Developer-Related Factors in Change Prediction

An Empirical Assessment

Conference paper (2017)

Authors

G. Catolino University of Salerno
F. Palomba Software Engineering - EEMCS
Andrea De Lucia University of Salerno
Filomena Ferrucci University of Salerno
A.E. Zaidman Software Engineering - EEMCS
Research Group
Software Engineering (EEMCS) (TU Delft)
Copyright: © 2017 G. Catolino, F. Palomba, Andrea De Lucia, Filomena Ferrucci, A.E. Zaidman
DOI
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https://doi.org/10.1109/ICPC.2017.19
Mining Software Repositories Change prediction Empirical Studies
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Published Date

2017

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Software Engineering

Abstract

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.