Finding failures by cluster analysis of execution profiles
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Artificial Intelligence Review - Special issue on lazy learning
Automated support for classifying software failure reports
Proceedings of the 25th International Conference on Software Engineering
Active learning for automatic classification of software behavior
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Empirical Software Engineering
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Techniques for Classifying Executions of Deployed Software to Support Software Engineering Tasks
IEEE Transactions on Software Engineering
Proceedings of the 2007 international symposium on Software testing and analysis
A Systematic Study of Failure Proximity
IEEE Transactions on Software Engineering
Insights on fault interference for programs with multiple bugs
ISSRE'09 Proceedings of the 20th IEEE international conference on software reliability engineering
A Dynamic Test Cluster Sampling Strategy by Leveraging Execution Spectra Information
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
Using Semi-supervised Clustering to Improve Regression Test Selection Techniques
ICST '11 Proceedings of the 2011 Fourth IEEE International Conference on Software Testing, Verification and Validation
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Software behavior learning is an important task in software engineering. Software behavior is usually represented as a program execution. It is expected that similar executions have similar behavior, i.e. revealing the same faults. Single-label learning has been used to assign a single label (fault) to a failing execution in the existing efforts. However, a failing execution may be caused by several faults simultaneously. Hence, it needs to assign multiple labels to support software engineering tasks in practice. In this paper, we present multi-label software behavior learning. A well-known multi-label learning algorithm ML-KNN is introduced to achieve comprehensive learning of software behavior. We conducted a preliminary experiment on two industrial programs: flex and grep. The experimental results show that multi-label learning can produce more precise and complete results than single-label learning.