Learning to Combine Multiple Ranking Metrics ...
Document type :
Communication dans un congrès avec actes
DOI :
Title :
Learning to Combine Multiple Ranking Metrics for Fault Localization
Author(s) :
Xuan, Jifeng [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Monperrus, Martin [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Self-adaptation for distributed services and large software systems [SPIRALS]
Monperrus, Martin [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Conference title :
ICSME - 30th International Conference on Software Maintenance and Evolution
City :
Victoria
Country :
Canada
Start date of the conference :
2014-09-28
Publication date :
2014-09-28
HAL domain(s) :
Informatique [cs]/Génie logiciel [cs.SE]
English abstract : [en]
Fault localization is an inevitable step in software debugging. Spectrum-based fault localization consists in computing a ranking metric on execution traces to identify faulty source code. Existing empirical studies on ...
Show more >Fault localization is an inevitable step in software debugging. Spectrum-based fault localization consists in computing a ranking metric on execution traces to identify faulty source code. Existing empirical studies on fault localization show that there is no optimal ranking metric for all faults in practice. In this paper, we propose Multric, a learning-based approach to combining multiple ranking metrics for effective fault localization. In Multric, a suspiciousness score of a program entity is a combination of existing ranking metrics. Multric consists two major phases: learning and ranking. Based on training faults, Multric builds a ranking model by learning from pairs of faulty and non-faulty source code elements. When a new fault appears, Multric computes the final ranking with the learned model. Experiments are conducted on 5386 seeded faults in ten open-source Java programs. We empirically compare Multric against four widely-studied metrics and three recently-proposed one. Our experimental results show that Multric localizes faults more effectively than state-of-art metrics, such as Tarantula, Ochiai, and Ample.Show less >
Show more >Fault localization is an inevitable step in software debugging. Spectrum-based fault localization consists in computing a ranking metric on execution traces to identify faulty source code. Existing empirical studies on fault localization show that there is no optimal ranking metric for all faults in practice. In this paper, we propose Multric, a learning-based approach to combining multiple ranking metrics for effective fault localization. In Multric, a suspiciousness score of a program entity is a combination of existing ranking metrics. Multric consists two major phases: learning and ranking. Based on training faults, Multric builds a ranking model by learning from pairs of faulty and non-faulty source code elements. When a new fault appears, Multric computes the final ranking with the learned model. Experiments are conducted on 5386 seeded faults in ten open-source Java programs. We empirically compare Multric against four widely-studied metrics and three recently-proposed one. Our experimental results show that Multric localizes faults more effectively than state-of-art metrics, such as Tarantula, Ochiai, and Ample.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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