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CodeLens: An Interactive Tool for Visualizing Code Representations

arXiv 2307.14902 ↗First posted 27 Jul 2023cs.SE cs.AI cs.LGcited by 0

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Abstract (arXiv)

Representing source code in a generic input format is crucial to automate software engineering tasks, e.g., applying machine learning algorithms to extract information. Visualizing code representations can further enable human experts to gain an intuitive insight into the code. Unfortunately, as of today, there is no universal tool that can simultaneously visualise different types of code representations. In this paper, we introduce a tool, CodeLens, which provides a visual interaction environment that supports various representation methods and helps developers understand and explore them. CodeLens is designed to support multiple programming languages, such as Java, Python, and JavaScript, and four types of code representations, including sequence of tokens, abstract syntax tree (AST), data flow graph (DFG), and control flow graph (CFG). By using CodeLens, developers can quickly visualize the specific code representation and also obtain the represented inputs for models of code. The Web-based interface of CodeLens is available at http://www.codelens.org. The demonstration video can be found at http://www.codelens.org/demo.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 27 Jul 2023.

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References · 12

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  3. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
  4. 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 744
  5. 2020GraphCodeBERT: Pre-training Code Representations with Data Flowcited by 609
  6. 2019Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networkscited by 272
  7. 2018code2vec: Learning Distributed Representations of Codecited by 250
  8. 2018code2seq: Generating Sequences from Structured Representations of Codecited by 179
  9. 2021Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLPcited by 132
  10. 2021GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Searchcited by 15
  11. 2022CodeS: Towards Code Model Generalization Under Distribution Shiftcited by 6
  12. 2022Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detectioncited by 3

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