A Mocktail of Source Code Representations

Vagavolu, Dheeraj; Swarna, Karthik Chandra; Chimalakonda, Sridhar

doi:10.1109/ase51524.2021.9678551

Cited by 11 publications

(2 citation statements)

References 28 publications

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“…V. Hellendoorn et al [19] propose a model called GREAT based on the transformer architecture by extracting global relational information from code graphs. D. Vagavolu et al [3] propose an approach that can extract and use program features from multiple code graphs. M. Lu et al [6] improved GGNN in program classification.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, source code processing, which tries to help computers automatically comprehend and analyze source code, has received a lot of attention. Several works have been suggested including code classification [1]- [6], method name prediction [3] [4] [7] [8], code summarization [3] [9] [10] and code clone detection [5] [11] [12], etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification

Yang¹,

Jin²,

Dou³

2023

International Conferences on Software Engineering and Knowledge Engineering

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification

Yang¹,

Jin²,

Dou³

2023

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Boosting Metamorphic Relation Prediction via Code Representation Learning: An Empirical Study

Zheng,

Jiang,

Quan Zhou

2024

Software Testing Verif & Rel

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Metamorphic testing (MT) is an effective testing technique having a broad range of applications. One key task for MT is the identification of metamorphic relations (MRs), which is a fundamental mechanism in MT and is critical to the automation of MT. Prior studies have proposed approaches for predicting MRs (PMR). One major idea behind these PMR approaches is to represent program source code information via manually designed code features and then to apply machine‐learning–based classifiers to automatically predict whether a specific MR can be applied on the target program. Nevertheless, the human‐involved procedure of selecting and extracting code features is costly, and it may not be easy to obtain sufficiently comprehensive features for representing source code. To overcome this limitation, in this study, we explore and evaluate the effectiveness of code representation learning techniques for PMR. By applying neural code representation models for automatically mapping program source code to code vectors, the PMR procedure can be boosted with learned code representations. We develop 32 PMR instances by, respectively, combining 8 code representation models with 4 typical classification models and conduct an extensive empirical study to investigate the effectiveness of code representation learning techniques in the context of MR prediction. Our findings reveal that code representation learning can positively contribute to the prediction of MRs and provide insights into the practical usage of code representation models in the context of MR prediction. Our findings could help researchers and practitioners to gain a deeper understanding of the strength of code representation learning for PMR and, hence, pave the way for future research in deriving or extracting MRs from program source code.

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