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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 1: AAAI-21 Technical Tracks 1

Towards Balanced Defect Prediction with Better Information Propagation

February 1, 2023

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Authors

Xianda Zheng

School of Cyber Science and Engineering, Southeast University, Nanjing, China


Yuan-Fang Li

Faculty of Information Technology, Monash University, Melbourne, Australia


Huan Gao

Microsoft Asia-Pacific Research and Development Group, Suzhou, China


Yuncheng Hua

School of Computer Science and Engineering, Southeast University, Nanjing, China


Guilin Qi

School of Cyber Science and Engineering, Southeast University, Nanjing, China School of Computer Science and Engineering, Southeast University, Nanjing, China Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing, China


DOI:

10.1609/aaai.v35i1.16157


Abstract:

Defect prediction, the task of predicting the presence of defects in source code artifacts, has broad application in software development. Defect prediction faces two major challenges, label scarcity, where only a small percentage of code artifacts are labeled, and data imbalance, where the majority of labeled artifacts are non-defective. Moreover, current defect prediction methods ignore the impact of information propagation among code artifacts and this negligence leads to performance degradation. In this paper, we propose DPCAG, a novel model to address the above three issues. We treat code artifacts as nodes in a graph, and learn to propagate influence among neighboring nodes iteratively in an EM framework. DPCAG dynamically adjusts the contributions of each node and selects high-confidence nodes for data augmentation. Experimental results on real-world benchmark datasets show that DPCAG improves performance compare to the state-of-the-art models. In particular, DPCAG achieves substantial performance superiority when measured by Matthews Correlation Coefficient (MCC), a metric that is widely acknowledged to be the most suitable for imbalanced data.

Topics: AAAI

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Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi Towards Balanced Defect Prediction with Better Information Propagation Proceedings of the AAAI Conference on Artificial Intelligence (2021) 759-767.

Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi Towards Balanced Defect Prediction with Better Information Propagation AAAI 2021, 759-767.

Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi (2021). Towards Balanced Defect Prediction with Better Information Propagation. Proceedings of the AAAI Conference on Artificial Intelligence, 759-767.

Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi. Towards Balanced Defect Prediction with Better Information Propagation. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.759-767.

Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi. 2021. Towards Balanced Defect Prediction with Better Information Propagation. "Proceedings of the AAAI Conference on Artificial Intelligence". 759-767.

Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi. (2021) "Towards Balanced Defect Prediction with Better Information Propagation", Proceedings of the AAAI Conference on Artificial Intelligence, p.759-767

Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi, "Towards Balanced Defect Prediction with Better Information Propagation", AAAI, p.759-767, 2021.

Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi. "Towards Balanced Defect Prediction with Better Information Propagation". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.759-767.

Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi. "Towards Balanced Defect Prediction with Better Information Propagation". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 759-767.

Xianda Zheng||Yuan-Fang Li||Huan Gao||Yuncheng Hua||Guilin Qi. Towards Balanced Defect Prediction with Better Information Propagation. AAAI[Internet]. 2021[cited 2023]; 759-767.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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