MBTModelGenerator: A software tool for reverse engineering of Model-based Testing (MBT) models from clickstream data of web applications
- URL: http://arxiv.org/abs/2506.08179v1
- Date: Mon, 09 Jun 2025 19:44:10 GMT
- Title: MBTModelGenerator: A software tool for reverse engineering of Model-based Testing (MBT) models from clickstream data of web applications
- Authors: Sasidhar Matta, Vahid Garousi,
- Abstract summary: The tool captures UI events, transforms them into state-transition models, and exports the result in a format compatible with the GraphWalker MBT tool.<n>This report documents the system requirements, design decisions, implementation details, testing process, and empirical evaluation of the tool, which is publicly available as open-source.
- Score: 1.516251872371896
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automated testing has become a standard practice in software engineering, yet the creation of test models and suites remains labor-intensive. To reduce this effort, we developed an open-source tool that automatically generates Model-Based Testing (MBT) models from clickstream data collected during user interaction with web applications. The tool captures UI events, transforms them into state-transition models, and exports the result in a format compatible with the GraphWalker MBT tool. This enables immediate test execution without the need for manual model creation. The approach lowers the barrier to MBT adoption by leveraging actual usage behavior and reducing the reliance on upfront modeling. This technical report documents the system requirements, design decisions, implementation details, testing process, and empirical evaluation of the tool, which is publicly available as open-source.
Related papers
- Can Test-Time Scaling Improve World Foundation Model? [67.82670175383761]
We introduce SWIFT, a test-time scaling framework tailored for world foundation models (WFMs)<n> Empirical results on the COSMOS model demonstrate that test-time scaling exists even in a compute-optimal way.<n>Our findings reveal that test-time scaling laws hold for WFMs and that SWIFT provides a scalable and effective pathway for improving WFM inference without retraining or increasing model size.
arXiv Detail & Related papers (2025-03-31T17:07:37Z) - Formal Analysis of the Contract Automata Runtime Environment with Uppaal: Modelling, Verification and Testing [0.3807314298073301]
A distributed runtime application called tt CARE has been introduced to realise service applications specified using a dialect of finite-state automata.<n>We detail the formal modelling, verification and testing of tt CARE.
arXiv Detail & Related papers (2025-01-22T15:03:25Z) - LlamaRestTest: Effective REST API Testing with Small Language Models [50.058600784556816]
We present LlamaRestTest, a novel approach that employs two custom Large Language Models (LLMs) to generate realistic test inputs.<n>We evaluate it against several state-of-the-art REST API testing tools, including RESTGPT, a GPT-powered specification-enhancement tool.<n>Our study shows that small language models can perform as well as, or better than, large language models in REST API testing.
arXiv Detail & Related papers (2025-01-15T05:51:20Z) - AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning [8.079755354261328]
We propose a multistage fitting procedure that is compatible with out-of-the-box Automated Machine Learning (AutoML) tools.
It is based on a Monte Carlo EM (MCEM) outer loop with a two stage inner loop, which trains a non-parametric AutoML grade model using item features followed by an item specific parametric model.
We show that the resulting model is typically more well, gets better predictive performance, and more accurate scores than existing methods.
arXiv Detail & Related papers (2024-09-13T13:36:51Z) - VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models [93.94887464110101]
We present an open-source toolkit for evaluating large multi-modality models based on PyTorch.<n>VLMEvalKit implements over 70 different large multi-modality models, including both proprietary APIs and open-source models.<n>We host OpenVLM Leaderboard to track the progress of multi-modality learning research.
arXiv Detail & Related papers (2024-07-16T13:06:15Z) - Provengo: A Tool Suite for Scenario Driven Model-Based Testing [2.4387555567462647]
Provengo is a suite of tools designed to facilitate the implementation of Scenario-Driven Model-Based Testing (SDMBT)
With Provengo, testers gain the ability to effortlessly create natural user stories and seamlessly integrate them into a model capable of generating effective tests.
arXiv Detail & Related papers (2023-08-30T10:34:12Z) - Formal Verification Of A Shopping Basket Application Model Using PRISM [0.0]
We present the results of a simulation using Prism Model Checker for a Shopping Basket Application Model.
The objective is to simulate the behavior of shoppers as they go through a number of defined states of the shopping process.
arXiv Detail & Related papers (2023-07-16T00:14:40Z) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z) - Automated Machine Learning Techniques for Data Streams [91.3755431537592]
This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time.
The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
arXiv Detail & Related papers (2021-06-14T11:42:46Z) - SciWING -- A Software Toolkit for Scientific Document Processing [21.394568145639894]
SciWING provides access to pre-trained models for scientific document processing tasks.
It includes ready-to-use web and terminal-based applications and demonstrations.
arXiv Detail & Related papers (2020-04-08T04:43:37Z) - Model Reuse with Reduced Kernel Mean Embedding Specification [70.044322798187]
We present a two-phase framework for finding helpful models for a current application.
In the upload phase, when a model is uploading into the pool, we construct a reduced kernel mean embedding (RKME) as a specification for the model.
Then in the deployment phase, the relatedness of the current task and pre-trained models will be measured based on the value of the RKME specification.
arXiv Detail & Related papers (2020-01-20T15:15:07Z)
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.