A Survey on Web Testing: On the Rise of AI and Applications in Industry
- URL: http://arxiv.org/abs/2503.05378v1
- Date: Fri, 07 Mar 2025 12:39:59 GMT
- Title: A Survey on Web Testing: On the Rise of AI and Applications in Industry
- Authors: Iva Kertusha, Gebremariem Assress, Onur Duman, Andrea Arcuri,
- Abstract summary: This paper presents a systematic literature survey focusing on web testing methodologies, tools, and trends from 2014 to 2024.<n>Our results show that web testing research has been highly active, with ICST as the leading venue.<n>Selenium is the most widely used tool, while industrial adoption and human studies remain comparatively limited.
- Score: 1.5149438988761574
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Web application testing is an essential practice to ensure the reliability, security, and performance of web systems in an increasingly digital world. This paper presents a systematic literature survey focusing on web testing methodologies, tools, and trends from 2014 to 2024. By analyzing \totalPapersIncluded research papers, the survey identifies key trends, demographics, contributions, tools, challenges, and innovations in this domain. In addition, the survey analyzes the experimental setups adopted by the studies, including the number of participants involved and the outcomes of the experiments. Our results show that web testing research has been highly active, with ICST as the leading venue. Most studies focus on novel techniques, emphasizing automation in black-box testing. Selenium is the most widely used tool, while industrial adoption and human studies remain comparatively limited. The findings provide a detailed overview of trends, advancements, and challenges in web testing research, the evolution of automated testing methods, the role of artificial intelligence in test case generation, and gaps in current research. Special attention was given to the level of collaboration and engagement with the industry. A positive trend in using industrial systems is observed, though many tools lack open-source availability.
Related papers
- Expectations vs Reality -- A Secondary Study on AI Adoption in Software Testing [0.7812210699650152]
In the software industry, artificial intelligence (AI) has been utilized more and more in software development activities.
In this paper, the objective was to identify what kind of empirical research with industry context has been conducted on AI in software testing.
arXiv Detail & Related papers (2025-04-07T11:03:54Z) - Requirements-Driven Automated Software Testing: A Systematic Review [13.67495800498868]
This study synthesizes the current state of REDAST research, highlights trends, and proposes future directions.<n>This systematic literature review ( SLR) explores the landscape of REDAST by analyzing requirements input, transformation techniques, test outcomes, evaluation methods, and existing limitations.
arXiv Detail & Related papers (2025-02-25T23:13:09Z) - Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - Testing Research Software: An In-Depth Survey of Practices, Methods, and Tools [3.831549883667425]
Testing research software is challenging due to the software's complexity and to the unique culture of the research software community.<n>This study focuses on test case design, challenges with expected outputs, use of quality metrics, execution methods, tools, and desired tool features.
arXiv Detail & Related papers (2025-01-29T16:27:13Z) - Machine Unlearning: A Survey [56.79152190680552]
A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning.
This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality.
No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios.
The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
arXiv Detail & Related papers (2023-06-06T10:18:36Z) - Towards Informed Design and Validation Assistance in Computer Games
Using Imitation Learning [65.12226891589592]
This paper proposes a new approach to automated game validation and testing.
Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming.
arXiv Detail & Related papers (2022-08-15T11:08:44Z) - Fairness Testing: A Comprehensive Survey and Analysis of Trends [30.637712832450525]
Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers.
This paper offers a comprehensive survey of existing studies in this field.
arXiv Detail & Related papers (2022-07-20T22:41:38Z) - Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
literature review [62.997667081978825]
This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today.
The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
arXiv Detail & Related papers (2022-04-29T08:06:05Z) - Scaling up Search Engine Audits: Practical Insights for Algorithm
Auditing [68.8204255655161]
We set up experiments for eight search engines with hundreds of virtual agents placed in different regions.
We demonstrate the successful performance of our research infrastructure across multiple data collections.
We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time.
arXiv Detail & Related papers (2021-06-10T15:49:58Z) - A Systematic Review of Online Exams Solutions in E-learning: Techniques,
Tools and Global Adoption [0.9489594423829898]
The reliable, fair, and seamless execution of online exams in E-learning is highly significant.
Online exams are conducted on E-learning platforms without the physical presence of students and instructors at the same place.
This poses several issues like integrity and security during online exams.
arXiv Detail & Related papers (2020-10-13T14:45:56Z) - Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement
Learning Framework [68.96770035057716]
A/B testing is a business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.
This paper introduces a reinforcement learning framework for carrying A/B testing in online experiments.
arXiv Detail & Related papers (2020-02-05T10:25:02Z)
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.