arXiv subjects

Pierre-Yves Schobbens

Publications and source records attributed to Pierre-Yves Schobbens.

5 recordsLinked to original sources

arXiv2026

Detecting HTTP Status Code Misuses in REST APIs via Static and Dynamic Analysis

REST APIs are widely used on the web for client-server communications. As REST is based on HTTP, server responses contain status codes to indicate the outcome of requests (e.g., 200 OK for a success and 404 Not Found for an unavailable resource). While HTTP status codes are standardized, their semantics are not enforced in REST, leading to many misuses in practice (e.g., using 500 Internal Server Error to describe a client error). Such misuses may have nefarious consequences, such as reducing interoperability, misleading API clients, or causing false positives in testing tools. In this paper, we present a combined static and dynamic analysis approach for detecting HTTP status code misuses in REST APIs. We first study 2,625 real-world REST API specifications to identify relevant status codes and derive a set of 30 usage rules based on HTTP standards and REST API principles. We then implement tools to identify such rule violations in OpenAPI specifications (static analysis) and in API behavior (dynamic analysis). Our evaluation finds that status code misuses are frequent and systematic in REST APIs, with both static and dynamic approaches detecting various misuses. We highlight that both approaches may be used in a complementary manner, and also provide insight for REST API testers and users alike.

cs.SE↗

arXiv2025

You Can REST Now: Automated REST API Documentation and Testing via LLM-Assisted Request Mutations

REST APIs are prevalent among web service implementations, easing interoperability through the HTTP protocol. API testers and users exploit the widely adopted OpenAPI Specification (OAS), a machine-readable standard to document REST APIs. However, documenting APIs is a time-consuming and error-prone task, and existing documentation is not always complete, publicly accessible, or up-to-date. This situation limits the efficiency of testing tools and hinders human comprehension. Large Language Models (LLMs) offer the potential to automatically infer API documentation, using their colossal training data. In this paper, we present RESTSpecIT, the first automated approach that infers documentation and performs black-box testing of REST APIs by leveraging LLMs. Our approach requires minimal user input compared to state-of-the-art tools; Given an API name and an LLM access key, RESTSpecIT generates API request seeds and mutates them with data returned by the LLM. The tool then analyzes API responses for documentation inference and testing purposes. RESTSpecIT utilizes an in-context prompt masking strategy, requiring no prior model fine-tuning. We evaluate the quality of our tool with three state-of-the-art LLMs: DeepSeek V3, GPT-4.1, and GPT-3.5. Our evaluation demonstrates that RESTSpecIT can (1) infer documentation with 88.62% of routes and 89.25% of query parameters found on average, (2) discover undocumented API data, (3) operate efficiently (in terms of model costs, requests sent, runtime), and (4) assist REST API testing by uncovering server errors and generating valid OpenAPI Specification inputs for testing tools.

cs.SE↗

arXiv2014

State Machine Flattening: Mapping Study and Assessment

State machine formalisms equipped with hierarchy and parallelism allow to compactly model complex system behaviours. Such models can then be transformed into executable code or inputs for model-based testing and verification techniques. Generated artifacts are mostly flat descriptions of system behaviour. \emph{Flattening} is thus an essential step of these transformations. To assess the importance of flattening, we have defined and applied a systematic mapping process and 30 publications were finally selected. However, it appeared that flattening is rarely the sole focus of the publications and that care devoted to the description and validation of flattening techniques varies greatly. Preliminary assessment of associated tool support indicated limited tool availability and scalability on challenging models. We see this initial investigation as a first step towards generic flattening techniques and scalable tool support, cornerstones of reliable model-based behavioural development.

cs.SE↗

arXiv2014

Towards Statistical Prioritization for Software Product Lines Testing

Software Product Lines (SPL) are inherently difficult to test due to the combinatorial explosion of the number of products to consider. To reduce the number of products to test, sampling techniques such as combinatorial interaction testing have been proposed. They usually start from a feature model and apply a coverage criterion (e.g. pairwise feature interaction or dissimilarity) to generate tractable, fault-finding, lists of configurations to be tested. Prioritization can also be used to sort/generate such lists, optimizing coverage criteria or weights assigned to features. However, current sampling/prioritization techniques barely take product behavior into account. We explore how ideas of statistical testing, based on a usage model (a Markov chain), can be used to extract configurations of interest according to the likelihood of their executions. These executions are gathered in featured transition systems, compact representation of SPL behavior. We discuss possible scenarios and give a prioritization procedure illustrated on an example.

cs.SE↗

arXiv2013

Verification for Reliable Product Lines

Many product lines are critical, and therefore reliability is a vital part of their requirements. Reliability is a probabilistic property. We therefore propose a model for feature-aware discrete-time Markov chains as a basis for verifying probabilistic properties of product lines, including reliability. We compare three verification techniques: The enumerative technique uses PRISM, a state-of-the-art symbolic probabilistic model checker, on each product. The parametric technique exploits our recent advances in parametric model checking. Finally, we propose a new bounded technique that performs a single bounded verification for the whole product line, and thus takes advantage of the common behaviours of the product line. Experimental results confirm the advantages of the last two techniques.

cs.SE↗