Guided Mutation Testing for JavaScript Web Applications


Mutation testing is a good test adequacy assessment technique. However, there's a high computational price in executing the test suite against a doubtless giant pool of generated mutants. Moreover, there's a lot of effort concerned in filtering out equivalent mutants. Prior work has mainly focused on detecting equivalent mutants once the mutation generation phase, which is computationally expensive and has limited potency. We have a tendency to propose an algorithm to pick variables and branches for mutation in addition to a metric, known as , to rank functions according to their relative importance from the appliance’s behaviour point of read. We have a tendency to present a method that leverages static and dynamic analysis to guide the mutation generation process towards components of the code that are additional probably to influence the program’s output. More, we target the JavaScript language, and propose a collection of mutation operators that are specific to internet applications. We tend to implement our approach in a tool referred to as Mutandis. The results of our empirical analysis show that (1) additional than ninety three p.c of generated mutants are non-equivalent, and (2) more than 75 percent of the surviving non-equivalent mutants are in the top 30 percent of the ranked functions.

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