{"title":"A failed proof can yield a useful test","authors":"Li Huang, Bertrand Meyer","doi":"10.1002/stvr.1859","DOIUrl":null,"url":null,"abstract":"Abstract A successful automated program proof is, in software verification, the ultimate triumph. In practice, however, the road to such success is paved with many failed proof attempts. Unlike a failed test, which provides concrete evidence of an actual bug in the program, a failed proof leaves the programmer in the dark. Can we instead learn something useful from it? The work reported here takes advantage of the rich information that some automatic provers internally collect about the program when attempting a proof. If the proof fails, the Proof2Test tool presented in this article uses the counterexample generated by the prover (specifically, the SMT solver underlying the Boogie tool used in the AutoProof system to perform correctness proofs of contract‐equipped Eiffel programs) to produce a failed test, which provides the programmer with immediately exploitable information to correct the program. The discussion presents Proof2Test and the application of the ideas and tool to a collection of representative examples.","PeriodicalId":49506,"journal":{"name":"Software Testing Verification & Reliability","volume":"2 1","pages":"0"},"PeriodicalIF":1.5000,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Testing Verification & Reliability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/stvr.1859","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}
引用次数: 1
Abstract
Abstract A successful automated program proof is, in software verification, the ultimate triumph. In practice, however, the road to such success is paved with many failed proof attempts. Unlike a failed test, which provides concrete evidence of an actual bug in the program, a failed proof leaves the programmer in the dark. Can we instead learn something useful from it? The work reported here takes advantage of the rich information that some automatic provers internally collect about the program when attempting a proof. If the proof fails, the Proof2Test tool presented in this article uses the counterexample generated by the prover (specifically, the SMT solver underlying the Boogie tool used in the AutoProof system to perform correctness proofs of contract‐equipped Eiffel programs) to produce a failed test, which provides the programmer with immediately exploitable information to correct the program. The discussion presents Proof2Test and the application of the ideas and tool to a collection of representative examples.
期刊介绍:
The journal is the premier outlet for research results on the subjects of testing, verification and reliability. Readers will find useful research on issues pertaining to building better software and evaluating it.
The journal is unique in its emphasis on theoretical foundations and applications to real-world software development. The balance of theory, empirical work, and practical applications provide readers with better techniques for testing, verifying and improving the reliability of software.
The journal targets researchers, practitioners, educators and students that have a vested interest in results generated by high-quality testing, verification and reliability modeling and evaluation of software. Topics of special interest include, but are not limited to:
-New criteria for software testing and verification
-Application of existing software testing and verification techniques to new types of software, including web applications, web services, embedded software, aspect-oriented software, and software architectures
-Model based testing
-Formal verification techniques such as model-checking
-Comparison of testing and verification techniques
-Measurement of and metrics for testing, verification and reliability
-Industrial experience with cutting edge techniques
-Descriptions and evaluations of commercial and open-source software testing tools
-Reliability modeling, measurement and application
-Testing and verification of software security
-Automated test data generation
-Process issues and methods
-Non-functional testing