Imperfect Debugging-Based Modeling of Fault Detection and Correction Using Statistical Methods

Abstract

With the current advancement of technology, real-time software has been extensively utilized in several complex systems. The eternally rising complexity of software formulates it exceptionally hard to maintain the reliability of software and has strained extraordinary awareness in software industries. Nearly all reliability-based software reliability growth models (SRGMs) are formulated using a general consideration that during the testing phase, all detected faults are instantly corrected without introducing any new fault. Therefore, both detection and correction of faults are considered as similar processes. In this paper, inclusive modeling is done for investigating the detection and correction of faults under an imperfect debugging scenario. This formulation is modeled by considering the postulation that new faults are involved throughout the correction of a hard type of fault. Numerous qualitative measures for assessment of reliability are considered and least square estimations of unidentified model parameters are assessed. The validation of the derived proposed models is verified through actual datasets. The measures of accuracy are the mean square error (MSE), root mean square error (RMSE), bias, variance and root mean square prediction error (RMSPE) are calculated employing valid datasets. The goodness of fit criteria is verified based on such qualitative measures.