Mutation Testing

Mutation Testing is applied by QA Valley to generate source code statements that resembles the original codebase that is subsequently embedded into the original source to confirm if the test cases will produce corresponding defects.

Mutants are defects introduced in an application to verify that the test cases are comprehensive enough to catch the error. A mutation refers to modifying a program by introducing mutants in ways to detect weakness in sections of the application that are seldom or are never accessed during execution. Certain small changes in the program module are made and compared with original modules and checked if the test cases can find an error. This testing technique helps in a good level of error detection and exposes the source code ambiguities by highlighting all the faults in the program.

The first phase of mutation testing commences by introducing various versions of faults in the source code. The test cases are executed on both the original application and mutated application, followed by a comparison of the results. Varying results conclude in the mutant being eliminated by the test case. Similar outcome concludes in the mutant code being kept alive and designing more effective test cases to eliminate the mutant code. Types of mutation testing include Statement Mutation where few lines of code are mutated, Value and Decision Mutation involving modifying the values of the parameters and control statements respectively.

QA Valley testing team automation tools include Stryker Mutator and PIT, Judy and Jester which helps to speed up the mutation testing process. QA Valley Mutation testing provides exhaustive testing of applications by producing reliable solutions to assess test suite reliability resulting in efficiency and accuracy of the test result. QA Valley’s has experienced testers who recommend the appropriate tool and analyse the results while proposing the appropriate corrective measures. Customers benefit from QA Valley’s impeccable testing services that provide near perfect error detection by uncovering ambiguities in the source code and recommendations to improve the standards.