Can a Simple Approach Perform Better for Cross-Project Defect Prediction?

Md. Hossain, Suravi Akhter, Md. Islam, Muhammad Alam, Mohammad Shoyaib

2024

Abstract

: Cross-Project Defect Prediction (CPDP) has gained considerable research interest due to the scarcity of historical labeled defective modules in a project. Although there are several approaches for CPDP, most of them contains several parameters that need to be tuned optimally to get the desired performance. Often, higher computational complexities of these methods make it difficult to tune these parameters. Moreover, existing methods might fail to align the shape and structure of the source and target data which in turn deteriorates the prediction performance. Addressing these issues, we investigate correlation alignment for CPDP (CCPDP) and compare it with state-of-the-art transfer learning methods. Rigorous experimentation over three benchmark datasets AEEEM, RELINK and SOFTLAB that include 46 different project-pairs, demonstrate its effectiveness in terms of F1-score, Balance and AUC compared to six other methods TCA, TCA+, JDA, BDA, CTKCCA and DMDA JFR. In terms of AUC, CCPDP wins at least 32 and at most 42 out of 46 project pairs compared to all transfer learning based method.

Paper Citation

in Harvard Style

Hossain M., Akhter S., Islam M., Alam M. and Shoyaib M. (2024). Can a Simple Approach Perform Better for Cross-Project Defect Prediction?. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-696-5, SciTePress, pages 328-335. DOI: 10.5220/0012617300003687

in Bibtex Style

@conference{enase24,
author={Md. Hossain and Suravi Akhter and Md. Islam and Muhammad Alam and Mohammad Shoyaib},
title={Can a Simple Approach Perform Better for Cross-Project Defect Prediction?},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2024},
pages={328-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012617300003687},
isbn={978-989-758-696-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Can a Simple Approach Perform Better for Cross-Project Defect Prediction?
SN - 978-989-758-696-5
AU - Hossain M.
AU - Akhter S.
AU - Islam M.
AU - Alam M.
AU - Shoyaib M.
PY - 2024
SP - 328
EP - 335
DO - 10.5220/0012617300003687
PB - SciTePress