@inproceedings{d01ddd338e034617b8d5c13d16a0f22f,
title = "Software Bug Prediction based on Complex Network Considering Control Flow",
abstract = "The prediction for software bug number provides vital guidance to the quality management and software testing. In this paper, a novel software bug number prediction method was proposed based on complex network considering control flow. Firstly, for each release of software, we constructed the Call Graph (CG), and for each release, Control Flow Graph (CFG) of every function were constructed. Then the CG Metrics (CGM) and CFG Metrics (CFGM) for each version were calculated with indicators from complex-network science. Finally, the results were sent to Panel Data Model (PDM) to perform the prediction on bugs fixed number. The experimental result showed that our method outperformed other prediction methods by 9.35\% to 16.85\%, and introducing CFGM reduced MAE by 5.1\% to 27.8\% than barely use CGM. The prediction of fixed bugs could indicate the software quality, and assist the quality control of software engineering.",
keywords = "Bug Prediction, Complex Network, Control Flow Graph, Panel Data Model",
author = "Zhanyi Hou and Gong, \{Ling Lin\} and Minghao Yang and Yizhuo Zhang and Shunkun Yang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2022 ; Conference date: 05-12-2022 Through 09-12-2022",
year = "2022",
doi = "10.1109/QRS-C57518.2022.00044",
language = "英语",
series = "Proceedings - 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security Companion, QRS-C 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "246--254",
booktitle = "Proceedings - 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security Companion, QRS-C 2022",
address = "美国",
}