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- Title
MPT‐embedding: An unsupervised representation learning of code for software defect prediction.
- Authors
Shi, Ke; Lu, Yang; Liu, Guangliang; Wei, Zhenchun; Chang, Jingfei
- Abstract
Software project defect prediction can help developers allocate debugging resources. Existing software defect prediction models are usually based on machine learning methods, especially deep learning. Deep learning‐based methods tend to build end‐to‐end models that directly use source code‐based abstract syntax trees (ASTs) as input. They do not pay enough attention to the front‐end data representation. In this paper, we propose a new framework to represent source code called multiperspective tree embedding (MPT‐embedding), which is an unsupervised representation learning method. MPT‐embedding parses the nodes of ASTs from multiple perspectives and encodes the structural information of a tree into a vector sequence. Experiments on both cross‐project defect prediction (CPDP) and within‐project defect prediction (WPDP) show that, on average, MPT‐embedding provides improvements over the state‐of‐the‐art method.
- Subjects
COMPUTER programming education; DEEP learning; SOURCE code; MACHINE learning; COMPUTER software; IMAGE representation
- Publication
Journal of Software: Evolution & Process, 2021, Vol 33, Issue 4, p1
- ISSN
2047-7473
- Publication type
Article
- DOI
10.1002/smr.2330