Our Predictive Test Selection model stands out due to its foundation in multi-year research, which has led to significant breakthroughs including the establishment of a provable optimal strategy. It estimates the probability of failure for each test, utilizing historical data on code changes and past test results. It is designed to continuously learn and adapt to your environment.

Recognizing the challenges posed by lack of information, it employs advanced hierarchical clustering methods, such as collaborative filtering, ensuring accurate predictions even for new tests and code components. Additionally, the model is time-aware, incorporating temporal data to effectively address the rapid changes occurring within the codebase.

Predicting over 99.96% of test failures and effectively avoiding unnecessary tests.

Clustering techniques for optimal predictions

Employing five distinct test clustering techniques to systematically handle scenarios characterized by limited historical data.

Optimal Avg Time to Failure strategy

Our research team has established a mathematical proof affirming that for optimal Recall AUC and ATTF, tests should be executed in descending order.