{"title":"PredSym: estimating software testing budget for a bug-free release","authors":"Arnamoy Bhattacharyya, Timur Malgazhdarov","doi":"10.1145/2994291.2994294","DOIUrl":null,"url":null,"abstract":"Symbolic execution tools are widely used during a software testing phase for finding hidden bugs and software vulnerabilities. Accurately predicting the time required by a symbolic execution tool to explore a chosen code coverage helps in planning the budget required in the testing phase. In this work, we present an automatic tool, PredSym, that uses static program features to predict the coverage explored by a symbolic execution tool - KLEE, for a given time budget and to predict the time required to explore a given coverage. PredSym uses LASSO regression to build a model that does not suffer from overfitting and can predict both the coverage and the time with a worst error of 10% on unseen datapoints. PredSym also gives code improvement suggestions based on a heuristic for improving the coverage generated by KLEE.","PeriodicalId":255079,"journal":{"name":"Proceedings of the 7th International Workshop on Automating Test Case Design, Selection, and Evaluation","volume":"363 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th International Workshop on Automating Test Case Design, Selection, and Evaluation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2994291.2994294","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Symbolic execution tools are widely used during a software testing phase for finding hidden bugs and software vulnerabilities. Accurately predicting the time required by a symbolic execution tool to explore a chosen code coverage helps in planning the budget required in the testing phase. In this work, we present an automatic tool, PredSym, that uses static program features to predict the coverage explored by a symbolic execution tool - KLEE, for a given time budget and to predict the time required to explore a given coverage. PredSym uses LASSO regression to build a model that does not suffer from overfitting and can predict both the coverage and the time with a worst error of 10% on unseen datapoints. PredSym also gives code improvement suggestions based on a heuristic for improving the coverage generated by KLEE.
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