arXiv subjects

Quentin Stiévenart

Publications and source records attributed to Quentin Stiévenart.

7 recordsLinked to original sources

arXiv2026

SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs

Machine Learning (ML) pipelines encode quality-relevant decisions across data preparation, training, evaluation, and configuration code. Some recurring source-level quality problems in these pipelines, known as ML code smells, may not cause immediate failures but can harm reproducibility, robustness, efficiency, or maintainability. Detecting ML code smell occurrences is challenging because the decisive evidence is often non-local, spanning helper functions, wrappers, imports, control-flow, and data-flow relations. We present SpecDetect4ML, a static analyser that operationalises 22 ML code smells using CPG views with project-level resolution. We evaluate it on 890 Python ML-based systems comprising more than 20M LOC and a system-level recall benchmark over the complete ML-relevant source subset of 10 selected systems. Under identical ML code smell specifications, CPG-based reasoning raises recall from 68.62\% to 88.14% compared with AST-only analysis, while keeping CPG precision comparable at 90.32%. These results show that project-level static reasoning expands the detectable portion of non-local ML code smell occurrences, while configuration-dependent and runtime-only occurrences remain outside our source-only static claims.

cs.SE↗

arXiv2026

LLM Code Smells: A Taxonomy and Detection Approach

Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and ability to simulate human reasoning to some extent. However, poor integration of LLM inference in source code can undermine software system quality. Therefore, inadequate LLM integration coding practices must be documented to help developers mitigate such issues. Following our earlier work on LLM code smells, this paper consolidates and refines the concept by presenting a self-contained taxonomy and a catalog of nine LLM code smells. We also create SpecDetect4LLM, a static source code analysis tool for their detection, and conduct extensive empirical evaluations of its detection effectiveness (precision and recall) as well as the prevalence of LLM code smells across 692 open-source software projects (171,194 source files). Our results show that LLM code smells affect 73.5% of the analyzed systems, with a detection precision of 91.3% and a recall of 71.8%.

cs.SE↗

arXiv2025

Specification and Detection of LLM Code Smells

Large Language Models (LLMs) have gained massive popularity in recent years and are increasingly integrated into software systems for diverse purposes. However, poorly integrating them in source code may undermine software system quality. Yet, to our knowledge, there is no formal catalog of code smells specific to coding practices for LLM inference. In this paper, we introduce the concept of LLM code smells and formalize five recurrent problematic coding practices related to LLM inference in software systems, based on relevant literature. We extend the detection tool SpecDetect4AI to cover the newly defined LLM code smells and use it to validate their prevalence in a dataset of 200 open-source LLM systems. Our results show that LLM code smells affect 60.50% of the analyzed systems, with a detection precision of 86.06%.

cs.SE↗

arXiv2023

Wasmizer: Curating WebAssembly-driven Projects on GitHub

WebAssembly has attracted great attention as a portable compilation target for programming languages. To facilitate in-depth studies about this technology, we have deployed Wasmizer, a tool that regularly mines GitHub projects and makes an up-to-date dataset of WebAssembly sources and their binaries publicly available. Presently, we have collected 2 540 C and C++ projects that are highly-related to WebAssembly, and built a dataset of 8 915 binaries that are linked to their source projects. To demonstrate an application of this dataset, we have investigated the presence of eight WebAssembly compilation smells in the wild.

cs.SE↗

arXiv2022

Developers Struggle with Authentication in Blazor WebAssembly

WebAssembly is a growing technology to build cross-platform applications. We aim to understand the security issues that developers encounter when adopting WebAssembly. We mined WebAssembly questions on Stack Overflow and identified 359 security-related posts. We classified these posts into 8 themes, reflecting developer intentions, and 19 topics, representing developer issues in this domain. We found that the most prevalent themes are related to bug fix support, requests for how to implement particular features, clarification questions, and setup or configuration issues. We noted that the topmost issues attribute to authentication in Blazor WebAssembly. We discuss six of them and provide our suggestions to clear these issues in practice.

cs.CR↗

arXiv2021

Security Risks of Porting C Programs to WebAssembly

WebAssembly is a compilation target for cross-platform applications that is increasingly being used. In this paper, we investigate whether one can transparently cross-compile C programs to WebAssembly, and if not, what impact porting can have on their security. We compile 17,802 programs that exhibit common vulnerabilities to 64-bit x86 and to WebAssembly binaries, and we observe that the execution of 4,911 binaries produces different results across these platforms. Through manual inspection, we identify three classes of root causes for such differences: the use of a different standard library implementation, the lack of security measures in WebAssembly, and the different semantics of the execution environments. We describe our observations and discuss the ones that are critical from a security point of view and need most attention from developers. We conclude that compiling an existing C program to WebAssembly for cross-platform distribution may require source code adaptations; otherwise, the security of the WebAssembly application may be at risk.

cs.CR↗

arXiv2021

The Security Risk of Lacking Compiler Protection in WebAssembly

WebAssembly is increasingly used as the compilation target for cross-platform applications. In this paper, we investigate whether one can rely on the security measures enforced by existing C compilers when compiling C programs to WebAssembly. We compiled 4,469 C programs with known buffer overflow vulnerabilities to x86 code and to WebAssembly, and observed the outcome of the execution of the generated code to differ for 1,088 programs. Through manual inspection, we identified that the root cause for these is the lack of security measures such as stack canaries in the generated WebAssembly: while x86 code crashes upon a stack-based buffer overflow, the corresponding WebAssembly continues to be executed. We conclude that compiling an existing C program to WebAssembly without additional precautions may hamper its security, and we encourage more research in this direction.

cs.CR↗