Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques
This work addresses the challenge of reducing debug time and dependency on experts for complex hardware, software, and firmware systems, though it appears incremental as it applies existing techniques to a specific domain.
The paper tackles the problem of accelerating system-level debug by proposing a root-causing procedure using rule learning and subgroup discovery techniques, which provides high-quality debug hints to reduce debug effort, as demonstrated in a case study on Power Management design failures.
We propose a root-causing procedure for accelerating system-level debug using rule-based techniques. We describe the procedure and how it provides high quality debug hints for reducing the debug effort. This includes the heuristics for engineering features from logs of many tests, and the data analytics techniques for generating powerful debug hints. As a case study, we used these techniques for root-causing failures of the Power Management (PM) design feature Package-C8 and showed their effectiveness. Furthermore, we propose an approach for mining the root-causing experience and results for reuse, to accelerate future debug activities and reduce dependency on validation experts. We believe that these techniques are beneficial also for other validation activities at different levels of abstraction, for complex hardware, software and firmware systems, both pre-silicon and post-silicon.