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PhD Final Examination – Rahul Gopinath


Friday, June 9, 2017 10:00 AM - 12:00 PM

On the Limits of Mutation Analysis
Mutation analysis is the gold standard for evaluating test-suite adequacy. It involves exhaustive seeding of all small faults in a program and evaluating the effectiveness of test suites in detecting these faults. Mutation analysis subsumes numerous structural coverage criteria, approximates fault detection capability of test suites, and the faults produced by mutation have been shown to be similar to the real faults.

This dissertation looks at the effectiveness of mutation analysis in terms of its ability to evaluate the quality of test suites, and how well the mutants generated emulate real faults. The effectiveness of mutation analysis hinges on its two fundamental hypotheses: The competent programmer hypothesis, and the coupling effect. The competent programmer hypothesis provides the model for the kinds of faults that mutation operators emulate, and the coupling effect provides guarantees on the ratio of faults prevented by a test suite that detects all simple faults to the complete set of possible faults. These foundational hypotheses determine the limits of mutation analysis in terms of the faults that can be prevented by a mutation adequate test suite. Hence, it is important to understand what factors affect these assumptions, what kinds of faults escape mutation analysis, and what impact interference between faults (coupling and masking) have.

A secondary concern is the computational footprint of mutation analysis. Mutation analysis requires the evaluation of numerous mutants, each of which potentially requires complete test runs to evaluate. Numerous heuristic methods exist to reduce the number of mutants that need to be evaluated. However, we do not know the effect of these heuristics on the quality of mutants thus selected. Similarly, whether the possible improvement in representation using these heuristics are subject to any limits have also not been studied in detail.

Our research investigates these fundamental questions in mutation analysis both empirically and theoretically. We show that while a majority of faults are indeed small, and hence within a finite neighborhood of the correct version, their size is larger than typical mutation operators. We show that strong interactions between simple faults can produce complex faults that are semantically unrelated to the component faults, and hence escape first order mutation analysis. We further validate the coupling effect for a large number of real-world faults, provide theoretical support for fault coupling, and evaluate its theoretical and empirical limits. Finally, we investigate the limits of heuristic mutation reduction strategies in comparison with random sampling in representativeness and find that they provide at most limited improvement.

These investigations underscore the importance of research into new mutation operators and show that the potential benefit far outweighs the perceived drawbacks in terms of computational cost.

Major Advisor: Carlos Jensen
Committee: Alex Groce
Committee: Tim Budd
Committee: Anita Sarma
Committee: Eric Walkingshaw
GCR: Joseph Louis

 


Kelley Engineering Center (campus map)
1007
Calvin Hughes
1 541 737 3168
Calvin.Hughes at oregonstate.edu
Sch Elect Engr/Comp Sci
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