LAUSR

The probability of the occurrence of faults increases manifolds when program lines of code exceed a few thousand in ubiquitous applications. Fault mitigation in ubiquitous applications, such as those of autonomous vehicular technologies (VTs), has not been effective even with the use of formal methods. Faults in such applications require exhaustive testing for a timely fix, which seems infeasible computationally. This emphasizes the imperative role of software fault tolerance (SFT) for autonomous applications. Several SFT techniques have been proposed, but failures revealed in VT applications imply that existing SFT techniques need to be fine-tuned. In this article, current replication-based SFT techniques are analyzed and classified with respect to their diversity, adjudication, and adaptivity. Essential parameters (reliability, time, variance, etc.) for adjudication, diversity, and adaptiveness are recorded. The identified parameters are mapped to different techniques (e.g., AFTRC, SCOP, VFT) for observing their shortcomings. Consequently, a generic framework named Diverse Parallel Adjudication for Software Fault Tolerance (DPA-SFT) is proposed. DPA-SFT addresses the shortcomings of existing SFT techniques for VTs with the added value of parallel and diverse adjudication. A prototype implementation of the proposed framework has been developed for assessing the viability of DPA-SFT over modules of VT. An empirical comparison of the proposed framework is performed with prevalent techniques (AFTRC, SCOP, VFT, etc). A thorough evaluation suggests that DPA-SFT performs better than contemporary SFT techniques in VTs due to its parallel and diverse adjudication.