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

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 automatic