Continuous Software Bug Prediction
Song Wang, Junjie Wang, Jaechang Nam, Nachiappan Nagappan
Abstract
Background: Many software bug prediction models have been proposed and evaluated on a set of well-known benchmark datasets. We conducted pilot studies on the widely used benchmark datasets and observed common issues among them. Specifically, most of existing benchmark datasets consist of randomly selected historical versions of software projects, which poses non-trivial threats to the validity of existing bug prediction studies since the real-world software projects often evolve continuously. Yet how to conduct software bug prediction in the real-world continuous software development scenarios is not well studied.
Topics & Concepts
Benchmark (surveying)Computer scienceSoftware bugSoftwareSoftware regressionSet (abstract data type)Data miningVerification and validationSoftware metricSoftware developmentSoftware evolutionSoftware engineeringMachine learningSoftware qualitySoftware constructionProgramming languageEngineeringGeodesyGeographyOperations managementSoftware Engineering ResearchSoftware Reliability and Analysis ResearchSoftware Engineering Techniques and Practices