Computer Science ›› 2022, Vol. 49 ›› Issue (7): 212-219.doi: 10.11896/jsjkx.210500075

• Artificial Intelligence • Previous Articles     Next Articles

Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism

XIONG Luo-geng, ZHENG Shang, ZOU Hai-tao, YU Hua-long, GAO Shang   

  1. School of Computer,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212100,China
  • Received:2021-05-12 Revised:2021-09-06 Online:2022-07-15 Published:2022-07-12
  • About author:XIONG Luo-geng,born in 1996,postgraduate.His main research interests include intelligent software engineering and so on.
    ZHENG Shang,born in 1983,Ph.D,associate professor,master's supervisor,is a member of China Computer Federation.His main research interests include intelligent software engineering and data mining.
  • Supported by:
    Natural Science Research Foundation for Higher Education of Jiangsu Province(18JBK520011),Primary Research and Development Plan(Social Development) of Zhenjiang(SH2019021) and Natural Science Foundation of Jiangsu Province(BK20191457).

Abstract: Software self-admitted technical debt(SATD) is written into the source code comments of the project by developers who leave a note admitting incurring intentionally for short-term benefits,and a large amount of SATD will be dangerous to software maintenance.In recent years,more scholars focus on the research of software SATD recognition and propose different identification approaches,such as SATD detection based on natural language processing or text mining.However,the identification results of most previous studies are not very well due to the existing thesaurus or manually extracted features,which not only consumes a lot of time,but also increases computational complexity.Therefore,a software SATD identification approach based on bidirectional gated recurrent unit(GRU) and attention mechanism is proposed.The word vector is obtained first through the Skip-gram model,and the bidirectional GRU network is constructed to obtain the high-level features.Finally,the attention mechanism is used to automatically discover words that play a key role in SATD identification,and the most important semantic information can be captured.Experimental results show that the proposed approach has excellent performance in precision,recall and F1-score.It can effectively identify software SATD and avoid complex feature engineering in traditional tasks.

Key words: Attention mechanism, GRU, SATD, Software maintenance, Word2vec

CLC Number: 

  • TP311
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