Artificial Intelligence Methods for Services and Product Sustaining Phase
Résumé
The main problem the project addresses is reducing time and effort spent by a vendor company on product sustaining phase. This is done by development of the approach for assisting with customer request resolution based on intellectual methods and machine learning algorithms. Solution collects resolved issues from bug tracking systems, local documentation and confluence pages, creates their respective vector models and teaches the algorithm on the data. For each retrieved unresolved issue, related resolved ones and documentation pages are found as a result of using trained algorithm. System also collects user-written rules, that are based on information from the issue, and checks them on each unresolved issue to give recommendations regarding its status change or additional needed information. Based on received information, the final report is constructed to show related issues, documentation and recommendations for each unresolved issue in a user-friendly manner. System was tested on Apache Kafka project issues and compared to manual approach performed on same data. The average time to analyze unresolved issues using the automated approach was 12.2 minutes, and the average time spent with the manual approach was 18.4 minutes, which means that our solution decreases complexity of issue analysis by ~33%.