Re-evaluating Method-Level Bug Prediction

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

8 Citations (Scopus)
22 Downloads (Pure)


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.
Original languageEnglish
Title of host publication25th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2018
Place of PublicationPiscataway, NJ
Number of pages10
ISBN (Electronic)978-1-5386-4969-5
Publication statusPublished - 2018
EventSANER 2018: 25th IEEE International Conference on Software Analysis, Evolution and ReengineeringSoftware Analysis, Evolution and Reengineering - Campobasso, Italy
Duration: 20 Feb 201823 Feb 2018
Conference number: 25


ConferenceSANER 2018
Abbreviated titleSANER
Internet address


  • empirical software engineering
  • bug prediction
  • replication
  • negative results

Fingerprint Dive into the research topics of 'Re-evaluating Method-Level Bug Prediction'. Together they form a unique fingerprint.

Cite this