Vineeth Kashyap, Jason Ruchti, Lucja Kot, Emma Turetsky, R. Swords, David Melski, Eric Schulte
{"title":"自动定制的bug基准生成","authors":"Vineeth Kashyap, Jason Ruchti, Lucja Kot, Emma Turetsky, R. Swords, David Melski, Eric Schulte","doi":"10.1109/SCAM.2019.00020","DOIUrl":null,"url":null,"abstract":"We introduce Bug-Injector, a system that automatically creates benchmarks for customized evaluation of static analysis tools. We share a benchmark generated using Bug-Injector and illustrate its efficacy by using it to evaluate the recall of two leading open-source static analysis tools: Clang Static Analyzer and Infer. Bug-Injector works by inserting bugs based on bug templates into real-world host programs. It runs tests on the host program to collect dynamic traces, searches the traces for a point where the state satisfies the preconditions for some bug template, then modifies the host program to \"inject\" a bug based on that template. Injected bugs are used as test cases in a static analysis tool evaluation benchmark. Every test case is accompanied by a program input that exercises the injected bug. We have identified a broad range of requirements and desiderata for bug benchmarks; our approach generates on-demand test benchmarks that meet these requirements. It also allows us to create customized benchmarks suitable for evaluating tools for a specific use case (e.g., a given codebase and set of bug types). Our experimental evaluation demonstrates the suitability of our generated benchmark for evaluating static bug-detection tools and for comparing the performance of different tools.","PeriodicalId":431316,"journal":{"name":"2019 19th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Automated Customized Bug-Benchmark Generation\",\"authors\":\"Vineeth Kashyap, Jason Ruchti, Lucja Kot, Emma Turetsky, R. Swords, David Melski, Eric Schulte\",\"doi\":\"10.1109/SCAM.2019.00020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce Bug-Injector, a system that automatically creates benchmarks for customized evaluation of static analysis tools. We share a benchmark generated using Bug-Injector and illustrate its efficacy by using it to evaluate the recall of two leading open-source static analysis tools: Clang Static Analyzer and Infer. Bug-Injector works by inserting bugs based on bug templates into real-world host programs. It runs tests on the host program to collect dynamic traces, searches the traces for a point where the state satisfies the preconditions for some bug template, then modifies the host program to \\\"inject\\\" a bug based on that template. Injected bugs are used as test cases in a static analysis tool evaluation benchmark. Every test case is accompanied by a program input that exercises the injected bug. We have identified a broad range of requirements and desiderata for bug benchmarks; our approach generates on-demand test benchmarks that meet these requirements. It also allows us to create customized benchmarks suitable for evaluating tools for a specific use case (e.g., a given codebase and set of bug types). Our experimental evaluation demonstrates the suitability of our generated benchmark for evaluating static bug-detection tools and for comparing the performance of different tools.\",\"PeriodicalId\":431316,\"journal\":{\"name\":\"2019 19th International Working Conference on Source Code Analysis and Manipulation (SCAM)\",\"volume\":\"80 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 19th International Working Conference on Source Code Analysis and Manipulation (SCAM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCAM.2019.00020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 19th International Working Conference on Source Code Analysis and Manipulation (SCAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCAM.2019.00020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We introduce Bug-Injector, a system that automatically creates benchmarks for customized evaluation of static analysis tools. We share a benchmark generated using Bug-Injector and illustrate its efficacy by using it to evaluate the recall of two leading open-source static analysis tools: Clang Static Analyzer and Infer. Bug-Injector works by inserting bugs based on bug templates into real-world host programs. It runs tests on the host program to collect dynamic traces, searches the traces for a point where the state satisfies the preconditions for some bug template, then modifies the host program to "inject" a bug based on that template. Injected bugs are used as test cases in a static analysis tool evaluation benchmark. Every test case is accompanied by a program input that exercises the injected bug. We have identified a broad range of requirements and desiderata for bug benchmarks; our approach generates on-demand test benchmarks that meet these requirements. It also allows us to create customized benchmarks suitable for evaluating tools for a specific use case (e.g., a given codebase and set of bug types). Our experimental evaluation demonstrates the suitability of our generated benchmark for evaluating static bug-detection tools and for comparing the performance of different tools.