{"title":"CIDFuzz:持续集成的Fuzz测试","authors":"Jiaming Zhang, Zhanqi Cui, Xiang Chen, Huiwen Yang, Liwei Zheng, Jianbin Liu","doi":"10.1049/sfw2.12125","DOIUrl":null,"url":null,"abstract":"<p>As agile software development and extreme programing have become increasingly popular, continuous integration (CI) has become a widely used collaborative work method. However, it is common to make changes frequently to a project during CI. If existing testing methods are applied to CI directly, it will be difficult to make testing resources focus on changes generated by CI, which results in insufficient testing for changes. To solve this problem, we propose a fuzz testing method for CI. First, differential analysis is performed to determine the change points generated during CI, change points are added to the taint source set, and static analysis is conducted to calculate the distances between each basic block and the taint sources. Then, the project under test is instrumented according to the distances. During fuzz testing, testing resources are allocated based on seed coverage to test the change points effectively. Using the proposed methods, we implement CIDFuzz as a prototype tool, and experiments are conducted on four open-source projects that use CI. Experimental results show that, compared with AFL and AFLGo, CIDFuzz can reduce the time costs of covering change points up to 39.59% and 41.64%, respectively. Also, CIDFuzz can reduce the time costs of reproducing vulnerabilities up to 34.78% and 25.55%.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"17 3","pages":"301-315"},"PeriodicalIF":1.5000,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2.12125","citationCount":"0","resultStr":"{\"title\":\"CIDFuzz: Fuzz testing for continuous integration\",\"authors\":\"Jiaming Zhang, Zhanqi Cui, Xiang Chen, Huiwen Yang, Liwei Zheng, Jianbin Liu\",\"doi\":\"10.1049/sfw2.12125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>As agile software development and extreme programing have become increasingly popular, continuous integration (CI) has become a widely used collaborative work method. However, it is common to make changes frequently to a project during CI. If existing testing methods are applied to CI directly, it will be difficult to make testing resources focus on changes generated by CI, which results in insufficient testing for changes. To solve this problem, we propose a fuzz testing method for CI. First, differential analysis is performed to determine the change points generated during CI, change points are added to the taint source set, and static analysis is conducted to calculate the distances between each basic block and the taint sources. Then, the project under test is instrumented according to the distances. During fuzz testing, testing resources are allocated based on seed coverage to test the change points effectively. Using the proposed methods, we implement CIDFuzz as a prototype tool, and experiments are conducted on four open-source projects that use CI. Experimental results show that, compared with AFL and AFLGo, CIDFuzz can reduce the time costs of covering change points up to 39.59% and 41.64%, respectively. Also, CIDFuzz can reduce the time costs of reproducing vulnerabilities up to 34.78% and 25.55%.</p>\",\"PeriodicalId\":50378,\"journal\":{\"name\":\"IET Software\",\"volume\":\"17 3\",\"pages\":\"301-315\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2.12125\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Software\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/sfw2.12125\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Software","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/sfw2.12125","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
As agile software development and extreme programing have become increasingly popular, continuous integration (CI) has become a widely used collaborative work method. However, it is common to make changes frequently to a project during CI. If existing testing methods are applied to CI directly, it will be difficult to make testing resources focus on changes generated by CI, which results in insufficient testing for changes. To solve this problem, we propose a fuzz testing method for CI. First, differential analysis is performed to determine the change points generated during CI, change points are added to the taint source set, and static analysis is conducted to calculate the distances between each basic block and the taint sources. Then, the project under test is instrumented according to the distances. During fuzz testing, testing resources are allocated based on seed coverage to test the change points effectively. Using the proposed methods, we implement CIDFuzz as a prototype tool, and experiments are conducted on four open-source projects that use CI. Experimental results show that, compared with AFL and AFLGo, CIDFuzz can reduce the time costs of covering change points up to 39.59% and 41.64%, respectively. Also, CIDFuzz can reduce the time costs of reproducing vulnerabilities up to 34.78% and 25.55%.
期刊介绍:
IET Software publishes papers on all aspects of the software lifecycle, including design, development, implementation and maintenance. The focus of the journal is on the methods used to develop and maintain software, and their practical application.
Authors are especially encouraged to submit papers on the following topics, although papers on all aspects of software engineering are welcome:
Software and systems requirements engineering
Formal methods, design methods, practice and experience
Software architecture, aspect and object orientation, reuse and re-engineering
Testing, verification and validation techniques
Software dependability and measurement
Human systems engineering and human-computer interaction
Knowledge engineering; expert and knowledge-based systems, intelligent agents
Information systems engineering
Application of software engineering in industry and commerce
Software engineering technology transfer
Management of software development
Theoretical aspects of software development
Machine learning
Big data and big code
Cloud computing
Current Special Issue. Call for papers:
Knowledge Discovery for Software Development - https://digital-library.theiet.org/files/IET_SEN_CFP_KDSD.pdf
Big Data Analytics for Sustainable Software Development - https://digital-library.theiet.org/files/IET_SEN_CFP_BDASSD.pdf