A controlled experiment of different code representations for learning-based program repair

Training a deep learning model on source code has gained significant traction recently. Since such models reason about vectors of numbers, source code needs to be converted to a code representation before vectorization. Numerous approaches have been proposed to represent source code, from sequences...

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Veröffentlicht in:	Empirical software engineering : an international journal Jg. 27; H. 7
Hauptverfasser:	Namavar, Marjane, Nashid, Noor, Mesbah, Ali
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2022 Springer Nature B.V
Schlagworte:	Compilers Computer Science Deep learning Interpreters Model accuracy Programming Languages Representations Software Engineering/Programming and Operating Systems Source code Special Issue on Software Testing in the Machine Learning Era Deep learning Program repair Code representation Controlled experiment
ISSN:	1382-3256, 1573-7616
Online-Zugang:	Volltext
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