CI/CD Failure Analysis System Using ML Hypothesis Prioritization
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Solution Overview
Problem
In software product development, identifying and resolving failures in Continuous Integration and Continuous Deployment (CI/CD) systems is challenging due to technical complexity, requiring expertise from various domains and being time-consuming, especially in complex projects with rapid delivery cadences.
Innovation Solution
Implementing a CI/CD system failure analysis and resolution apparatus that uses machine learning to form hypotheses on potential root causes of failures, prioritizes them based on data analysis and user feedback, and automatically implements resolutions, incorporating data from build pipelines, logs, and other processes to streamline failure identification and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual failure analysis methods are used in CI/CD systems, then expertise from various domains can be applied, but the time required for failure analysis and resolution increases significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a data collector, pattern recognizer, and suggestion generator that mediates between the complex CI/CD failure data and human experts. This intermediary automatically processes build data, log data, and test data to generate prioritized failure cause suggestions, reducing the time experts need to spend on initial analysis while maintaining analysis accuracy through structured domain knowledge integration.
Solution Approach 2:
The system performs preliminary failure analysis actions by automatically collecting build data, log data, and test data, and generating prioritized suggestions for failure causes before human experts intervene. This preliminary processing filters and organizes information in advance, allowing experts to focus on validating and acting on pre-analyzed findings rather than starting from scratch.
2Productivity
If automated failure analysis systems are implemented, then failure resolution time is reduced, but the system complexity and difficulty of implementation increase
Solution Approach 1:
The patent segments the failure analysis system into distinct functional modules: a data collector module for gathering build, log, and test data; a pattern recognizer module for identifying failure patterns; and a suggestion generator module for producing prioritized failure cause suggestions. This segmentation reduces implementation complexity by allowing each module to be developed, tested, and maintained independently while working together to achieve automated failure analysis.
3Measurement precision
If comprehensive data collection from build pipelines and logs is performed, then failure cause identification accuracy improves, but data processing complexity and time requirements increase
Solution Approach 1:
The patent extracts only the most relevant data elements from comprehensive build pipelines and logs through the data collector module, which selectively gathers build data, log data, and test data based on predefined criteria. The pattern recognizer then extracts meaningful patterns from this filtered data, and the suggestion generator extracts prioritized failure cause suggestions. This extraction approach maintains identification accuracy by focusing on critical data while reducing processing complexity by eliminating irrelevant information.
Data Source
AI summary
In some examples, continuous integration and continuous deployment (CI/CD) system failure analysis and resolution may include analyzing data for a CI/CD system to identify anomalous tests related to the CI/CD system. A plurality of hypotheses on a cause of failure associated with the CI/CD system may be determined based on a further analysis of the analyzed data. The determined hypotheses may be prioritized based on a confidence level analysis. Failure resolution to prevent failure associated with the CI/CD system may be determine based on a response to one of the prioritized hypotheses. Further, the determined failure resolution may be implemented.