DevOps Code Module Risk Detection via Execution Profiles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In DevOps environments, identifying and fixing bugs before deployment is challenging due to the complexity of enterprise-level virtualized systems, where testing all possible environments is not feasible, and unanticipated interactions between code modules can lead to reliability, availability, and performance issues.
Innovation Solution
A method that collects and analyzes execution measurements and control flows across multiple test environments to create profiles, identifies differences, calculates risk of code module failure, and provides diagnostic data and recommended actions to mitigate potential issues, using machine learning to 'learn' the behavior of the application code and compare it against known behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If code modules are tested in multiple different environments to ensure reliability, then the reliability of code deployment is improved, but the complexity of the testing process increases
Solution Approach 1:
The patent creates execution profiles that capture the behavior characteristics of code modules across different environments. These profiles serve as copies or representations of the actual code execution, allowing comparison without requiring exhaustive testing in every possible environment. The profile comparison mechanism enables reliability assessment by identifying behavioral differences across environments without manually testing each scenario.
Solution Approach 2:
The system performs preliminary execution of code modules in test environments before actual deployment to generate execution profiles. This preliminary action captures the expected behavior patterns, control flows, and performance characteristics in advance, allowing for proactive identification of potential issues before the code reaches production environments.
2Measurement precision
If comprehensive testing is performed across all possible environments, then the detection precision of bugs is improved, but the time required for testing increases
Solution Approach 1:
Instead of executing exhaustive tests in every environment, the patent creates execution profiles that capture essential behavioral characteristics as compressed representations. These profiles enable rapid comparison to identify anomalies without requiring complete re-testing across all environments, significantly reducing testing time while maintaining detection precision through profile-based anomaly identification.
Solution Approach 2:
The system performs partial testing by executing code modules in selected test environments to generate representative profiles, rather than exhaustive testing in all possible environments. The profile comparison mechanism then identifies potential issues by detecting deviations from expected behavior patterns, achieving adequate bug detection precision with reduced testing effort and time.
3Reliability
If code changes are made to fix identified issues, then the reliability of the code module is improved, but the risk of introducing new errors increases
Solution Approach 1:
The system implements feedback by continuously comparing execution profiles before and after code changes. When code modifications are made to fix identified issues, the updated execution profile is compared against the original profile to detect any unintended behavioral changes. This feedback mechanism alerts developers to potential new errors introduced by the fix, allowing for additional validation before deployment.
Solution Approach 2:
Before deploying code changes to production, the system performs preliminary validation by generating execution profiles from the modified code and comparing them against baseline profiles. This preliminary action identifies potential regression issues or new errors introduced by the changes, allowing developers to address problems before the modified code reaches production environments.
Data Source
AI summary
A computer executes a first version of a code module in a first test environment, collects a first set of execution measurements, and creates a first profile based on the first set of execution measurements. The computer executes the first version of the code module in a second test environment, collects a second set of execution measurements, and creates a second profile based on the second set of execution measurements. In response to a difference between the first profile and the second profile exceeding a threshold, the computer provides diagnostic data characterizing the difference, analyzes an impact of the difference on the execution of the code module, calculates a risk of code module failure value based on analyzing the impact of the difference, and provides a recommended action based on analyzing the impact of the difference and the calculated risk of code module failure value.