Automated Code Testing System for Latency Measurement
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Solution Overview
Problem
Current methods for testing candidate code in web services and e-commerce applications are manually intensive and lack efficiency in measuring latency, making it difficult to determine if new code introduces unacceptable levels of latency before deployment.
Innovation Solution
A code testing system that uses a testing server to process production data alongside production servers, collecting pre-deployment and post-deployment performance data, and applying statistical tests like the student t-test and linear regression to accurately measure and analyze latency changes, preventing deployment of latency-causing code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual review methods are used to test candidate code, then operational complexity is reduced, but measurement precision and productivity deteriorate
Solution Approach 1:
The patent replaces manual mechanical review processes with automated statistical testing systems. Multiple statistical tests (t-test, linear regression, chi-square) automatically analyze performance data to detect latency changes, substituting human manual inspection with computational analysis that provides superior measurement precision while maintaining manageable system complexity through standardized testing procedures.
2Device complexity
If manual review methods are used to test candidate code, then device complexity is reduced, but productivity worsens
Solution Approach 1:
The testing system performs self-service automated statistical analysis without requiring manual intervention. The system automatically collects performance data, executes multiple statistical tests, compares pre-deployment and post-deployment metrics, and generates deployment recommendations, enabling rapid continuous integration/continuous deployment (CI/CD) pipelines that significantly improve code deployment productivity.
3Measurement precision
If automated statistical testing is implemented, then measurement precision improves, but device complexity worsens
Solution Approach 1:
The patent segments the testing system into distinct modular components: data collection modules that gather performance metrics, separate statistical test execution modules (t-test, linear regression, chi-square), and analysis modules that interpret results. This segmentation allows each component to be independently developed, maintained, and optimized, managing overall system complexity while achieving high measurement precision through specialized statistical analyses.
4Reliability
If comprehensive statistical tests are applied, then reliability improves, but loss of time worsens
Solution Approach 1:
The patent implements a tiered statistical testing approach where multiple statistical tests are applied, but the system can be configured to execute only essential tests based on risk tolerance and performance requirements. Critical path tests with highest impact on deployment reliability are executed first, allowing partial completion of the testing suite when time constraints exist, balancing reliability assurance with testing duration through selective execution of the most impactful statistical analyses.
Data Source
AI summary
A system for testing candidate code to determine if the candidate code is approved for deployment to a production environment. The system may include a code testing engine configured to test a first code set of previously approved code to a testing server and a production environment including multiple production servers. At a deployment time, the code testing engine may then deploy candidate code to the testing server, while the production servers execute the first code set. Performance data for a time period before the deployment time and after the deployment time is collected. Latency data sets are fetched from the performance data sets and compared using multiple statistical tests. If the multiple statistical tests generate passing results, the candidate code is approved for deployment to the production environment.