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Learn how to predict faulty validations in cluster issue detection systems using machine learning approaches in this conference talk from DevConf.CZ 2025. Discover how to analyze validation rules as repeated code patterns and create unique datasets for machine learning applications in large-scale codebases. Explore the computation of numerical descriptors including code length, complexity, entropy, and time since introduction across different Git branches, and understand how to compare these metrics with historical bug fixes to identify patterns. Examine preliminary research findings that reveal strong correlations between various factors and validation reliability in systems maintained by SRE engineers. Compare the effectiveness of classical machine learning models against modern large language models (LLMs) for predicting which validations are most likely to require fixes and generate false positives. Gain insights into automated validation quality improvement techniques and their potential impact on maintaining hundreds of validation rules in production environments.
