Automated Update of Android Deprecated API Usages with Large Language Models

• URL: http://arxiv.org/abs/2411.04387v1
• Date: Thu, 07 Nov 2024 03:01:50 GMT
• Title: Automated Update of Android Deprecated API Usages with Large Language Models
• Authors: Tarek Mahmud, Bin Duan, Meiru Che, Awatif Yasmin, Anne H. H. Ngu, Guowei Yang,
• Abstract summary: This paper introduces GUPPY, an automated approach that utilizes large language models (LLMs) to update Android API usages. GUPPY uses GPT-4, one of the most powerful LLMs, to update deprecated-API usages, ensuring compatibility in both the old and new API levels. Our evaluation, conducted on 360 benchmark API usages from 20 deprecated APIs and an additional 156 deprecated API usages from the latest API levels 33 and 34, demonstrates GUPPY's advantages over the state-of-the-art techniques.
• Score: 8.507162624206956
• License:
• Abstract: Android apps rely on application programming interfaces (APIs) to access various functionalities of Android devices. These APIs however are regularly updated to incorporate new features while the old APIs get deprecated. Even though the importance of updating deprecated API usages with the recommended replacement APIs has been widely recognized, it is non-trivial to update the deprecated API usages. Therefore, the usages of deprecated APIs linger in Android apps and cause compatibility issues in practice. This paper introduces GUPPY, an automated approach that utilizes large language models (LLMs) to update Android deprecated API usages. By employing carefully crafted prompts, GUPPY leverages GPT-4, one of the most powerful LLMs, to update deprecated-API usages, ensuring compatibility in both the old and new API levels. Additionally, GUPPY uses GPT-4 to generate tests, identify incorrect updates, and refine the API usage through an iterative process until the tests pass or a specified limit is reached. Our evaluation, conducted on 360 benchmark API usages from 20 deprecated APIs and an additional 156 deprecated API usages from the latest API levels 33 and 34, demonstrates GUPPY's advantages over the state-of-the-art techniques.

Related papers

• A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How [53.65636914757381]
  API suggestion is a critical task in modern software development. Recent advancements in large code models (LCMs) have shown promise in the API suggestion task.
  arXiv  Detail & Related papers  (2024-09-20T03:12:35Z)
• FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking [57.53742155914176]
  API call generation is the cornerstone of large language models' tool-using ability. Existing supervised and in-context learning approaches suffer from high training costs, poor data efficiency, and generated API calls that can be unfaithful to the API documentation and the user's request. We propose an output-side optimization approach called FANTASE to address these limitations.
  arXiv  Detail & Related papers  (2024-07-18T23:44:02Z)
• WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment [49.00213183302225]
  We propose a framework to induce new APIs by grounding wikiHow instruction to situated agent policies. Inspired by recent successes in large language models (LLMs) for embodied planning, we propose a few-shot prompting to steer GPT-4.
  arXiv  Detail & Related papers  (2024-07-10T15:52:44Z)
• CodeUpdateArena: Benchmarking Knowledge Editing on API Updates [77.81663273436375]
  We present CodeUpdateArena, a benchmark for knowledge editing in the code domain. An instance in our benchmark consists of a synthetic API function update paired with a program synthesis example. Our benchmark covers updates of various types to 54 functions from seven diverse Python packages.
  arXiv  Detail & Related papers  (2024-07-08T17:55:04Z)
• A Large-scale Investigation of Semantically Incompatible APIs behind Compatibility Issues in Android Apps [13.24503570840706]
  We conduct a large-scale discovery of incompatible APIs in the Android Open Source Project (AOSP) We propose a unified framework to detect incompatible APIs, especially for semantic changes. Our approach detects 5,481 incompatible APIs spanning from version 4 to version 33.
  arXiv  Detail & Related papers  (2024-06-25T10:12:37Z)
• APIGen: Generative API Method Recommendation [16.541442856821]
  APIGen is a generative API recommendation approach through enhanced in-context learning (ICL) APIGen searches for similar posts to the programming queries from the lexical, syntactical, and semantic perspectives. With the reasoning process, APIGen makes recommended APIs better meet the programming requirement of queries.
  arXiv  Detail & Related papers  (2024-01-29T02:35:42Z)
• Exception-aware Lifecycle Model Construction for Framework APIs [4.333061751230906]
  This paper adopts a static analysis technique to extract exception summary information in the framework API code. It generates exception-aware API lifecycle models for the given framework/library project. Compared to the exception-unaware API lifecycle modeling on 60 versions, JavaExp can identify 18% times more API changes.
  arXiv  Detail & Related papers  (2024-01-05T06:35:47Z)
• Private-Library-Oriented Code Generation with Large Language Models [52.73999698194344]
  This paper focuses on utilizing large language models (LLMs) for code generation in private libraries. We propose a novel framework that emulates the process of programmers writing private code. We create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval.
  arXiv  Detail & Related papers  (2023-07-28T07:43:13Z)
• Carving UI Tests to Generate API Tests and API Specification [8.743426215048451]
  API-level testing can play an important role, in-between unit-level testing and UI-level (or end-to-end) testing. Existing API testing tools require API specifications, which often may not be available or, when available, be inconsistent with the API implementation. We present an approach that leverages UI testing to enable API-level testing for web applications.
  arXiv  Detail & Related papers  (2023-05-24T03:53:34Z)
• Evaluating Embedding APIs for Information Retrieval [51.24236853841468]
  We evaluate the capabilities of existing semantic embedding APIs on domain generalization and multilingual retrieval. We find that re-ranking BM25 results using the APIs is a budget-friendly approach and is most effective in English. For non-English retrieval, re-ranking still improves the results, but a hybrid model with BM25 works best, albeit at a higher cost.
  arXiv  Detail & Related papers  (2023-05-10T16:40:52Z)
• HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions [35.48276161473216]
  We present HAPI, a longitudinal dataset of 1,761,417 instances of commercial ML API applications. Each instance consists of a query input for an API along with the API's output prediction/annotation and confidence scores.
  arXiv  Detail & Related papers  (2022-09-18T01:52:16Z)

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.