This paper presents an empirical evaluation of Large Language Models (LLMs) for automated model-based test generation, compared with a state-of-the-art model-based testing tool (GraphWalker) and its built-in algorithms (random and quick random for edge and vertex coverage settings).
Abstract
Large language models have shown strong potential for software engineering tasks, particularly software testing. Model-based testing (MBT) is a software testing technique. To address the broad scalability challenge for industrial adoption of MBTs, our paper presents an empirical evaluation of Large Language Models (LLMs) for automated model-based test generation, compared with a state-of-the-art model-based testing tool (GraphWalker) and its built-in algorithms (random and quick random for edge and vertex coverage settings). Our evaluation indicates strong potential to optimize and shorten test paths and step sizes using the recent five state-of-the-art LLMs (GPT-5.1, GPT-5.2, Claude Opus 4.5, Claude Sonnet 4.5, and Gemini 2.5 Pro) against four GraphWalker models (two web applications (Parabank and Testinium) and two hardware applications (TLC and RISC-V) ) of escalating complexity.
The results show that appropriate model slicing significantly improves completion correctness while simultaneously reducing token usage across several structural and semantic evaluation metrics, and establish model slicing as a key factor in LLM-based model completion and provide guidance for effective context selection for other modeling tasks.
This paper presents a formal mathematical model for categorizing the outcome of generated-tests into four classes, a couple of basic metrics: Bug-Revealing Rate (BRR) and Bug-Validating Rate (BVR); and two basic statistical tests to ensure that the results are rigorous.
Zeyad Farooq Lutfi· Al-Noor Journal of Engineeri...· 0 citations
Public tests are widely used to guide large language model code generation, but whether models treat them as executable specifications or merely as extra prompt context remains unclear. We study test-driven code generation on HumanEval+, MBPP+, and recent LiveCodeBench tasks using Qwen2.5-Coder-7B and Qwen3.6-27B. We compare natural-language-only prompts with relevant visible tests, shuffled outputs, irrelevant tests, assertion-only tests, and stronger-model-generated synthetic tests. Evaluation combines hidden or private test pass rates with task-level behavior flips, linear probes, and layer-wise hidden-state shifts. Visible tests substantially improve Qwen2.5 performance on MBPP+ but have little or unstable effect on HumanEval+ and LiveCodeBench. For Qwen3.6 on LiveCodeBench, the natural-language-only pass rate rises from 13.1% to 39.4%, yet relevant original tests add only 2.9 percentage points (p = .458), and synthetic high5 tests add 1.7 points over their matched baseline (p = .701). More tests produce larger representation shifts, but accuracy gains quickly saturate or disappear; assertion-only and irrelevant-test prompts can strongly alter hidden states without improving correctness. After controlling for prompt length, task-level shifts are associated with both beneficial and harmful flips, with a substantially stronger association for harms. These results show that tests influence code models through both semantic guidance and prompt-context perturbation, and that representational change alone does not demonstrate effective test utilization.
Yunhao Liang, Chengguang Gan, Ruixuan Ying et al.· 0 citations
Recent advances in large language models (LLMs) have driven growing interest in using LLMs to automate test generation. Prior work commonly evaluates generated test suites using proxy metrics such as code coverage and mutation score. However, studies by Inozemtseva et al. and Papadakis et al. show that, for human-written tests, correlations among coverage, mutation, and real-bug detection can largely vanish once test suite size is controlled, raising concerns about the validity of evaluations based on proxy metrics. It also remains unclear whether these conclusions carry over to LLM-generated tests, given that prevailing LLM-based test-generation workflows differ substantially from traditional approaches. In this paper, we conduct a large-scale replication study of these two prior works using a wide range of test suites generated by a diverse set of LLMs, and re-examine the relationships among coverage, mutation, and real-bug detection effectiveness. Our findings diverge substantially from prior results. We show that the usefulness of coverage and mutation is highly context-dependent: in regression-style settings where the code provided to the LLM can be reasonably assumed bug-free, these metrics can provide meaningful signals when comparing across models; in another common scenario where the code-under-test may already be buggy and the goal is to expose the bug within the code-under-test, they no longer serve as reliable indicators. We also find little evidence that test suite size is a dominant confounder for correlations among coverage, mutation, and real-bug detection for LLM-generated tests. Based on these findings, we discuss how to interpret results from prior studies and provide actionable guidance for evaluating LLM-based test generation.
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
