AFGNN: API Misuse Detection using Graph Neural Networks and Clustering

• URL: http://arxiv.org/abs/2604.07891v1
• Date: Thu, 09 Apr 2026 07:01:16 GMT
• Title: AFGNN: API Misuse Detection using Graph Neural Networks and Clustering
• Abstract summary: AFGNN is a novel Graph Neural Network (GNN)-based framework for efficiently detecting API misuses in Java code.<n>AFGNN uses a novel API Flow Graph (AFG) representation that captures the API execution sequence, data, and control flow information present in the code to model the API usage patterns.<n> Experiments on popular API usage datasets show that AFGNN significantly outperforms state-of-the-art small language models and API misuse detectors.
• Score: 3.727482310060583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Application Programming Interfaces (APIs) are crucial to software development, enabling integration of existing systems with new applications by reusing tried and tested code, saving development time and increasing software safety. In particular, the Java standard library APIs, along with numerous third-party APIs, are extensively utilized in the development of enterprise application software. However, their misuse remains a significant source of bugs and vulnerabilities. Furthermore, due to the limited examples in the official API documentation, developers often rely on online portals and generative AI models to learn unfamiliar APIs, but using such examples may introduce unintentional errors in the software. In this paper, we present AFGNN, a novel Graph Neural Network (GNN)-based framework for efficiently detecting API misuses in Java code. AFGNN uses a novel API Flow Graph (AFG) representation that captures the API execution sequence, data, and control flow information present in the code to model the API usage patterns. AFGNN uses self-supervised pre-training with AFG representation to effectively compute the embeddings for unknown API usage examples and cluster them to identify different usage patterns. Experiments on popular API usage datasets show that AFGNN significantly outperforms state-of-the-art small language models and API misuse detectors.

Related papers

• Framework-Aware Code Generation with API Knowledge Graph-Constructed Data: A Study on HarmonyOS [52.483888557864326]
  APIKG4SYN is a framework designed to exploit API knowledge graphs for the construction of API-oriented question-code pairs.<n>We build the first benchmark for HarmonyOS code generation using APIKG4SYN.
  arXiv  Detail & Related papers  (2025-11-29T08:13:54Z)
• Benchmarking Web API Integration Code Generation [3.5669873833301047]
  We present WAPIIBench, a dataset and evaluation pipeline designed to assess the ability of LLMs to generate web API invocation code.<n>Our experiments with several open-source LLMs reveal that generating API invocations poses a significant challenge.<n>None of the evaluated open-source models was able to solve more than 40% of the tasks.
  arXiv  Detail & Related papers  (2025-09-24T14:36:44Z)
• Identifying and Mitigating API Misuse in Large Language Models [26.4403427473915]
  API misuse in code generated by large language models (LLMs) represents a serious emerging challenge in software development.<n>This paper presents the first comprehensive study of API misuse patterns in LLM-generated code, analyzing both method selection and parameter usage across Python and Java.<n>We propose Dr.Fix, a novel LLM-based automatic program repair approach for API misuse based on the aforementioned taxonomy.
  arXiv  Detail & Related papers  (2025-03-28T18:43:12Z)
• Your Fix Is My Exploit: Enabling Comprehensive DL Library API Fuzzing with Large Language Models [49.214291813478695]
  Deep learning (DL) libraries, widely used in AI applications, often contain vulnerabilities like overflows and use buffer-free errors.<n>Traditional fuzzing struggles with the complexity and API diversity of DL libraries.<n>We propose DFUZZ, an LLM-driven fuzzing approach for DL libraries.
  arXiv  Detail & Related papers  (2025-01-08T07:07:22Z)
• 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> Experimental results demonstrate that ExploraCoder significantly improves performance for models lacking prior API knowledge.
  arXiv  Detail & Related papers  (2024-12-06T19:00:15Z)
• 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)
• Enabling Communication via APIs for Mainframe Applications [4.872049174955585]
  We propose a novel framework for creating APIs for legacy mainframe applications. Our approach involves identifying APIs by compiling artifacts such as transactions, screens, control flow blocks, inter-microservice calls, business rules, and data accesses. We use static analyses like liveness and reaching definitions to traverse the code and automatically compute API signatures.
  arXiv  Detail & Related papers  (2024-08-08T05:35:36Z)
• 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)
• A Classification-by-Retrieval Framework for Few-Shot Anomaly Detection to Detect API Injection Attacks [9.693391036125908]
  We propose a novel unsupervised few-shot anomaly detection framework composed of two main parts. First, we train a dedicated generic language model for API based on FastText embedding. Next, we use Approximate Nearest Neighbor search in a classification-by-retrieval approach.
  arXiv  Detail & Related papers  (2024-05-18T10:15:31Z)
• 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)
• Using sequential drift detection to test the API economy [4.056434158960926]
  API economy refers to the widespread integration of API (advanced programming interface) It is desirable to monitor the usage patterns and identify when the system is used in a way that was never used before. In this work we analyze both histograms and call graph of API usage to determine if the usage patterns of the system has shifted.
  arXiv  Detail & Related papers  (2021-11-09T13:24:19Z)

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.