An approach for API synthesis using large language models

• URL: http://arxiv.org/abs/2502.15246v1
• Date: Fri, 21 Feb 2025 06:42:20 GMT
• Title: An approach for API synthesis using large language models
• Authors: Hua Zhong, Shan Jiang, Sarfraz Khurshid,
• Abstract summary: We present a novel approach of using large language models (LLMs) in API synthesis.<n>LLMs offer a foundational technology to capture developer insights and provide an ideal framework for enabling more effective API synthesis.<n>We perform an experimental evaluation of our approach using 135 real-world programming tasks, and compare it with FrAngel, a state-of-the-art API synthesis tool.
• Score: 5.222592525740847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: APIs play a pivotal role in modern software development by enabling seamless communication and integration between various systems, applications, and services. Component-based API synthesis is a form of program synthesis that constructs an API by assembling predefined components from a library. Existing API synthesis techniques typically implement dedicated search strategies over bounded spaces of possible implementations, which can be very large and time consuming to explore. In this paper, we present a novel approach of using large language models (LLMs) in API synthesis. LLMs offer a foundational technology to capture developer insights and provide an ideal framework for enabling more effective API synthesis. We perform an experimental evaluation of our approach using 135 real-world programming tasks, and compare it with FrAngel, a state-of-the-art API synthesis tool. The experimental results show that our approach completes 133 of the tasks, and overall outperforms FrAngel. We believe LLMs provide a very useful foundation for tackling the problem of API synthesis, in particular, and program synthesis, in general.

Related papers

• Creating an LLM-based AI-agent: A high-level methodology towards enhancing LLMs with APIs [0.0]
  Large Language Models (LLMs) have revolutionized various aspects of engineering and science.<n>This thesis serves as a comprehensive guide that elucidates a multi-faceted approach for empowering LLMs with the capability to leverage Application Programming Interfaces (APIs)<n>We propose an on-device architecture that aims to exploit the functionality of carry-on devices by using small models from the Hugging Face community.
  arXiv  Detail & Related papers  (2024-12-17T14:14:04Z)
• ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration [70.26807758443675]
  ExploraCoder is a training-free framework that empowers large language models to invoke unseen APIs in code solution.<n>We show that ExploraCoder significantly improves performance for models lacking prior API knowledge, achieving an absolute increase of 11.24% over niave RAG approaches and 14.07% over pretraining methods in pass@10.
  arXiv  Detail & Related papers  (2024-12-06T19:00:15Z)
• Demystifying Application Programming Interfaces (APIs): Unlocking the Power of Large Language Models and Other Web-based AI Services in Social Work Research [0.0]
  Application Programming Interfaces (APIs) are essential tools for social work researchers aiming to harness advanced technologies like Large Language Models (LLMs) and other AI services. This paper demystifies APIs and illustrates how they can enhance research methodologies. Practical code examples demonstrate how LLMs can generate API code for accessing specialized services, such as extracting data from unstructured text.
  arXiv  Detail & Related papers  (2024-10-26T16:07:12Z)
• ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis [80.34000499166648]
  We propose a Graph-based Sampling strategy to sample more relevant tool combinations, and a Planned-generation strategy to create plans that guide the synthesis of coherent dialogues. We apply SFT on LLaMA-3.1-8B using 8,000 synthetic dialogues generated with ToolFlow. Results show that the model achieves tool-calling performance comparable to or even surpassing GPT-4, while maintaining strong general capabilities.
  arXiv  Detail & Related papers  (2024-10-24T05:45:04Z)
• A Solution-based LLM API-using Methodology for Academic Information Seeking [49.096714812902576]
  SoAy is a solution-based LLM API-using methodology for academic information seeking. It uses code with a solution as the reasoning method, where a solution is a pre-constructed API calling sequence. Results show a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines.
  arXiv  Detail & Related papers  (2024-05-24T02:44:14Z)
• Semantic API Alignment: Linking High-level User Goals to APIs [6.494714497852088]
  We present a vision to span multiple steps from requirements engineering to implementation using existing libraries. This approach, which we call Semantic API Alignment (SEAL), aims to bridge the gap between a user's high-level goals and the specific functions of one or more APIs.
  arXiv  Detail & Related papers  (2024-05-07T11:54:32Z)
• Octopus: On-device language model for function calling of software APIs [9.78611123915888]
  Large Language Models (LLMs) play a crucial role due to their advanced text processing and generation abilities. This study introduces a new strategy aimed at harnessing on-device LLMs in invoking software APIs.
  arXiv  Detail & Related papers  (2024-04-02T01:29:28Z)
• Lightweight Syntactic API Usage Analysis with UCov [0.0]
  We present a novel conceptual framework designed to assist library maintainers in understanding the interactions allowed by their APIs. These customizable models enable library maintainers to improve their design ahead of release, reducing friction during evolution. We implement these models for Java libraries in a new tool UCov and demonstrate its capabilities on three libraries exhibiting diverse styles of interaction.
  arXiv  Detail & Related papers  (2024-02-19T10:33:41Z)
• LILO: Learning Interpretable Libraries by Compressing and Documenting Code [71.55208585024198]
  We introduce LILO, a neurosymbolic framework that iteratively synthesizes, compresses, and documents code. LILO combines LLM-guided program synthesis with recent algorithmic advances in automated from Stitch. We find that AutoDoc boosts performance by helping LILO's synthesizer to interpret and deploy learned abstractions.
  arXiv  Detail & Related papers  (2023-10-30T17:55:02Z)
• ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs [104.37772295581088]
  Open-source large language models (LLMs), e.g., LLaMA, remain significantly limited in tool-use capabilities. We introduce ToolLLM, a general tool-usetuning encompassing data construction, model training, and evaluation. We first present ToolBench, an instruction-tuning framework for tool use, which is constructed automatically using ChatGPT.
  arXiv  Detail & Related papers  (2023-07-31T15:56:53Z)
• Leveraging Language to Learn Program Abstractions and Search Heuristics [66.28391181268645]
  We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization.
  arXiv  Detail & Related papers  (2021-06-18T15:08:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.