Software is all around us in our lives in the industrialized world, and we as a society and individuals need it to function correctly. Software testing fills the role of performing behavior audits, to guide the correction of the software to its intended behavior. The consequences of faulty software can range to the late arrival of trains, to nuclear meltdowns. This places quality requirements on the software of various levels. Program based mutation testing provides a high level of faultfinding capability. It does this by injecting many synthetic faults into the code under test, as described by mutation operators. These faults are used to search for testcases that would identify such faults, and consequently find real faults that the synthetic faults mimic. However, mutation testing is costly on three accounts; each mutant of the original code is compiled, each mutant should ideally have an associated testcase to reveal that fault the mutant contains, finally the testcases are analyzed thoroughly by looking the output of the original and mutants to reveal the error in behavior. In order to reduce cost while maintaining a high level of faultfinding, selective mutation testing is investigated, it uses a subset of all the available mutation operators. The investigation found that using Absolute value-, and Relational operator-, mutation reduces cost of mutation testing by 80%, while uncovering 83% of the injected faults.

Context. Software systems are under constant updating for being faulty and to improve and introduce features. The Software testing is the most commonly used  method for validating the quality of software systems. Agile processes help to  automate testing process. A regression test is the main strategy used in testing. Regression testing is time consuming, but with increase in codebases is making it more time extensive and time consuming. Making regression testing time efficient for continuous integration is the new strategy.   Objectives. This thesis focuses on co-relating code packages to test packages by automating mutation to inject error into C code. Regression testing against mutated code establishes co-relations. Co-relation data of particular modified code packages can be used for test selections. This method is most effective than the traditional test selection method. For this thesis to reduce the mutation costs selective mutation method is selected. Demonstrating the proof of concept helps to prove proposed  hypothesis.   Methods. An experiment answers the research questions. Testing of hypothesis on open source C programs will evaluate efficiency. Using this correlation method testers can reduce the testing cycles regardless of test environments. Results. Experimenting with sample programs using automated selective mutation the efficiency to co-relate tests to code packages was 93.4%.   Results. After experimenting with sample programs using automated selective mutation the efficiency to co-relate tests to code packages was 93.4%.   Conclusions. This research concludes that the automated mutation to obtain test selection statistics can be adopted. Though it is difficult for mutants to fail every test case, supposing that this method works with 93.4% efficient test failure on an average, then this method can reduce the test suite size to 5% for the particular modified code package.