Enhanced Performance of Adaptive Random Partitioning Testing by Unifying the ARPT-1 and ARPT-2 Strategies
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
The software testing is considered as the most powerful and important phase. Effective testing process will leads to more accurate and reliable results and high quality software products. Random testing (RT) is a major software testing strategy and their effortlessness makes them conceivable as the most efficient testing strategies concerning the time required for experiment determination, its significant drawback of RT is defect detection efficacy. This draw back has been beat by Adaptive
... ng (AT), however AT is enclosed of computational complexity. One most important method for improving RT is Adaptive random testing (ART). Another class of testing strategies is partition testing is one of the standard software program checking out strategies, which involves dividing the enter domain up into a set number of disjoint partitions, and selecting take a look at cases from inside every partition The hybrid approach is a combination of AT and RPT that is already existing called as ARPT strategy. In ARPT the random partitioning is improved by introducing different clustering algorithms solves the parameter space of problem between the target method and objective function of the test data. In this way random partitioning is improved to reduce the time conception and complexity in ARPT testing strategies. The parameters of enhanced ARPT testing approaches are optimized by utilizing different optimization algorithms. The computational complexity of Optimized Improved ARPT (OIARPT) testing strategies is reduced by selecting the best test cases using Support Vector Machine (SVM). In this paper the testing strategies of Optimized Improved ARPT with SVM are unified and named as Unified ARPT (UARPT) which enhances the testing performance and reduces the time complexity to test software.