{"title":"线点的实证研究:一种新的过去故障算法","authors":"Maximilian Scholz, Richard Torkar","doi":"10.1002/stvr.1787","DOIUrl":null,"url":null,"abstract":"This paper proposes the novel past‐faults fault prediction algorithm Linespots, based on the Bugspots algorithm. We analyse the predictive performance and runtime of Linespots compared with Bugspots with an empirical study using the most significant self‐built dataset as of now, including high‐quality samples for validation. As a novelty in fault prediction, we use Bayesian data analysis and Directed Acyclic Graphs to model the effects. We found consistent improvements in the predictive performance of Linespots over Bugspots for all seven evaluation metrics. We conclude that Linespots should be used over Bugspots in all cases where no real‐time performance is necessary.","PeriodicalId":49506,"journal":{"name":"Software Testing Verification & Reliability","volume":"1 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"An empirical study of Linespots: A novel past‐fault algorithm\",\"authors\":\"Maximilian Scholz, Richard Torkar\",\"doi\":\"10.1002/stvr.1787\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes the novel past‐faults fault prediction algorithm Linespots, based on the Bugspots algorithm. We analyse the predictive performance and runtime of Linespots compared with Bugspots with an empirical study using the most significant self‐built dataset as of now, including high‐quality samples for validation. As a novelty in fault prediction, we use Bayesian data analysis and Directed Acyclic Graphs to model the effects. We found consistent improvements in the predictive performance of Linespots over Bugspots for all seven evaluation metrics. We conclude that Linespots should be used over Bugspots in all cases where no real‐time performance is necessary.\",\"PeriodicalId\":49506,\"journal\":{\"name\":\"Software Testing Verification & Reliability\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2020-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Testing Verification & Reliability\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1002/stvr.1787\",\"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":"Software Testing Verification & Reliability","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/stvr.1787","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}
An empirical study of Linespots: A novel past‐fault algorithm
This paper proposes the novel past‐faults fault prediction algorithm Linespots, based on the Bugspots algorithm. We analyse the predictive performance and runtime of Linespots compared with Bugspots with an empirical study using the most significant self‐built dataset as of now, including high‐quality samples for validation. As a novelty in fault prediction, we use Bayesian data analysis and Directed Acyclic Graphs to model the effects. We found consistent improvements in the predictive performance of Linespots over Bugspots for all seven evaluation metrics. We conclude that Linespots should be used over Bugspots in all cases where no real‐time performance is necessary.
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