Interpreting Intermittent Bugs in Mozilla Applications Using Change Angle
Learning in evolving environments involves learning from data where the statistical characteristics can change over time. Current change detection algorithms that are used online for data streams detect whether a change has occurred in the data but there is always a detection delay. None of the existing online techniques can accurately pin-point the exact location of when the change starts to occur, which can be critical. We present a novel method Change Angle and we show, for the first time, how to pin-point online the location at which change starts to occur. We apply our Change Angle method in the application area of software revision control using Mozilla data, where it is important to detect not only the presence of change but also to pin-point accurately the location of when change starts to occur.
KeywordsData streams Change detection Software repository
We thank all Mozilla engineers that were involved in the development of this research project especially Kyle Lahnakoski, Joel Maher, and Jonathan Griffin for helping us acquire access to the relevant data and validate the work.
- 1.Baena-Garcıa, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavaldà, R., Morales-Bueno, R.: Early drift detection method. In: Proceedings of the 4th International Workshop on Knowledge Discovery from Data Streams, pp. 77–86 (2006)Google Scholar
- 2.Bifet, A., Gavaldà, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 7th SIAM International Conference on Data Mining (SDM), pp. 443–448 (2007)Google Scholar
- 4.Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the 6th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 71–80 (2000)Google Scholar
- 8.Huang, D.T.J., Koh, Y.S., Dobbie, G., Bifet, A.: Drift detection using stream volatility. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9284, pp. 417–432. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23528-8_26CrossRefGoogle Scholar
- 9.Huang, D.T.J., Koh, Y.S., Dobbie, G., Pears, R.: Detecting volatility shift in data streams. In: Proceedings of the 10th IEEE ICDM, pp. 863–868 (2014)Google Scholar
- 14.Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, vol. 25 (1995)Google Scholar