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ASE 2020

Detecting and Explaining Self-Admitted Technical Debts with Attention-based Neural Networks

Xin Wang, Jin Liu, Li Li, Xiao Chen, Xiao Liu, Hao Wu

Full textDOI 10.1145/3324884.3416583dblp

Abstract

Self-Admitted Technical Debt (SATD) is a sub-type of technical debt. It is introduced to represent such technical debts that are intentionally introduced by developers in the process of software development. While being able to gain short-term benefits, the introduction of SATDs often requires to be paid back later with a higher cost, e.g., introducing bugs to the software or increasing the complexity of the software.

BibTeX
@inproceedings{Wang-al:ASE20,
  author    = {Xin Wang and
               Jin Liu and
               Li Li and
               Xiao Chen and
               Xiao Liu and
               Hao Wu},
  title     = {Detecting and Explaining {Self-Admitted} Technical Debts with Attention-based Neural Networks},
  booktitle = {ASE},
  pages     = {871--882},
  publisher = {{IEEE}},
  year      = {2020},
}

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