Random unit-test generation with MUT-aware sequence recommendation
Proceedings of the IEEE/ACM international conference on Automated software engineering
Adaptive random testing: an illusion of effectiveness?
Proceedings of the 2011 International Symposium on Software Testing and Analysis
BALLERINA: automatic generation and clustering of efficient random unit tests for multithreaded code
Proceedings of the 34th International Conference on Software Engineering
Software—Practice & Experience
An orchestrated survey of methodologies for automated software test case generation
Journal of Systems and Software
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Adaptive Random Testing (ART) is a testing technique which is based on an observation that a test input usually has the same potential as its neighbors in detection of a specific program defect. ART helps to improve the efficiency of random testing in that test inputs are selected evenly across the input spaces. However, the application of ART to object-oriented programs (e.g., C++ and Java) still faces a strong challenge in that the input spaces of object-oriented programs are usually high dimensional, and therefore an even distribution of test inputs in a space as such is difficult to achieve. In this paper, we propose a divergence-oriented approach to adaptive random testing of Java programs to address this challenge. The essential idea of this approach is to prepare for the tested program a pool of test inputs each of which is of significant difference from the others, and then to use the ART technique to select test inputs from the pool for the tested program. We also develop a tool called ARTGen to support this testing approach, and conduct experiment to test several popular opensource Java packages to assess the effectiveness of the approach. The experimental result shows that our approach can generate test cases with high quality.