Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques

TL;DR

This paper introduces a rule-based, data-driven approach to accelerate system-level debugging by generating high-quality hints from logs, demonstrated on power management failures, and emphasizes reusing root-causing knowledge to improve future debugging efficiency.

Contribution

The paper presents a novel rule learning and subgroup discovery framework for root-causing system failures, including techniques for feature engineering, data analytics, and knowledge reuse to enhance debugging processes.

Findings

Effective root-causing of Power Management failures using the proposed techniques

Generation of high-quality debug hints from test logs

Reusability of root-causing experience for future debugging

Abstract

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.