Summary of On Enhancing Root Cause Analysis with Sql Summaries For Failures in Database Workload Replays at Sap Hana, by Neetha Jambigi et al.
On Enhancing Root Cause Analysis with SQL Summaries for Failures in Database Workload Replays at SAP HANA
by Neetha Jambigi, Joshua Hammesfahr, Moritz Mueller, Thomas Bach, Michael Felderer
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework automates root cause analysis for regression testing in databases, addressing false positive errors caused by various factors. A machine learning-based approach is employed to analyze failures during replays, aiming for effective workload capturing and replaying. The framework addresses generalizability challenges by leveraging a large language model (LLM) to extract concise failure summaries from failed SQL statements, enhancing the classification process. Experimental results show an improved F1-Macro score of 4.77%. This approach benefits end users by providing additional insights into found issues and improving replay result assessment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A database testing method is designed to improve accuracy. By analyzing why errors happen during a test, it can provide better results. However, there are challenges in making sure the results generalize well. To overcome this, a special language model is used to understand failed SQL statements and extract important information about what went wrong. This helps make more accurate predictions about future failures. The method has been tested and shown to be effective, with a significant improvement in accuracy. |
Keywords
» Artificial intelligence » Classification » Language model » Large language model » Machine learning » Regression
