Mapping Language to Code in Programmatic Context (1808.09588v1)

Abstract: Source code is rarely written in isolation. It depends significantly on the programmatic context, such as the class that the code would reside in. To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class. This task is challenging because the desired code can vary greatly depending on the functionality the class provides (e.g., a sort function may or may not be available when we are asked to "return the smallest element" in a particular member variable list). We introduce CONCODE, a new large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment. We also present a detailed error analysis suggesting that there is significant room for future work on this task.

• The paper presents a novel encoder-decoder architecture that integrates natural language documentation with rich programmatic context, achieving a BLEU score of 22.11.
• It leverages the extensive CONCODE dataset of over 100,000 Java classes to model interactions between method documentation and surrounding code elements.
• The approach ensures syntactic validity by generating abstract syntax trees and paves the way for future research in context-aware code generation.

Mapping Language to Code in Programmatic Context

The paper "Mapping Language to Code in Programmatic Context" addresses the complex task of generating source code from natural language (NL) descriptions by leveraging the programmatic context provided by class variables and methods. Traditionally, approaches to NL-to-code generation have focused on limited contexts or specific templates, thus failing to emulate the nuanced manner in which human programmers write code, often within rich pre-existing environments.

To advance this field, the authors introduce CONCODE, a large-scale dataset with over 100,000 examples of Java classes sourced from online repositories. This dataset is distinguished by its scale and diversity, offering a broad spectrum of code templates and environments drawn from various domains.

The centerpiece of the paper is a novel encoder-decoder architecture designed to model the intricate interactions between method documentation and the surrounding class environment. This architecture comprises a specialized neural network that utilizes sub-word representations for environment identifiers (variables, methods, etc.) and data types. One of the distinctive features is a two-step attention mechanism that first focuses on NL documentation before attending to contextual variables and methods, facilitating accurate mapping and copying of relevant identifiers during code generation.

The model derives and outputs abstract syntax trees (AST) using production rules, ensuring syntactic validity—a notable direction aligning with contemporary advances in grammar-aware neural code generation. Experiments show that the model outperforms existing techniques, including various neural and retrieval-based baselines, by achieving a BLEU score of 22.11 on the newly introduced CONCODE dataset.

The implications of this work are significant both in practical and theoretical realms. Practically, this method offers enhanced precision in auto-generating class member functions, potentially streamlining workflows in software development environments reliant on large codebases. Theoretically, it pioneers pathways for future research into context-aware code generation, underscoring the importance of integrating environment knowledge into NL processing systems comprehensively.

The paper highlights the potential for continued research in error analysis and model improvements. The presented error analysis illuminates scenarios where domain-specific contexts or richer environment documentation could further enhance the precision of code generation. Moreover, this research paves the way for exploring advanced encoding techniques and attention mechanisms that could improve the model's capability to generalize identifiers and comprehend complex software domains.

Overall, "Mapping Language to Code in Programmatic Context" offers a substantial contribution to the intersection of NLP and code generation by introducing an innovative approach to leveraging program context. The insights and tools developed here set the stage for further exploration into creating more intelligent systems that effectively bridge the gap between natural language and executable code. Future developments might see these systems employed in integrated development environments (IDEs), thereby providing robust auto-completion suggestions while considering the specificities of given class contexts.