Software Defect Prediction Based on SAINT 


Vol. 13,  No. 5, pp. 236-242, May  2024
https://doi.org/10.3745/TKIPS.2024.13.5.236


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  Abstract

Software Defect Prediction (SDP) enhances the efficiency of software development by proactively identifying modules likely to contain errors. A major challenge in SDP is improving prediction performance. Recent research has applied deep learning techniques to the field of SDP, with the SAINT model particularly gaining attention for its outstanding performance in analyzing structured data. This study compares the SAINT model with other leading models (XGBoost, Random Forest, CatBoost) and investigates the latest deep learning techniques applicable to SDP. SAINT consistently demonstrated superior performance, proving effective in improving defect prediction accuracy. These findings highlight the potential of the SAINT model to advance defect prediction methodologies in practical software development scenarios, and were achieved through a rigorous methodology including cross-validation, feature scaling, and comparative analysis.

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[IEEE Style]

S. Mohapatra, E. Ju, J. Lee, D. Ryu, "Software Defect Prediction Based on SAINT," The Transactions of the Korea Information Processing Society, vol. 13, no. 5, pp. 236-242, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.5.236.

[ACM Style]

Sriman Mohapatra, Eunjeong Ju, Jeonghwa Lee, and Duksan Ryu. 2024. Software Defect Prediction Based on SAINT. The Transactions of the Korea Information Processing Society, 13, 5, (2024), 236-242. DOI: https://doi.org/10.3745/TKIPS.2024.13.5.236.

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