Software defect prediction via LSTM

Software quality plays an important role in the software lifecycle. Traditional software defect prediction approaches mainly focused on using hand-crafted features to detect defects. However, like human languages, programming languages contain rich semantic and structural information, and the cause...

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Veröffentlicht in:	IET software Jg. 14; H. 4; S. 443 - 450
Hauptverfasser:	Deng, Jiehan, Lu, Lu, Qiu, Shaojian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 01.08.2020
Schlagworte:	AST node sequence contextual features defective code feature extraction human languages learning (artificial intelligence) long short-term memory network LSTM machine learning techniques numerical vectors open source projects program abstract syntax trees program debugging program diagnostics programming languages public domain software recurrent neural nets Research Article semantic features software defect prediction approaches software lifecycle software quality structural information trees (mathematics) word embedding techniques program debugging open source projects long short-term memory network contextual features LSTM defective code public domain software program diagnostics AST node sequence human languages recurrent neural nets trees (mathematics) software lifecycle software defect prediction approaches software quality machine learning techniques programming languages word embedding techniques program abstract syntax trees feature extraction structural information learning (artificial intelligence) semantic features numerical vectors
ISSN:	1751-8806, 1751-8814, 1751-8814
Online-Zugang:	Volltext
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