Investigating fault injection techniques in hardware‐based deep neural networks and mutation‐based fault localization.

This article discusses two papers that examine different aspects of software reliability using fault injection techniques. The first paper investigates the impact of transient hardware faults on deep learning neural network inference, particularly in safety-critical applications like autonomous vehicles and healthcare systems. The authors enhance fault injection techniques to reveal the significant influence of hardware faults on these applications. The second paper addresses the challenges of fault localization in software debugging and presents a novel approach, Delta4Ms, that mitigates mutant bias and improves fault localization accuracy. These papers provide valuable insights into ensuring software reliability and resilience in different contexts. [Extracted from the article]