Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866913372092497920 |
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| author | Khasidashvili, Zurab |
| author_facet | Khasidashvili, Zurab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_00622 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques Khasidashvili, Zurab Software Engineering Artificial Intelligence Machine Learning 68T30, 68T05, E.0; I.2; G.3; B.6 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. |
| title | Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques |
| topic | Software Engineering Artificial Intelligence Machine Learning 68T30, 68T05, E.0; I.2; G.3; B.6 |
| url | https://arxiv.org/abs/2207.00622 |