Can Language Models Pass Software Testing Certification Exams? a case study

• URL: http://arxiv.org/abs/2603.23142v1
• Date: Tue, 24 Mar 2026 12:38:37 GMT
• Title: Can Language Models Pass Software Testing Certification Exams? a case study
• Abstract summary: This study evaluates 60 multimodal language models from both commercial vendors and the open-source community.<n>The evaluation is performed using 30 sample exams of different types (core foundation, core advanced, specialist, and expert) from the International Software Testing Qualifications Board.<n>Two models passed all the certifications by scoring at least 65% in all of the 30 certification exams.
• Score: 2.5317298080728494
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) play a pivotal role in both academic research and broader societal applications. LLMs are increasingly used in software testing activities such as test case generation, selection, and repair. However, several important questions remain: (1) do LLMs possess enough information about software testing principles to perform software testing tasks effectively? (2) do LLMs possess sufficient conceptual understanding of software testing to answer software testing questions under metamorphic transformations? and (3) do certain properties of software testing questions influence the performance of LLMs? To answer these questions, this study evaluates 60 multimodal language models from both commercial vendors and the open-source community. The evaluation is performed using 30 sample exams of different types (core foundation, core advanced, specialist, and expert) from the International Software Testing Qualifications Board (ISTQB), which are used to assess the competence of human testers. In total, each model is evaluated on 1,171 questions. Furthermore, to ensure sufficient conceptual understanding, the models are also tested on exam questions transformed using context-preserving metamorphic techniques. Two models passed all the certifications by scoring at least 65% in all of the 30 certification exams, with commercial models generally outperforming open-source ones. We analyze the reasons behind incorrect answers and provide recommendations for improving the design of software testing certification exams.

Related papers

• SimpleDevQA: Benchmarking Large Language Models on Development Knowledge QA [58.75982433502236]
  The Dev Knowledge QA task accounts for 39.6% of interactions.<n>Only 27.5% of real Dev Knowledge QA dialogues focus on code understanding.<n>Only 17.1% of real-world Dev Knowledge QA dialogues can be used for constructing a benchmark.
  arXiv  Detail & Related papers  (2025-12-09T17:58:36Z)
• Do AI models help produce verified bug fixes? [62.985237003585674]
  Large Language Models are used to produce corrections to software bugs.<n>This paper investigates how programmers use Large Language Models to complement their own skills.<n>The results are a first step towards a proper role for AI and LLMs in providing guaranteed-correct fixes to program bugs.
  arXiv  Detail & Related papers  (2025-07-21T17:30:16Z)
• A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing [8.22619177301814]
  Large Language Models (LLMs) have shown potential in various unit testing tasks.<n>We present a large-scale empirical study on fine-tuning LLMs for unit testing.
  arXiv  Detail & Related papers  (2024-12-21T13:28:11Z)
• ASTER: Natural and Multi-language Unit Test Generation with LLMs [6.259245181881262]
  We describe a generic pipeline that incorporates static analysis to guide LLMs in generating compilable and high-coverage test cases.<n>We conduct an empirical study to assess the quality of the generated tests in terms of code coverage and test naturalness.
  arXiv  Detail & Related papers  (2024-09-04T21:46:18Z)
• A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites [1.4563527353943984]
  Large Language Models (LLMs) have been applied to various aspects of software development. We present AgoneTest: an automated system for generating test suites for Java projects.
  arXiv  Detail & Related papers  (2024-08-14T23:02:16Z)
• Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation [11.517293765116307]
  Unit testing is essential for software reliability, yet manual test creation is time-consuming and often neglected.<n>This study presents the first large-scale empirical evaluation of LLM-generated unit tests at the class level.
  arXiv  Detail & Related papers  (2024-06-28T20:38:41Z)
• TESTEVAL: Benchmarking Large Language Models for Test Case Generation [15.343859279282848]
  We propose TESTEVAL, a novel benchmark for test case generation with large language models (LLMs)<n>We collect 210 Python programs from an online programming platform, LeetCode, and design three different tasks: overall coverage, targeted line/branch coverage, and targeted path coverage.<n>We find that generating test cases to cover specific program lines/branches/paths is still challenging for current LLMs.
  arXiv  Detail & Related papers  (2024-06-06T22:07:50Z)
• LOVA3: Learning to Visual Question Answering, Asking and Assessment [61.51687164769517]
  Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge.<n>Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills.<n>We introduce LOVA3, an innovative framework named "Learning tO Visual question Answering, Asking and Assessment"
  arXiv  Detail & Related papers  (2024-05-23T18:21:59Z)
• An empirical study of testing machine learning in the wild [35.13282520395855]
  Machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive nature, ensuring the quality of these systems remains a significant challenge for the research community. Recent research in ML/DL quality assurance has adapted concepts from traditional software testing, such as mutation testing, to improve reliability.
  arXiv  Detail & Related papers  (2023-12-19T21:18:14Z)
• Are We Testing or Being Tested? Exploring the Practical Applications of Large Language Models in Software Testing [0.0]
  A Large Language Model (LLM) represents a cutting-edge artificial intelligence model that generates coherent content. LLM can play a pivotal role in software development, including software testing. This study explores the practical application of LLMs in software testing within an industrial setting.
  arXiv  Detail & Related papers  (2023-12-08T06:30:37Z)
• Position: AI Evaluation Should Learn from How We Test Humans [65.36614996495983]
  We argue that psychometrics, a theory originating in the 20th century for human assessment, could be a powerful solution to the challenges in today's AI evaluations.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Benchmarking Foundation Models with Language-Model-as-an-Examiner [47.345760054595246]
  We propose a novel benchmarking framework, Language-Model-as-an-Examiner. The LM serves as a knowledgeable examiner that formulates questions based on its knowledge and evaluates responses in a reference-free manner.
  arXiv  Detail & Related papers  (2023-06-07T06:29:58Z)
• ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models [78.08792285698853]
  We present a large-scale empirical study on general language ability evaluation of pretrained language models (ElitePLM) Our empirical results demonstrate that: (1) PLMs with varying training objectives and strategies are good at different ability tests; (2) fine-tuning PLMs in downstream tasks is usually sensitive to the data size and distribution; and (3) PLMs have excellent transferability between similar tasks.
  arXiv  Detail & Related papers  (2022-05-03T14:18:10Z)
• Beyond Accuracy: Behavioral Testing of NLP models with CheckList [66.42971817954806]
  CheckList is a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation. In a user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.
  arXiv  Detail & Related papers  (2020-05-08T15:48:31Z)

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.