|Table of Contents|

A Cross Project Defect Prediction Method for Source Project Training Data Selection(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2022年01期
Page:
110-117
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
A Cross Project Defect Prediction Method for Source Project Training Data Selection
Author(s):
Gai Jinjing; Zheng Shang; Yu Hualong; Gao Shang
(School of Computer,Jiangsu University of Science and Technology,Zhenjiang 212100,China)
Keywords:
cross project; defect prediction; software quality; data selection; JS divergence; relative density; mixed Gaussian model; Monte Carlo
PACS:
TP311.5
DOI:
10.3969/j.issn.1001-4616.2022.01.016
Abstract:
In real software development,a project which needs defect prediction is always new or without any historical data. It is necessary to use training data from several projects and performs prediction on another one. Therefore,cross project defect prediction(CPDP)has become a means of software quality assurance and been studied by researchers. However,the performance of prediction model can be directly affected by training data. In order to solve the problem,a cross-project software defect prediction method based on Jensen-Shannon divergence and relative density is proposed in this paper. Firstly,the Gaussian mixture model(GMM)is applied to the source and target projects respectively,and then the JS divergence between the target and all candidate projects is calculated by Monte Carlo method. Secondly,according to the obtained JS divergence,the source project that is most similar to the target project is selected. Thirdly,the concept of relative density is proposed to improve the training data quality of selected source project. Finally,some common classifiers are used to build the prediction model. Experimental results show that the proposed method can not only improve the performance of the prediction model,but also show high adaptability to different classifiers.

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