CodeGen4Libs: A Two-Stage Approach for Library-Oriented Code Generation

Automated code generation has been extensively studied in recent literature. In this work, we first survey 66 participants to motivate a more pragmatic code generation scenario, i.e., library-oriented code generation, where the generated code should implement the functionally of the natural language...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 434 - 445
Hauptverfasser: Liu, Mingwei, Yang, Tianyong, Lou, Yiling, Du, Xueying, Wang, Ying, Peng, Xin
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 11.09.2023
Schlagworte:
Code Generation
Codes
Computer languages
Language Model
Libraries
Natural languages
Software
Surveys
Task analysis
Third-party Library
ISSN:2643-1572
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Automated code generation has been extensively studied in recent literature. In this work, we first survey 66 participants to motivate a more pragmatic code generation scenario, i.e., library-oriented code generation, where the generated code should implement the functionally of the natural language query with the given library. We then revisit existing learning-based code generation techniques and find they have limited effectiveness in such a library-oriented code generation scenario. To address this limitation, we propose a novel library-oriented code generation technique, CodeGen4Libs, which incorporates two stages: import generation and code generation. The import generation stage generates import statements for the natural language query with the given third-party libraries, while the code generation stage generates concrete code based on the generated imports and the query. To evaluate the effectiveness of our approach, we conduct extensive experiments on a dataset of 403,780 data items. Our results demonstrate that CodeGen4Libs outperforms baseline models in both import generation and code generation stages, achieving improvements of up to 97.4% on EM (Exact Match), 54.5% on BLEU, and 53.5% on Hit@All. Overall, our proposed CodeGen4Libs approach shows promising results in generating high-quality code with specific third-party libraries, which can improve the efficiency and effectiveness of software development.
ISSN:2643-1572
DOI:10.1109/ASE56229.2023.00159