Automated CI/CD Error Resolution via Pattern Matching
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Solution Overview
Problem
Continuous integration and continuous deployment (CI/CD) pipelines often fail due to complex system setups and variable components, leading to significant delays in software deployment as traditional troubleshooting methods are time-consuming and inefficient.
Innovation Solution
A system and method that processes deployment logs to identify patterns in errors, matches them with past errors, and automatically implements solutions by modifying software or computing device configurations, using machine learning models to quickly resolve recurring deployment issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional troubleshooting methods are used to resolve deployment errors, then human experts can analyze and fix issues, but the process is time-consuming and reduces deployment productivity
Solution Approach 1:
The system enables self-service by automatically analyzing deployment errors, matching them with historical patterns, and implementing solutions without human intervention. The error analysis service autonomously processes deployment logs, identifies patterns, and executes corrective actions, allowing the system to resolve its own issues and eliminate waiting time for expert intervention
Solution Approach 2:
The system performs preliminary action by pre-processing deployment logs and maintaining a database of historical error patterns and solutions. When a deployment error occurs, the system can quickly match it against pre-analyzed patterns and apply known solutions, avoiding the need for time-consuming real-time expert analysis and accelerating the resolution process
2Measurement precision
If manual analysis of deployment errors is performed, then accurate problem identification can be achieved, but significant time is lost in troubleshooting
Solution Approach 1:
The system replaces manual mechanical analysis with automated computational analysis. Machine learning models and pattern matching algorithms automatically process deployment logs and identify error causes, achieving both high accuracy in error identification and rapid analysis without the time constraints of manual expert review
Solution Approach 2:
The system creates copies of historical error patterns and solutions in a structured database. When new deployment errors occur, the system compares them against these stored patterns to quickly identify matching issues and apply proven solutions, maintaining accuracy while dramatically reducing analysis time through efficient pattern matching
3Adaptability or versatility
If deployment errors are resolved manually, then complex issues can be addressed with expert knowledge, but the process creates delays in getting software to production
Solution Approach 1:
The system implements dynamic error resolution by continuously learning from new deployment errors and updating its pattern database. The system adapts to new error types and evolves its solution strategies over time, maintaining versatility in handling complex issues while operating at automated speeds that respect deployment timelines
Solution Approach 2:
The system incorporates feedback loops where deployment error outcomes are fed back into the pattern database. Successful resolutions are stored as patterns for future reference, and the system continuously improves its accuracy by learning from past performance, enabling it to handle increasingly complex errors automatically while maintaining rapid response times
Data Source
AI summary
A method, a system, and computer program product for automatic implementation of solutions for deployment errors in continuous integration and continuous deployment pipelines. A deployment log including a deployment error indicating a failure to deploy a software product including a software configuration at a computing device comprising a computing device configuration is received. The deployment error is processed to determine a pattern of the deployment error. A match between the deployment error and a past deployment error is determined based on the pattern of the deployment error and a frequency of occurrence of the pattern within a set interval of time. An output indicating a solution for the deployment error is provided based on the match between the deployment error and the past deployment error. The solution for the deployment error is implemented by modifying the software configuration or the computing device configuration.