Predicting Software Performance with Divide-and-Learn

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.

BibTeX

@inproceedings{Gong-Chen:FSE23,
  author    = {Jingzhi Gong and
               Tao Chen},
  title     = {Predicting Software Performance with {Divide-and-Learn}},
  booktitle = {{ESEC/SIGSOFT} {FSE}},
  pages     = {858--870},
  publisher = {{ACM}},
  year      = {2023},
}

Predicting Software Performance with Divide-and-Learn

Abstract

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