arXiv subjects

Tolgahan Bardakci

Publications and source records attributed to Tolgahan Bardakci.

5 recordsLinked to original sources

arXiv2026

RESTCov: A Tool for Structural Coverage Analysis of REST APIs

REST APIs are widely used in modern software systems, but developers and testers often lack visibility into which parts of an API specification are exercised by a test suite. Traditional coverage analysis usually relies on source-code instrumentation, which is impractical for REST APIs that are distributed, externally maintained, and hence accessible only through black-box execution. This paper presents RESTCov, a lightweight tool that computes structural REST API coverage from an OpenAPI specification and observed HTTP request/response logs, reporting coverage across paths, operations, parameters, media types, status codes, and status classes. RESTCov produces both machine-readable results and a human-readable HTML report, helping users inspect coverage gaps, diagnose specification-log mismatches, and evaluate REST API test suites without requiring access to the implementation. Screencast: https://youtu.be/mNz2P43OyUc Repository: https://github.com/2tolgahan2/RESTCov

cs.SE↗

arXiv2026

Prompt-Based REST API Test Amplification in Industry: An Experience Report

Large Language Models (LLMs) are increasingly used to support software testing tasks, yet there is little evidence of their effectiveness for REST API testing in industrial settings. To address this gap, we replicate our earlier work on LLM-based REST API test amplification within an industrial context at one of the largest logistics companies in Belgium. We apply LLM-based test amplification to six representative endpoints of a production microservice embedded in a large-scale, security-sensitive system, where there is in-depth complexity in authentication, stateful behavior, and organizational constraints. Our experience shows that LLM-based test amplification remains practically useful in industry by increasing coverage and revealing various observations and anomalies.

cs.SE↗

arXiv2025

Agentic LLMs for REST API Test Amplification: A Comparative Study Across Cloud Applications

Representational State Transfer (REST) APIs are a cornerstone of modern cloud native systems. Ensuring their reliability demands automated test suites that exercise diverse and boundary level behaviors. Nevertheless, designing such test cases remains a challenging and resource intensive endeavor. This study extends prior work on Large Language Model (LLM) based test amplification by evaluating single agent and multi agent configurations across four additional cloud applications. The amplified test suites maintain semantic validity with minimal human intervention. The results demonstrate that agentic LLM systems can effectively generalize across heterogeneous API architectures, increasing endpoint and parameter coverage while revealing defects. Moreover, a detailed analysis of computational cost, runtime, and energy consumption highlights trade-offs between accuracy, scalability, and efficiency. These findings underscore the potential of LLM driven test amplification to advance the automation and sustainability of REST API testing in complex cloud environments.

cs.SE↗

arXiv2025

Test Amplification for REST APIs via Single and Multi-Agent LLM Systems

REST APIs (Representational State Transfer Application Programming Interfaces) play a vital role in modern cloud-native applications. As these APIs grow in complexity and scale, ensuring their correctness and robustness becomes increasingly important. Automated testing is essential for identifying hidden bugs, particularly those that appear in edge cases or under unexpected inputs. However, creating comprehensive and effective test suites for REST APIs is challenging and often demands significant effort. In this paper, we investigate the use of large language model (LLM) systems, both single-agent and multi-agent setups, for amplifying existing REST API test suites. These systems generate additional test cases that aim to push the boundaries of the API, uncovering behaviors that might otherwise go untested. We present a comparative evaluation of the two approaches across several dimensions, including test coverage, bug detection effectiveness, and practical considerations such as computational cost and energy usage. Our evaluation demonstrates increased API coverage, identification of numerous bugs in the API under test, and insights into the computational cost and energy consumption of both approaches.

cs.SE↗

arXiv2025

Test Amplification for REST APIs Using "Out-of-the-box" Large Language Models

REST APIs (Representational State Transfer Application Programming Interfaces) are an indispensable building block in today's cloud-native applications, so testing them is critically important. However, writing automated tests for such REST APIs is challenging because one needs strong and readable tests that exercise the boundary values of the protocol embedded in the REST API. In this paper, we report our experience with using "out of the box" large language models (ChatGPT and GitHub's Copilot) to amplify REST API test suites. We compare the resulting tests based on coverage and understandability, and we derive a series of guidelines and lessons learned concerning the prompts that result in the strongest test suite.

cs.SE↗