Bug Report Duplicate Detection via ML Classification

The system performs preliminary duplicate detection by comparing incoming bug reports against existing reports in the database using machine learning algorithms before the reports are fully processed. This preliminary action identifies potential duplicates early in the workflow, preventing redundant debugging work from occurring in the first place.

The system incorporates feedback loops where detected duplicate relationships are continuously learned and used to improve future duplicate detection accuracy. User confirmations of duplicate identifications feed back into the training data, refining the machine learning model's ability to distinguish true duplicates from unique bugs over time.

The patent introduces an intermediary processing layer that includes feature extraction modules, similarity computation engines, and classification algorithms. This intermediary layer sits between raw bug report data and the final duplicate determination, breaking down the complex task into manageable stages: extracting relevant features from reports, computing similarity metrics, applying machine learning classification, and presenting results for confirmation.

The duplicate detection system is segmented into independent modular components: bug report ingestion modules, feature extraction modules, similarity computation modules, machine learning classification modules, and result presentation modules. Each module handles a specific aspect of the detection process, allowing for independent development, testing, and optimization of individual components.