Using BiLSTM with attention mechanism to automatically detect self-admitted technical debt

Bidirectional LSTM with attention mechanism and convolutional layer for text classification

(2019) Gang Liu et al.   NEUROCOMPUTING

A survey of self-admitted technical debt

(2019) Giancarlo Sierra et al.   JOURNAL OF SYSTEMS AND SOFTWARE

On the value of a prioritization scheme for resolving Self-admitted technical debt

(2018) Solomon Mensah et al.   JOURNAL OF SYSTEMS AND SOFTWARE

LSTM-EFG for wind power forecasting based on sequential correlation features

(2018) Ruiguo Yu et al.   Future Generation Computer Systems-The International Journal of eScience

Using Natural Language Processing to Automatically Detect Self-Admitted Technical Debt

(2017) Everton da Silva Maldonado et al.   IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

Identification and management of technical debt: A systematic mapping study

(2016) Nicolli S.R. Alves et al.   INFORMATION AND SOFTWARE TECHNOLOGY

How do software development teams manage technical debt? – An empirical study

(2016) Jesse Yli-Huumo et al.   JOURNAL OF SYSTEMS AND SOFTWARE

A systematic mapping study on technical debt and its management

(2015) Zengyang Li et al.   JOURNAL OF SYSTEMS AND SOFTWARE

An exploration of technical debt

(2013) Edith Tom et al.   JOURNAL OF SYSTEMS AND SOFTWARE

A Balancing Act: What Software Practitioners Have to Say about Technical Debt

(2012) Erin Lim et al.   IEEE SOFTWARE

Combating the Small Sample Class Imbalance Problem Using Feature Selection

(2009) Mike Wasikowski et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING