DocumentCode :
176146
Title :
Learning to Combine Multiple Ranking Metrics for Fault Localization
Author :
Jifeng Xuan ; Monperrus, M.
Author_Institution :
INRIA Lille - Nord Eur., Lille, France
fYear :
2014
fDate :
Sept. 29 2014-Oct. 3 2014
Firstpage :
191
Lastpage :
200
Abstract :
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.
Keywords :
Java; learning (artificial intelligence); program debugging; public domain software; software fault tolerance; source code (software); Multric; faulty source code identification; learning-based approach; multiple ranking metrics; nonfaulty source code elements; open-source Java programs; program entity; software debugging; spectrum-based fault localization; training faults; Computational modeling; Debugging; Java; Measurement; Object oriented modeling; Training; Training data; Fault localization; learning to rank; multiple ranking metrics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Maintenance and Evolution (ICSME), 2014 IEEE International Conference on
Conference_Location :
Victoria, BC
ISSN :
1063-6773
Type :
conf
DOI :
10.1109/ICSME.2014.41
Filename :
6976085
Link To Document :
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