App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Prediction of defect severity based on file co-editing network and structural entropy

Mao Mo1
Pan Weifeng1
Wang Jiale1
Pan Yun1
Yuan Chengxiang1
1. School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou Zhejiang 310018, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0069

Abstract

Existing software defect prediction studies are mostly limited to binary classification tasks. They cannot predict defect severity at a fine-grained level. To address this problem, this paper proposes a defect severity prediction method named FCSE-DP (FCSE-based Defect Severity Prediction) . The method uses file co-editing networks and structural entropy. First, this method constructed a File Co-editing Network (FCN) using historical change and defect data. It integrated code modification amounts and a time decay factor. The FCN captures the implicit logical dependencies generated by developer collaboration. Second, this method extracted the File Co-editing Structural Entropy (FCSE) of each file as a core feature. This feature measures the global risk of code. The method then combined FCSE with 14 traditional static code features. Finally, this method employed the LightGBM algorithm to build a multi-classification model. The model predicts defect severity levels. Experimental results on eight Apache open-source projects show that introducing FCSE improves model performance. Compared to a baseline model using only traditional static features, the LightGBM model with FCSE increased the macro-averaged F1-score from 0.253 to 0.477. The quadratic weighted kappa rose from 0.169 to 0.467. Accuracy increased from 0.553 to 0.638. The FCSE feature effectively enhances the model's ability to identify defect severity. It provides a quantitative basis for locating high-risk code modules.

Foundation Support

国家自然科学基金(62272412)
桐乡通用人工智能研究院项目(TAGI2-A-2024-0003)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0069
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-03] Accepted Paper

Cite This Article

毛墨, 潘伟丰, 王家乐, 等. 基于文件共编辑网和结构熵的缺陷严重程度预测 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0069. (Mao Mo, Pan Weifeng, Wang Jiale, et al. Prediction of defect severity based on file co-editing network and structural entropy [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0069. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)