Multi Objective Optimization of Software Test Cases Using Evolutionary and Soft Computing Techniques

Abstract

Software testing occurs concurrently during the software development to identify errors as early as possible and to assure that changes made in software did not affect the system pessimistically. Since software testing is a time consuming, complex, full of uncertainty and expensive activity, delivering the human safety, medical, robotics software without proper testing may lead to potentially much higher cost than that of testing. However, during the development phase, the test suite is updated and tends to increase in size. Due to the resources and time constraints for re-executing large test suites, it is important to develop techniques to reduce the effort of regression testing. Some challenges for testing software systems are the effort, cost, complexity, ambiguity, test data adequacy involved in optimizing test cases. Optimization of the test cases before testing will surely cut down efforts, cost and improve the quality of software testing. Complexity, risks, cost, fuzziness and multi-criteria test cases fitness evaluation makes test case optimization an essential part of software testing. Several approaches have been proposed for reducing the effort, cost and improve the quality of regression testing such as test case selection, classification, filteration, prioritization, and test suite minimization. Test suite minimization techniques aim at identifying and eliminating redundant, obsolete, unfit, ambiguous test cases from the suite. Test suite minimization techniques identify a subset of test cases from suite, required to re-test the changes in the software. Test case selection is a selection of a subset of test cases from the test suite based on some test criteria. Test case prioritization techniques schedule test cases for execution in an order to increase the early fault detection, but the problem of identifying an optimized test suite with maximum coveragebility in cost efficient manner is the critical problem of software testing.