Print Email Facebook Twitter Re-evaluating Method-Level Bug Prediction Title Re-evaluating Method-Level Bug Prediction Author Pascarella, L. (TU Delft Software Engineering) Palomba, F. (University of Zürich) Bacchelli, A. (University of Zürich) Date 2018 Abstract Bug prediction is aimed at supporting developers in the identification of code artifacts more likely to be defective. Researchers have proposed prediction models to identify bug prone methods and provided promising evidence that it is possible to operate at this level of granularity. Particularly, models based on a mixture of product and process metrics, used as independent variables, led to the best results.In this study, we first replicate previous research on method- level bug prediction on different systems/timespans. Afterwards, we reflect on the evaluation strategy and propose a more realistic one. Key results of our study show that the performance of the method-level bug prediction model is similar to what previously reported also for different systems/timespans, when evaluated with the same strategy. However—when evaluated with a more realistic strategy—all the models show a dramatic drop in performance exhibiting results close to that of a random classifier. Our replication and negative results indicate that method-level bug prediction is still an open challenge. Subject empirical software engineeringbug predictionreplicationnegative results To reference this document use: http://resolver.tudelft.nl/uuid:eaf8f53d-47f6-445b-9548-d434939c8d4a DOI https://doi.org/10.1109/SANER.2018.8330264 Publisher IEEE, Piscataway, NJ ISBN 978-1-5386-4969-5 Source 25th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2018 Event SANER 2018, 2018-02-20 → 2018-02-23, Campobasso, Italy Part of collection Institutional Repository Document type conference paper Rights © 2018 L. Pascarella, F. Palomba, A. Bacchelli Files PDF TUD_SERG_2018_006.pdf 351.66 KB Close viewer /islandora/object/uuid:eaf8f53d-47f6-445b-9548-d434939c8d4a/datastream/OBJ/view