Hidden Markov Model Approach for Software Reliability Estimation with Logic Error

To ensure the safe operation of any software controlled critical systems, quality factors like reliability and safety are given utmost importance. In this paper, we have chosen to analyze the impact of logic error that is one of the contributors to the above factors. In view of this, we propose a novel framework based on a data driven approach known as software failure estimation with logic error (SFELE). Here, the probabilistic nature of software error is explored by observing the operation of a safety critical system by injecting logic fault. The occurrence of error, its propagations and transformations are analyzed from its inception to end of its execution cycle through the hidden Markov model (HMM) technique. We found that the proposed framework SFELE supports in labeling and quantifying the behavioral properties of selected errors in a safety critical system while traversing across its system components in addition to reliability estimation of the system. Our attempt at the design level can help the design engineers to improve their system quality in a cost-effective manner.