A goal of performance testing is to find situations when applications unexpectedly exhibit worsened characteristics for certain combinations of input values. A fundamental question of performance testing is how to select a manageable subset of the input data faster to find performance problems in applications automatically. We present a novel tool, FOREPOST, for finding performance problems in applications automatically using black-box software testing. In this paper, we demonstrate how FOREPOST extracts rules from execution traces of applications by using machine learning algorithms, and then uses these rules to select test input data automatically to steer applications towards computationally intensive paths and to find performance problems. FOREPOST is available in our online appendix (http://www.cs.wm.edu/semeru/data/ICSE16-FOREPOST), which contains the tool, source code and demo video.
{"title":"FOREPOST","authors":"Q. Luo, D. Poshyvanyk, A. Nair, M. Grechanik","doi":"10.1145/2889160.2889164","DOIUrl":"https://doi.org/10.1145/2889160.2889164","url":null,"abstract":"A goal of performance testing is to find situations when applications unexpectedly exhibit worsened characteristics for certain combinations of input values. A fundamental question of performance testing is how to select a manageable subset of the input data faster to find performance problems in applications automatically. We present a novel tool, FOREPOST, for finding performance problems in applications automatically using black-box software testing. In this paper, we demonstrate how FOREPOST extracts rules from execution traces of applications by using machine learning algorithms, and then uses these rules to select test input data automatically to steer applications towards computationally intensive paths and to find performance problems. FOREPOST is available in our online appendix (http://www.cs.wm.edu/semeru/data/ICSE16-FOREPOST), which contains the tool, source code and demo video.","PeriodicalId":111740,"journal":{"name":"Proceedings of the 38th International Conference on Software Engineering Companion","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115839019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present RDIT, a novel dynamic algorithm to precisely detect data races in multi-threaded programs with incomplete trace information - the presence of missing events. RDIT enhances the Happens-Before algorithm by relaxing the need to collect the full execution trace, while still being precise and maximal i.e, it detects a maximal set of true data races while generating no false positives. Our approach is based on a sound BarrierPair model that abstracts away missing events by capturing the invocation data of their enclosing methods. By making the least conservative abstraction and by formulating maximal thread causality as logical constraints, we can detect a maximal set of true races from the information available.
{"title":"RDIT: race detection from incomplete traces","authors":"Arun K. Rajagopalan","doi":"10.1145/2889160.2891039","DOIUrl":"https://doi.org/10.1145/2889160.2891039","url":null,"abstract":"We present RDIT, a novel dynamic algorithm to precisely detect data races in multi-threaded programs with incomplete trace information - the presence of missing events. RDIT enhances the Happens-Before algorithm by relaxing the need to collect the full execution trace, while still being precise and maximal i.e, it detects a maximal set of true data races while generating no false positives. Our approach is based on a sound BarrierPair model that abstracts away missing events by capturing the invocation data of their enclosing methods. By making the least conservative abstraction and by formulating maximal thread causality as logical constraints, we can detect a maximal set of true races from the information available.","PeriodicalId":111740,"journal":{"name":"Proceedings of the 38th International Conference on Software Engineering Companion","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129811491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 38th International Conference on Software Engineering Companion","authors":"","doi":"10.1145/2889160","DOIUrl":"https://doi.org/10.1145/2889160","url":null,"abstract":"","PeriodicalId":111740,"journal":{"name":"Proceedings of the 38th International Conference on Software Engineering Companion","volume":"52 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131874276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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