Software Bug Prediction based on Complex Network Considering Control Flow

被引：0

作者：

Hou, Zhanyi ^{[1
]}

Gong, LingLin ^{[2
]}

Yang, Minghao ^{[1
]}

Zhang, Yizhuo ^{[1
]}

Yang, Shunkun ^{[1
]}

机构：

[1] Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China

[2] China Elect Power Res Inst, 15 East Xiaoying Rd, Beijing, Peoples R China

来源：

2022 IEEE 22ND INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY, AND SECURITY COMPANION, QRS-C | 2022年

关键词：

Bug Prediction; Complex Network; Control Flow Graph; Panel Data Model; RELIABILITY; MODEL;

D O I：

10.1109/QRS-C57518.2022.00044

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The prediction for software bug number provides vital guidance to the quality management and software testing. In this paper, a novel software bug number prediction method was proposed based on complex network considering control flow. Firstly, for each release of software, we constructed the Call Graph (CG), and for each release, Control Flow Graph (CFG) of every function were constructed. Then the CG Metrics (CGM) and CFG Metrics (CFGM) for each version were calculated with indicators from complex-network science. Finally, the results were sent to Panel Data Model (PDM) to perform the prediction on bugs fixed number. The experimental result showed that our method outperformed other prediction methods by 9.35% to 16.85%, and introducing CFGM reduced MAE by 5.1% to 27.8% than barely use CGM. The prediction of fixed bugs could indicate the software quality, and assist the quality control of software engineering.

引用

页码：246 / 254

页数：9

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