Detecting and explaining self-admitted technical debts with attention-based neural networks
Xin Wang, Jin Liu, Li Li, Xiao Chen, Xiao Liu, Hao Wu
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.
Topics & Concepts
Technical debtDebtComputer scienceSoftwareProcess (computing)Term (time)Software developmentArtificial neural networkSoftware engineeringRisk analysis (engineering)Data scienceArtificial intelligenceBusinessFinanceOperating systemQuantum mechanicsPhysicsSoftware Engineering ResearchSoftware System Performance and ReliabilitySoftware Reliability and Analysis Research