Test case optimization using artificial bee colony algorithm

Abstract

Software Testing is one of the integral parts of software development lifecycle. Exhaustive testing on software is impossible to achieve as the testing is a continuous process. Using this as a constraint, software testing is performed in a way that requires reducing the testing effort but should provide high quality software that can yield comparable results. To accomplish this optimized testing, a software test case optimization technique based on artificial bee colony algorithm is proposed here. Based on intelligent behavior of honey bee, this method generates optimal number of test cases to be executed on software under test (SUT). This approach is qualified by self-organization, robustness and focuses on generation of paths derived from cyclomatic complexity. The resulting solution guarantees full path coverage. © 2011 Springer-Verlag.