Release Risk Assessment via Historical Pattern Matching
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Solution Overview
Problem
Software releases often contain errors that negatively impact production environments, leading to product recalls, rollbacks, or emergency fixes, as existing systems lack effective risk assessment mechanisms to predict and mitigate potential issues before deployment.
Innovation Solution
A release assessment system that generates a language model of historical keywords, compares software code to identify patterns with past issues, calculates a risk score based on historical changes, and adjusts or reevaluates features before production deployment, using a distributed system with client devices, databases, and machine learning for proactive risk prediction and minimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If releases are deployed to production environment, then product functionality is improved, but errors in releases negatively impact production environment stability
Solution Approach 1:
The system performs risk assessment and pattern matching before deployment by comparing release characteristics against historical keywords and patterns. This preliminary analysis identifies potential issues before they affect production, allowing preventive measures to be taken while maintaining the ability to deploy functional improvements.
2Measurement precision
If risk assessment systems are implemented, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system creates a database of historical keywords and patterns from past releases and issues. Instead of building a completely new complex analysis system, it copies and leverages existing historical data and patterns to enable accurate risk prediction through pattern matching against known issues.
Solution Approach 2:
The system introduces an intermediary layer of historical keyword patterns and risk models that mediates between the release code and the production environment. This intermediary assessment layer predicts potential issues without requiring direct complex interaction between all system components.
3Reliability
If comprehensive risk assessment is performed, then reliability is improved, but deployment time is increased
Solution Approach 1:
The system performs risk assessment by comparing against a curated set of historical patterns and keywords rather than analyzing every possible code path and scenario. This partial action approach provides sufficient reliability assessment without the excessive time cost of comprehensive exhaustive analysis.
Data Source
AI summary
The present disclosure is directed to methods and systems for assessing the risk of a release to a production environment. The release assessment system can determine the risk of a release of a feature, such as a deployable software package, to an environment based on the characteristics of the release. The release assessment system compares the words in the software code of the release to stored historical keywords to identify any patterns or similarities between the current release and historical issues/errors/incidents. Based on the identified similarity to historical releases, the system determines a risk score for releasing the feature to the production environment.