ESEIW 2022
Sun 18 - Fri 23 September 2022 Helsinki, Finland
Fri 23 Sep 2022 11:40 - 12:00 at Sonck - Session 4B - Code Review & Defects Chair(s): Per Runeson

Background: Defect Prediction research aims at assisting software engineers in the early identification of software defect during the development process. A variety of automated approaches, ranging from traditional classification models to more sophisticated learning approaches, have been explored to this end. Among these, recent studies have proposed the use of ensemble prediction models (i.e., aggregation of multiple base classifiers) to build more robust defect prediction models.

Aims: In this paper, we introduce a novel approach based on multi-objective evolutionary search to automatically generate defect prediction ensembles. Our proposal is not only novel with respect to the more general area of evolutionary generation of ensembles, but it also advances the state-of-the-art in the use of ensemble in defect prediction.

Method: We assess the effectiveness of our approach, dubbed as {\sc \textbf{M}ulti-objective Ensemble Generation (MEG), by empirically benchmarking it with respect to the most related proposals we found in the literature on defect prediction ensembles and on multi-objective evolutionary ensembles (which, to the best of our knowledge, had never been previously applied to tackle defect prediction).

Result: Our results show that MEG is able to generate ensembles which produce similar or more accurate predictions than those achieved by all the other approaches considered in 73% of the cases (with favourable large effect sizes in 80% of them.

Conclusions: MEG is not only able to generate ensembles that yield more accurate defect predictions with respect to the benchmarks considered, but it also does it automatically, thus relieving the engineers from the burden of manual design and experimentation.

Fri 23 Sep

Displayed time zone: Athens change

11:00 - 12:30
Session 4B - Code Review & DefectsESEM Technical Papers / ESEM Emerging Results and Vision Papers / ESEM Journal-First Papers at Sonck
Chair(s): Per Runeson Lund University
To What Extent Cognitive-Driven Development Improves Code Readability?
ESEM Technical Papers
Leonardo Barbosa UFPA, Victor Santiago UFPA, Alberto de Souza Zup Innovation, Gustavo Pinto Federal University of Pará (UFPA) and Zup Innovation
Only Time Will Tell: Modelling Information Diffusion in Code Review with Time-Varying Hypergraphs
ESEM Technical Papers
Michael Dorner Blekinge Institute of Technology, Darja Šmite Blekinge Institute of Technology, Daniel Mendez Blekinge Institute of Technology, Krzysztof Wnuk Blekinge Institute of Technology , Jacek Czerwonka Developer Services, Microsoft
DOI Pre-print
MEG: Multi-objective Ensemble Generation for Software Defect Prediction
ESEM Technical Papers
Rebecca Moussa University College London, Giovani Guizzo University College London, Federica Sarro University College London
Towards a taxonomy of code review smells
ESEM Journal-First Papers
Emre Doğan Bilkent University, Eray Tüzün Bilkent University
Vision and Emerging Results
Example Driven Code Review Explanation
ESEM Emerging Results and Vision Papers
Shadikur Rahman York University, Umme Ayman Koana York University, Maleknaz Nayebi York University