Improvising the effectiveness of test suites using differential evolution technique

The process of testing any software system is an atrocious task which indeed consumes a ton of effort, and expensive also. Required effort and time to do adequate as well as effective testing get bigger, as the software gets more complexed that can lead to swarm over the project budget or some test cases left uncovered or delay in completion. A suitably generated test suite does not only locate errors but also aid in reducing cost investment associated with the testing process. This paper implements an optimizing technique called as Differential Evolution to improve the effectiveness of test cases using Average Percentage of Fault Detection (APFD) metric. APFD is taken as the fitness function which is to be optimized. In this work, We have performed comparison of our approach with other existing prioritizing approaches and Experimental computations show that Differential Evolution technique achieve better APFD values than other techniques.