Automated tests of software can often independently log different bugs for the same underlying problem (root cause). Manually identifying duplicate bugs is a source of toil for engineers. A related problem of bug management is that of bug routing, e.g., determining the right team or person to route a bug report to for the purposes of debugging. This disclosure describes techniques for bug deduplication and bug routing based on machine learning (ML). Per the techniques, a binary machine classification model is trained to aggregate bugs with a common root cause. Bugs in a class of bugs with a common root cause are deduplicated, e.g., represented by just one of the multiple bugs in the class. Further, a multi-class ML model is trained to predict the right team for handling a new (incoming) bug.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.