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View all- Wang SGeng MLin BSun ZWen MLiu YLi LBissyandé TMao X(2024)Fusing Code SearchersIEEE Transactions on Software Engineering10.1109/TSE.2024.340304250:7(1852-1866)Online publication date: Jul-2024
Natural Language to Code: How Far Are We?
Authors: 
Shangwen Wang, 
Mingyang Geng, Bo Lin, Zhensu Sun, 
Ming Wen, Yepang Liu, Li Li, Tegawendé F. Bissyandé, Xiaoguang MaoAuthors Info & Claims
Pages 375 - 387
Abstract
A longstanding dream in software engineering research is to devise effective approaches for automating development tasks based on developers' informally-specified intentions. Such intentions are generally in the form of natural language descriptions. In recent literature, a number of approaches have been proposed to automate tasks such as code search and even code generation based on natural language inputs. While these approaches vary in terms of technical designs, their objective is the same: transforming a developer's intention into source code. The literature, however, lacks a comprehensive understanding towards the effectiveness of existing techniques as well as their complementarity to each other. We propose to fill this gap through a large-scale empirical study where we systematically evaluate natural language to code techniques. Specifically, we consider six state-of-the-art techniques targeting code search, and four targeting code generation. Through extensive evaluations on a dataset of 22K+ natural language queries, our study reveals the following major findings: (1) code search techniques based on model pre-training are so far the most effective while code generation techniques can also provide promising results; (2) complementarity widely exists among the existing techniques; and (3) combining the ten techniques together can enhance the performance for 35% compared with the most effective standalone technique. Finally, we propose a post-processing strategy to automatically integrate different techniques based on their generated code. Experimental results show that our devised strategy is both effective and extensible.
Supplementary Material
"Code reuse is an important part of software development. The adoption of code reuse practices is especially common among Node.js developers. The Node.js package manager, NPM, indexes over 1 Million packages and developers often seek out packages to solve programming tasks. Due to the vast number of packages, selecting the right package is difficult and time consuming. With the goal of improving productivity of developers that heavily reuse code through third-party packages, we present Node Code Query (NCQ), a Read-Eval-Print-Loop environment that allows developers to 1) search for NPM packages using natural language queries, 2) search for code snippets related to those packages, 3) automatically correct errors in these code snippets, 4) quickly setup new environments for testing those snippets, and 5) transition between search and editing modes. In two user studies with a total of 20 participants, we find that participants begin programming faster and conclude tasks faster with NCQ than with baseline approaches, and that they like, among other features, the search for code snippets and packages. Our results suggest that NCQ makes Node.js developers more efficient in reusing code. "
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November 2023
2215 pages
ISBN:9798400703270
DOI:10.1145/3611643
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View all- Wang SGeng MLin BSun ZWen MLiu YLi LBissyandé TMao X(2024)Fusing Code SearchersIEEE Transactions on Software Engineering10.1109/TSE.2024.340304250:7(1852-1866)Online publication date: Jul-2024
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