Insight knowledge in search based software testing
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Theoretical analysis of local search in software testing
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
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AUSTIN: An open source tool for search based software testing of C programs
Information and Software Technology
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Runtime Analysis is a type of theoretical investigation that aims to determine, via rigorous mathematical proofs,the time a search algorithm needs to find an optimal solution.This type of investigation is useful to understand why a search algorithm could be successful, and it gives insight of how search algorithms work. In previous work,we proved the runtimes of different search algorithms on the test data generation for the Triangle Classification (TC)problem. We theoretically proved that Alternating Variable Method (AVM) has the best performance on the coverage of the most difficult branch in our empirical study. In this paper,we prove that the runtime of AVM on all the branches of TC is O((log n)2). That is necessary and sufficient to prove that AVM has a better runtime on TC compared to the other search algorithms we previously analysed. The theorems in this paper are useful for future analyses. In fact, to state theta search algorithm has worse runtime compared to AVM, it will be just sufficient to prove that its lower bound is higher than Ω((log n)2) on the coverage of at least one branch of TC.