Developer-Related Factors in Change Prediction: An Empirical Assessment

Gemma Catolino, Fabio Palomba, Andrea De Lucia, Filomena Ferrucci, Andy Zaidman

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

19 Citations (Scopus)
45 Downloads (Pure)


Predicting the areas of the source code having a higher likelihood to change in the future is a crucial activity to allow developers to plan preventive maintenance operations such as refactoring or peer-code reviews. In the past the research community was active in devising change prediction models based on structural metrics extracted from the source code. More recently, Elish et al. showed how evolution metrics can be more efficient for predicting change-prone classes. In this paper, we aim at making a further step ahead by investigating the role of different developer-related factors, which are able to capture the complexity of the development process under different perspectives, in the context of change prediction. We also compared such models with existing change-prediction models based on evolution and code metrics. Our findings reveal the capabilities of developer-based metrics in identifying classes of a software system more likely to be changed in the future. Moreover, we observed interesting complementarities among the experimented prediction models, that may possibly lead to the definition of new combined models exploiting developer-related factors as well as product and evolution metrics.
Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 25th International Conference on Program Comprehension, ICPC 2017
Place of PublicationLos Alamitos, CA
Number of pages10
ISBN (Electronic)978-1-5386-0535-6
Publication statusPublished - 2017
EventICPC 2017: 25th IEEE International Conference on Program Comprehension - Buenos Aires, Argentina
Duration: 22 May 201723 May 2017
Conference number: 25


ConferenceICPC 2017
CityBuenos Aires
OtherCo-located with the 39th International Conference on Software Engineering (ICSE 2017)
Internet address


  • Change prediction
  • Empirical Studies
  • Mining Software Repositories


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