Predicting Software Performance with Divide-and-Learn
Jingzhi Gong, Tao Chen
Abstract
Predicting the performance of highly configurable software systems is the foundation for performance testing and quality assurance. To that end, recent work has been relying on machine/deep learning to model software performance. However, a crucial yet unaddressed challenge is how to cater for the sparsity inherited from the configuration landscape: the influence of configuration options (features) and the distribution of data samples are highly sparse.
Topics & Concepts
Computer scienceSoftware quality assuranceSoftwareSoftware engineeringSoftware qualitySoftware systemMachine learningSearch-based software engineeringDeep learningQuality assuranceArtificial intelligenceSoftware constructionSoftware developmentEngineeringProgramming languageOperations managementExternal quality assessmentSoftware System Performance and ReliabilitySoftware Engineering ResearchSoftware Reliability and Analysis Research