Automated Code Change Confidence Scoring for Deployment
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Solution Overview
Problem
The existing code change request management process is time-consuming and inefficient due to manual gatekeeping, leading to increased turnaround times and potential capital losses, as it requires lengthy approvals and human intervention, which delays the deployment of new code.
Innovation Solution
A computer-implemented method that collects data from a development system to compute a confidence score for code change requests, allowing for human gate-free promotion of new code when the score is high, and indicating the need for human intervention when the score is low, while considering developer availability and environment health factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual gatekeeping process is used for code change requests, then code quality assurance is improved, but deployment time and productivity deteriorate
Solution Approach 1:
The gatekeeping process is segmented into automated risk assessment (confidence score calculation) and human review (when needed). The system divides the traditional monolithic manual review into discrete automated checks that evaluate multiple factors independently, allowing high-confidence changes to bypass human review entirely.
Solution Approach 2:
An automated confidence score calculation system acts as an intermediary between code submission and human gatekeeping. This intermediary evaluates multiple data factors and determines whether human review is necessary, filtering out low-risk changes before they reach human reviewers.
2Productivity
If human gatekeeping is eliminated for fast deployment, then productivity is improved, but code quality assurance and reliability deteriorate
Solution Approach 1:
The system performs preliminary automated risk assessment and confidence score calculation before deployment decisions are made. By evaluating multiple data factors in advance, the system prepares comprehensive risk information that enables informed automated approval decisions without compromising quality assurance.
Solution Approach 2:
The system continuously learns from deployment outcomes and feedback data, refining its confidence score calculations over time. Successful deployments reinforce the automated process, while issues trigger re-evaluation and adjustment of risk factors, creating a self-improving quality assurance mechanism.
3Measurement precision
If comprehensive data analysis is performed for each code change request, then assessment accuracy is improved, but processing time and system complexity worsen
Solution Approach 1:
The system uses a universal confidence score calculation framework that handles multiple different data factors through a single integrated process. The same computational logic evaluates diverse inputs (developer history, code metrics, test results, environment factors) uniformly, reducing the need for separate complex evaluation systems for each factor type.
Solution Approach 2:
The system dynamically adjusts the weight and relevance of different data factors based on the specific context of each code change request. Rather than using fixed complex rules, the system modifies parameter importance according to the situation, simplifying the evaluation process while maintaining high assessment accuracy.
Data Source
AI summary
A computer-implemented method, in accordance with one aspect of the present invention, includes collecting data existing in a development system that relates to the code change request, in response to receiving a code change request to merge new code with existing code. Factors from the collected data are computed for assessing a risk of promoting the new code, the factors including at least: a developer information factor, a developer availability factor, and an environment health analysis factor. The factors are processed to compute a confidence score for the code change request. If the confidence score is in a first predefined range, the new code corresponding to the code change request is promoted for merging with the existing code, without human intervention. If the confidence score is in a second predefined range, an indication that human intervention is needed before promoting the code is output.