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Top1.0 Introduction
With Software testing is one of the significant task of software development. software testing is crucial to check the software application program to promise that the system is working accurately. Software testing is the momentous part of an effective software product. Software testing is the way of executing the software program for locating faults which trigger software failure. Most important objective of software testing is to recognize faults exist in software under development. Combinatorial testing (CT) is a valuable software testing method used to recognize the faults in couple of feature mixtures of Software Program Applications (SPA). Software testing demands time and cost consumed on software development. This cost may reduce quickly as testing time reduces. Software may be published into the market without being tested adequately due to marketing force and idea to save time and reduce costs. Publishing software products without high quality into the market is unacceptable. Because it may trigger loss of incomes or even damage of life. Accordingly, software testers should construct high-quality test cases that discover maximum of the faults in the software within the scheduled time for testing. Excellence of software program is attained by means of accurate test suite. CT is crucial for effectual test suite generation. CT facilitates the tester to perform testing with a small set of test cases for achieving very high fault coverage. Essential goal of software testing is to create a set of tiniest test cases which covers higher faults in smaller amount of time. Therefore, the excellence of software program is determined in terms of software metrics like highest faults type coverage, tiniest test suite size and least computational time. So, test suite minimization methods perform a most important role in minimizing the number of test suites and reducing time and cost of software testing without modifying their quality. Numerous research works have been performed for test case reduction to enhance the quality of software system. Still, time complexity of software testing was not reduced. Hence, this review work aims test suite optimization effectively for enhancing the capability of Software.
We have identified two different heuristic algorithms called (Bestoun S. Ahmed, 2016) Cuckoo Search Algorithm (CSA) and (Annibale Panichella et al., 2015) Diversity based Genetic Algorithm (DIV-GA) as baseline algorithms for performing this research.A cuckoo search algorithm (CSA) was designed in (Bestoun S. Ahmed, 2016) with the combinatorial method for reducing the test case and improving the fault detection. The CSA detects the configuration-aware faults but it does not considerably improve the performance of test case minimization. A Diversity based Genetic Algorithm (DIV-GA) was introduced in (Annibale Panichella et al., 2015) for selecting the test case with multiple objectives. The DIV-GA display more faults at a similar level of execution cost. The algorithm does not minimize the testing cost at a required level. But we identified that both baseline algorithms are demanded less fault coverage capability, lack of test case minimization, failure to solve the multi-objective problems, failure to improve software quality, more computational time to optimize the test suite.
To solve the above quoted issues in test suite optimization problem using cuckoo search algorithm and Diversity based Genetic Algorithm, Hybrid Multi-criteria Particle Swarm and Ranked Firefly Metaheuristic Optimization (HMCPW-RFMO) is developed. HMCPW-RFMO algorithm is built based on higher faults-type coverage analysis and produce an efficient optimum results. The major contribution of the proposed HMCPW-RFMO technique is described as follows,
Ø To enrich the capability of test suite reduction for software testing.
Ø To enlarge the test suite reduction rate and to cut the execution time for software testing compared with cuckoo search algorithm and Diversity based Genetic Algorithm .
Ø To optimize test cases in test suite in terms of higher faults type coverage and less execution time.