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

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.

Keywords

• software maintenance|satd|word2vec|attention mechanism|gru