Using program data-state scarcity to guide automatic test data generation
Software Quality Control
Detecting visually similar Web pages: Application to phishing detection
ACM Transactions on Internet Technology (TOIT)
Test data regeneration: generating new test data from existing test data
Software Testing, Verification & Reliability
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Achieving scalable model-based testing through test case diversity
ACM Transactions on Software Engineering and Methodology (TOSEM)
Orthogonal exploration of the search space in evolutionary test case generation
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Diversity oriented test data generation using metaheuristic search techniques
Information Sciences: an International Journal
Static test case prioritization using topic models
Empirical Software Engineering
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Search-based software testing (SBST) has shown a po- tential to decrease cost and increase quality of testing- related software development activities. Research in SBST has so far mainly focused on the search for isolated tests that are optimal according to a fitness function that guides the search. In this paper we make the case for fitness func- tions that measure test fitness in relation to existing or pre- viously found tests; a test is good if it is diverse from other tests. We present a model for test variability and propose the use of a theoretically optimal diversity metric at vari- ation points in the model. We then describe how to apply a practically useful approximation to the theoretically opti- mal metric. The metric is simple and powerful and can be adapted to a multitude of different test diversity measure- ment scenarios. We present initial results from an experi- ment to compare how similar to human subjects, the metric can cluster a set of test cases. To carry out the experiment we have extended an existing framework for test automation in an object-oriented, dynamic programming language.