Machine Learning Build Outcome Prediction

The patent applies preliminary action by performing static analysis and machine learning-based predictions before automated tests are executed. The system analyzes feature data from source code, build configuration, and test information to predict potential build failures in advance, allowing teams to address issues before they reach the testing phase, thus maintaining reliability while reducing actual test execution time.

The patent replaces the mechanical execution of automated tests with a machine learning-based prediction system. Instead of physically running tests to detect issues, the system uses trained models that analyze code features and build parameters to predict outcomes, substituting computational prediction for empirical testing in certain scenarios.

The patent applies partial action by selectively applying static analysis and prediction models to specific high-risk areas identified through feature data analysis, rather than uniformly testing all code paths. The machine learning model prioritizes predictions for code sections with higher failure probabilities based on historical data and code characteristics.

The patent introduces static analysis tools and machine learning prediction models as intermediaries between code development and automated testing. These intermediaries filter and identify potential issues before tests run, allowing the testing process to focus on critical areas rather than exhaustive coverage, thus reducing overall testing time while maintaining quality.

The patent implements feedback mechanisms where the machine learning model continuously learns from actual build outcomes and test results. The system uses feature data from previous builds, including source code changes, build parameters, and test outcomes, to refine predictions for future modifications. This feedback loop enables the system to adapt to new code patterns and provide increasingly accurate predictions, helping maintain integrity during rapid development cycles.

The patent applies preliminary action by providing early warnings about potential build failures before code is committed or built. The static analysis and prediction models identify risky modifications in advance, allowing developers to address issues before they affect build integrity, thus enabling faster releases without sacrificing quality.