Talks # 15: Shubhadeep Roychowdhury; Applying Machine Learning on Source Code | Summary and Q&A

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November 27, 2020
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Abhishek Thakur
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Talks # 15: Shubhadeep Roychowdhury; Applying Machine Learning on Source Code

TL;DR

Learn how machine learning can improve code documentation by automatically generating docstrings and providing type checking and bug detection.

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Questions & Answers

Q: How can machine learning be applied to code documentation?

Machine learning models can be used to generate docstrings for code functions, provide type checking, detect bugs, and even generate unit tests. These models learn from large code corpora to predict useful information about code.

Q: What are some real-world applications of machine learning on source code?

Companies like Kite, TabNine, and Microsoft are using machine learning to enhance code completion and documentation. For example, Kite offers an auto-completion engine integrated in VS Code, while TabNine provides code auto-completion using machine learning. Microsoft's Visual Studio IntelliCode also uses machine learning for AI-assisted development.

Q: What are the limitations of machine learning on source code?

One limitation is the lack of common sense and interpretability in deep learning models. They may struggle to understand concepts specific to code, such as arithmetic operations. Another limitation is the challenge of dealing with open vocabulary in code, where different developers may use different naming conventions and code styles.

Q: How does Dokley, by Codist, improve code documentation?

Dokley is a tool developed by Codist that automatically generates docstrings for Python code. It uses a machine learning model to predict the purpose and functionality of code functions, helping developers write better documentation.

Summary & Key Takeaways

  • Machine learning can be applied to source code to automate code summarization, generate docstrings, and provide type checking and bug detection.

  • Companies like Kite, TabNine, and Microsoft are already using machine learning to enhance code completion and documentation.

  • Treehugger, an open-source library by Codist, provides a unified API to analyze different programming languages and extract useful information from code.

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