An Empirical Comparison of Automated Generation and Classification Techniques for Object-Oriented Unit Testing

Authors:
Marcelo d'Amorim;Carlos Pacheco;Tao Xie;Darko Marinov;Michael D. Ernst
Affiliations:
University of Illinois, Urbana-Champaign, IL, USA;MIT, Cambridge, MA, USA;North Carolina State University, Raleigh, NC, USA;University of Illinois, Urbana-Champaign, IL, USA;MIT, Cambridge, MA, USA
Venue:
ASE '06 Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering
Year:
2006

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Abstract

Testing involves two major activities: generating test inputs and determining whether they reveal faults. Automated test generation techniques include random generation and symbolic execution. Automated test classification techniques include ones based on uncaught exceptions and violations of operational models inferred from manually provided tests. Previous research on unit testing for object-oriented programs developed three pairs of these techniques: model-based random testing, exception-based random testing, and exception-based symbolic testing. We develop a novel pair, model-based symbolic testing. We also empirically compare all four pairs of these generation and classification techniques. The results show that the pairs are complementary (i.e., reveal faults differently), with their respective strengths and weaknesses.