Why train-and-select when you can use them all? ensemble model for fault localisation

Cited 0 time in webofscience Cited 3 time in scopus
  • Hit : 161
  • Download : 0
Learn-to-rank techniques have been successfully applied to fault localisation to produce ranking models that place faulty program elements at or near the top. Genetic Programming has been successfully used as a learning mechanism to produce highly effective ranking models for fault localisation. However, the inherent stochastic nature of GP forces its users to learn multiple ranking models and choose the best performing one for the actual use. This train-and-select approach means that the absolute majority of the computational resources that go into the evolution of ranking models are eventually wasted. We introduce Ensemble Model for Fault Localisation (EMF), which is a learn-to-rank fault localisation technique that utilises all trained models to improve the accuracy of localisation even further. EMF ranks program elements using a lightweight, voting-based ensemble of ranking models. We evaluate EMF using 389 real-world faults in Defects4J benchmark. EMF can place 30.1% more faults at the top when compared to the best performing individual model from the train-and-select approach. We also apply Genetic Algorithm (GA) to construct the best performing ensemble. Compared to naively using all ranking models, GA generated ensembles can localise further 9.2% more faults at the top on average.
Publisher
ACM Press
Issue Date
2019-07-15
Language
English
Citation

the Genetic and Evolutionary Computation Conference, pp.1408 - 1416

DOI
10.1145/3321707.3321873
URI
http://hdl.handle.net/10203/268458
Appears in Collection
CS-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0