IMPROVEMENT IN MUTATION TESTING USING BOLTZMANN LEARNING FOR FAULT PREDICTION IN TEST CASES

Akanksha Tiwari, Abhinav Singh

Abstract


The software engineering is the technology to process the software and perform various operations on that software. The testing the important application of software engineering in which test cases are applied to detect faults from the software . In the recent times, it is been analyzed that faults may also arise in the test cases which are used for the fault detection. In this work, mutation algorithm is applied for the detection of faults from the software. To improve performance of mutation algorithm in terms of fault detection rate the technique of back propagation is applied which learn from the precious experience and drive new values. The system is tested on 10 test cases and simulation is performed in MATLAB. The simulation results show that the fault detection rate is increased and execution time is reduced.

Keywords


structurally complex,mutation,faults.

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References


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DOI: https://doi.org/10.26483/ijarcs.v9i3.6046

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