Title
A Test Suite Reduction Approach to Improving the Effectiveness of Fault Localization
Abstract
In order to improve the effectiveness of fault localization, various test suite reduction techniques have been proposed. However, excessive or improper reduction of test cases may lose some testing information, thus causing a negative impact on fault localization. In this paper, we propose a new similarity-based test suite reduction approach to improving spectrum-based fault localization. Firstly, this approach extracts the high suspicious statements in the faulty program and removes the coincidental passed test cases for test suite selection. Then, it selects similar passed test cases for each different failed test case from the new passed test set based on the similar proportion of their execution traces, and determines the final composition of each similar test set based on the contribution of failed test cases to fault localization. By using the execution information of each similar test set with a spectrum-based fault localization approach, the ranks of statements can be obtained. Finally, synthesizing all ranks of statements in each rank list, we obtain the final rank list of statements. Several experiments show that our approach can help reduce the debugging effort in terms of the percentage of statements needed to be inspected when locating faults in both single-fault and multi-fault programs. Moreover, the results demonstrate that the value of similar proportion have nontrivial influence on the effectiveness of fault localization.
Year
DOI
Venue
2017
10.1109/SATE.2017.10
2017 International Conference on Software Analysis, Testing and Evolution (SATE)
Keywords
Field
DocType
debugging,fault localization,test case reduction,similar test case
Test suite,Data mining,Computer science,Test case,Reliability engineering,Test set,Debugging
Conference
ISBN
Citations 
PageRank 
978-1-5386-3688-6
1
0.35
References 
Authors
23
5
Name
Order
Citations
PageRank
Wenhao Fu152.15
Huiqun Yu219136.27
Guisheng Fan39125.45
Xiang Ji42011.57
Xin Pei5121.98