Improved code quality can be accomplished by using analytics and machine learning to the code's content and meta data, as well as data from other related sources. Recommendations based just on content mining have reached relatively low accuracy (between 20% and 30%), but recommendations based on code, meta data, and other relevant sources have achieved 75–90% accuracy. In general, applying analytics and machine learning to code and code-related artifacts enables enterprises to provide faster and higher-quality code. The recommendations generated by automated impact analysis improve the quality of the code in the following ways:
- Developing and running small unit tests for the risky components/files
- Multi-layer code reviews done for hot files
- Dynamic build verification tests
- QA teams focus test on the high-risk areas
- Corresponding test cases execution cadence have increased for hot files
- Improved quality with early deduction of defects.
Recommendations generated by analytics and machine learning models aid teams in making faster decisions, but they are not a substitute for human intellect. Frequent auditing of the models' effectiveness using statistical measures such as precision, recall, and F-measure, as well as upgrading models with missing heuristics, would help ensure the models' correctness.
Additionally, automated impact analysis enables the identification of areas that will be impacted by incoming changes and the optimization of the test plan appropriately, ensuring faster feedback and maximum coverage. It enables the company to meet time-to-market objectives while delivering the highest-quality products.