考虑控制流的复杂网络软件缺陷预测

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI:10.1109/QRS-C57518.2022.00044

Zhanyi Hou, Ling-lin Gong, Minghao Yang, Yizhuo Zhang, Shunkun Yang

{"title":"考虑控制流的复杂网络软件缺陷预测","authors":"Zhanyi Hou, Ling-lin Gong, Minghao Yang, Yizhuo Zhang, Shunkun Yang","doi":"10.1109/QRS-C57518.2022.00044","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"82 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Software Bug Prediction based on Complex Network Considering Control Flow\",\"authors\":\"Zhanyi Hou, Ling-lin Gong, Minghao Yang, Yizhuo Zhang, Shunkun Yang\",\"doi\":\"10.1109/QRS-C57518.2022.00044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":183728,\"journal\":{\"name\":\"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)\",\"volume\":\"82 5\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/QRS-C57518.2022.00044\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/QRS-C57518.2022.00044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

软件bug数量的预测对软件质量管理和软件测试具有重要的指导意义。本文提出了一种考虑控制流的基于复杂网络的软件缺陷数预测方法。首先，对于每个版本的软件，我们构建了调用图(Call Graph, CG)，对于每个版本，我们构建了每个功能的控制流图(Control Flow Graph, CFG)。然后利用复杂网络科学的指标，计算各版本的CG Metrics (CGM)和CFG Metrics (CFGM)。最后，将结果发送给面板数据模型(PDM)对bug固定数量进行预测。实验结果表明，该方法的预测准确率比其他方法高9.35% ~ 16.85%，其中CFGM的引入比不使用CGM的MAE降低了5.1% ~ 27.8%。修正错误的预测可以反映软件的质量，辅助软件工程的质量控制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Software Bug Prediction based on Complex Network Considering Control Flow

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)

自引率

0.00%

发文量