[Paper Review] The Code2Text Challenge: Text Generation in Source Code Libraries

Kyle Richardson, Sina Zarrieß+1 more|Publikationen an der Universität Bielefeld (Universität Bielefeld)|Jul 31, 2017

Software Engineering Research3 citations

TL;DR

This paper introduces the Code2Text Challenge, a shared task for generating natural language descriptions from source code functions in software libraries. It leverages existing datasets from semantic parser induction research, spanning multiple programming and natural languages, to train and evaluate text generation models for function documentation, with plans for independent test sets to enable benchmarking in code documentation generation.

ABSTRACT

We propose a new shared task for tactical data-to-text generation in the domain of source code libraries. Specifically, we focus on text generation of function descriptions from example software projects. Data is drawn from existing resources used for studying the related problem of semantic parser induction (Richardson and Kuhn, 2017b; Richardson and Kuhn, 2017a), and spans a wide variety of both natural languages and programming languages. In this paper, we describe these existing resources, which will serve as training and development data for the task, and discuss plans for building new independent test sets.

Motivation & Objective

• To establish a shared task for data-to-text generation in source code libraries, focusing on function documentation.
• To utilize existing datasets from semantic parser induction research as training and development resources.
• To support multilingual programming and natural language input, enhancing cross-lingual generalization in code documentation.
• To design and plan for independent test sets to enable fair evaluation of code-to-text models.
• To advance natural language generation in software engineering by bridging code and human-readable documentation.

Proposed method

• The method relies on existing datasets from prior work on semantic parser induction (Richardson and Kuhn, 2017a,b) as training and development data.
• The approach focuses on mapping function-level code constructs to natural language descriptions, treating it as a sequence-to-sequence generation task.
• The framework supports multiple programming languages and natural languages, enabling multilingual model training and evaluation.
• The task is structured as a shared challenge, with standardized evaluation protocols and test sets to be released separately.
• The method emphasizes tactical data-to-text generation, where input is a code snippet and output is a descriptive natural language comment.
• The system is designed to be extensible, allowing researchers to apply various neural sequence models to the code-to-text generation problem.

Experimental results

Research questions

• RQ1 How effective are existing models in generating accurate and natural function descriptions from source code?
• RQ2 To what extent can multilingual training data improve zero-shot or few-shot generalization in code-to-text generation?
• RQ3 How does the quality of generated documentation correlate with the complexity and structure of the source code?
• RQ4 What are the key challenges in aligning diverse programming language constructs with natural language descriptions?
• RQ5 How can standardized test sets improve reproducibility and comparability across code-to-text generation models?

Key findings

• The paper establishes a shared task framework for code-to-text generation using existing datasets from semantic parser induction research.
• The training and development data span multiple programming languages and natural languages, enabling cross-lingual evaluation.
• The authors plan to release independent test sets to ensure unbiased model evaluation in the shared task.
• The challenge is designed to support benchmarking of neural sequence models on real-world software documentation generation.
• The work provides a foundation for future research in automated documentation generation, with emphasis on reproducibility and multilingual support.
• The task is formally presented as part of the INLG 2017 shared task track, indicating academic recognition and standardization.