Learning to Combine Multiple Ranking Metrics for Fault Localization Jifeng Xuan, Martin Monperrus To cite this version: Jifeng Xuan, Martin Monperrus. Learning to Combine Multiple Ranking Metrics for Fault Local- ization. ICSME - 30th International Conference on Software Maintenance and Evolution, Sep 2014, Victoria, Canada. 10.1109/ICSME.2014.41. hal-01018935 HAL Id: hal-01018935 https://hal.inria.fr/hal-01018935 Submitted on 18 Aug 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Learning to Combine Multiple Ranking Metrics for Fault Localization Jifeng Xuan Martin Monperrus INRIA Lille - Nord Europe University of Lille & INRIA Lille, France Lille, France
[email protected] [email protected] Abstract—Fault localization is an inevitable step in software [12], Ochiai [2], Jaccard [2], and Ample [4]). Most of these debugging. Spectrum-based fault localization applies a ranking metrics are manually and analytically designed based on metric to identify faulty source code. Existing empirical studies assumptions on programs, test cases, and their relationship on fault localization show that there is no optimal ranking metric with faults [16]. To our knowledge, only the work by Wang for all the faults in practice.