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Multi Objective ◽  
Software Defect

scholarly journals Local versus Global Models for Just-In-Time Software Defect Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Xingguang Yang ◽  
Huiqun Yu ◽  
Guisheng Fan ◽  
Kai Shi ◽  
Liqiong Chen
Keyword(s):  
Cross Validation ◽  
Prediction Models ◽  
Prediction Performance ◽  
Defect Prediction ◽  
Just In Time ◽  
Software Defect Prediction ◽  
Local Models ◽  
Global Models ◽  
Software Defect ◽  
Cross Project

Just-in-time software defect prediction (JIT-SDP) is an active topic in software defect prediction, which aims to identify defect-inducing changes. Recently, some studies have found that the variability of defect data sets can affect the performance of defect predictors. By using local models, it can help improve the performance of prediction models. However, previous studies have focused on module-level defect prediction. Whether local models are still valid in the context of JIT-SDP is an important issue. To this end, we compare the performance of local and global models through a large-scale empirical study based on six open-source projects with 227417 changes. The experiment considers three evaluation scenarios of cross-validation, cross-project-validation, and timewise-cross-validation. To build local models, the experiment uses the k-medoids to divide the training set into several homogeneous regions. In addition, logistic regression and effort-aware linear regression (EALR) are used to build classification models and effort-aware prediction models, respectively. The empirical results show that local models perform worse than global models in the classification performance. However, local models have significantly better effort-aware prediction performance than global models in the cross-validation and cross-project-validation scenarios. Particularly, when the number of clusters k is set to 2, local models can obtain optimal effort-aware prediction performance. Therefore, local models are promising for effort-aware JIT-SDP.

Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction

2018 ◽  
Vol 9 (6) ◽  
pp. 547 ◽  
Author(s):  
Yun Niu ◽  
Zeyu Tian ◽  
Maoqing Zhang ◽  
Xingjuan Cai ◽  
Jianwei Li
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Multi Objective ◽  
Software Defect
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