Recently there has been an upsurge of interest in both, Search-Based Software Testing (SBST), and Dynamic Symbolic Execution (DSE). Each of these two approaches has complementary strengths and weaknesses, making it a natural choice to explore the degree to which the strengths of one can be exploited to offset the weakness of the other. This paper introduces an augmented version of DSE that uses a SBST-based approach to handling floating point computations, which are known to be problematic for vanilla DSE. The approach has been implemented as a plug in for the Microsoft Pex DSE testing tool. The paper presents results from both, standard evaluation benchmarks, and two open source programs.
Abstract-We present an algorithm for constructing fitness functions that improve the efficiency of search-based testing when trying to generate branch adequate test data. The algorithm combines symbolic information with dynamic analysis and has two key advantages: It does not require any change in the underlying test data generation technique and it avoids many problems traditionally associated with symbolic execution, in particular the presence of loops. We have evaluated the algorithm on industrial closed source and open source systems using both local and global search-based testing techniques, demonstrating that both are statistically significantly more efficient using our approach. The test for significance was done using a onesided, paired Wilcoxon signed rank test. On average, the local search requires 23.41% and the global search 7.78% fewer fitness evaluations when using a symbolic execution based fitness function generated by the algorithm.
Previous approaches to search based test data generation tend to focus on coverage, rather than oracle cost. While there may be an aspiration that systems should have models, checkable specifications and/or contract driven development, this sadly remains an aspiration; in many real cases, system behaviour must be checked by a human. This painstaking checking process forms a significant cost, the oracle cost, which previous work on automated test data generation tends to overlook. One simple way to reduce oracle cost consists of reducing the number of tests generated. In this paper we introduce three algorithms which do this without compromising coverage achieved. We present the results of an empirical study of the effectiveness of the three algorithms on five benchmark programs containing non trivial search spaces for branch coverage. The results indicate that it is, indeed, possible to make reductions in the number of test cases produced by search based testing, without loss of coverage.
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