Gamified GUI testing with Selenium in the IntelliJ IDE: A Prototype Plugin
- URL: http://arxiv.org/abs/2403.09842v1
- Date: Thu, 14 Mar 2024 20:11:11 GMT
- Title: Gamified GUI testing with Selenium in the IntelliJ IDE: A Prototype Plugin
- Authors: Giacomo Garaccione, Tommaso Fulcini, Paolo Stefanut Bodnarescul, Riccardo Coppola, Luca Ardito,
- Abstract summary: This paper presents GIPGUT: a prototype of a gamification plugin for IntelliJ IDEA.
The plugin enhances testers' engagement with typically monotonous and tedious tasks through achievements, rewards, and profile customization.
The results indicate high usability and positive reception of the gamification elements.
- Score: 0.559239450391449
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Software testing is a crucial phase in software development, enabling the detection of issues and defects that may arise during the development process. Addressing these issues enhances software applications' quality, reliability, user experience, and performance. Graphical User Interface (GUI) testing, one such technique, involves mimicking a regular user's interactions with an application to identify defects. However, GUI testing is often underutilized due to its perceived repetitiveness, error-proneness, and lack of immediate feedback on test quality. In recent years, gamification-incorporating game elements in non-game contexts to boost interest, motivation, and engagement-has gained traction in various fields, including software engineering and education. This paper presents GIPGUT: a prototype of a gamification plugin for IntelliJ IDEA, an Integrated Development Environment (IDE) that supports scripted GUI testing. The plugin enhances testers' engagement with typically monotonous and tedious tasks through achievements, rewards, and profile customization. A preliminary prototype evaluation was conducted with a small group of users to assess its usability and the impact of gamification on the GUI testing process. The results indicate high usability and positive reception of the gamification elements. However, due to the limited sample size of participants, further research is necessary to understand the plugin's effectiveness fully.
Related papers
- GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous Exploration [56.58744345634623]
We propose GUI-Bee, an MLLM-based autonomous agent, to collect high-quality, environment-specific data through exploration.
We also introduce NovelScreenSpot, a benchmark for testing how well the data can help align GUI action grounding models to novel environments.
arXiv Detail & Related papers (2025-01-23T18:16:21Z) - GUI Testing Arena: A Unified Benchmark for Advancing Autonomous GUI Testing Agent [24.97846085313314]
We propose a formalized and comprehensive environment to evaluate the entire process of automated GUI Testing.
We divide the testing process into three key subtasks: test intention generation, test task execution, and GUI defect detection.
It evaluates the performance of different models using three data types: real mobile applications, mobile applications with artificially injected defects, and synthetic data.
arXiv Detail & Related papers (2024-12-24T13:41:47Z) - Zero-Shot Prompting Approaches for LLM-based Graphical User Interface Generation [53.1000575179389]
We propose a Retrieval-Augmented GUI Generation (RAGG) approach, integrated with an LLM-based GUI retrieval re-ranking and filtering mechanism.
In addition, we adapt Prompt Decomposition (PDGG) and Self-Critique (SCGG) for GUI generation.
Our evaluation, which encompasses over 3,000 GUI annotations from over 100 crowd-workers with UI/UX experience, shows that SCGG, in contrast to PDGG and RAGG, can lead to more effective GUI generation.
arXiv Detail & Related papers (2024-12-15T22:17:30Z) - Which Combination of Test Metrics Can Predict Success of a Software Project? A Case Study in a Year-Long Project Course [1.553083901660282]
Testing plays an important role in securing the success of a software development project.
We investigate whether we can quantify the effects various types of testing have on functional suitability.
arXiv Detail & Related papers (2024-08-22T04:23:51Z) - VideoGUI: A Benchmark for GUI Automation from Instructional Videos [78.97292966276706]
VideoGUI is a novel multi-modal benchmark designed to evaluate GUI assistants on visual-centric GUI tasks.
Sourced from high-quality web instructional videos, our benchmark focuses on tasks involving professional and novel software.
Our evaluation reveals that even the SoTA large multimodal model GPT4o performs poorly on visual-centric GUI tasks.
arXiv Detail & Related papers (2024-06-14T17:59:08Z) - Interlinking User Stories and GUI Prototyping: A Semi-Automatic LLM-based Approach [55.762798168494726]
We present a novel Large Language Model (LLM)-based approach for validating the implementation of functional NL-based requirements in a graphical user interface (GUI) prototype.
Our approach aims to detect functional user stories that are not implemented in a GUI prototype and provides recommendations for suitable GUI components directly implementing the requirements.
arXiv Detail & Related papers (2024-06-12T11:59:26Z) - Leveraging Large Language Models for Efficient Failure Analysis in Game Development [47.618236610219554]
This paper proposes a new approach to automatically identify which change in the code caused a test to fail.
The method leverages Large Language Models (LLMs) to associate error messages with the corresponding code changes causing the failure.
Our approach reaches an accuracy of 71% in our newly created dataset, which comprises issues reported by developers at EA over a period of one year.
arXiv Detail & Related papers (2024-06-11T09:21:50Z) - Vision-Based Mobile App GUI Testing: A Survey [29.042723121518765]
Vision-based mobile app GUI testing approaches emerged with the development of computer vision technologies.
We provide a comprehensive investigation of the state-of-the-art techniques on 271 papers, among which 92 are vision-based studies.
arXiv Detail & Related papers (2023-10-20T14:04:04Z) - Improving Testing Behavior by Gamifying IntelliJ [13.086283144520513]
We introduce IntelliGame, a gamified plugin for the popular IntelliJ Java Integrated Development Environment.
IntelliGame rewards developers for positive testing behavior using a multi-level achievement system.
A controlled experiment with 49 participants reveals substantial differences in the testing behavior triggered by IntelliGame.
arXiv Detail & Related papers (2023-10-17T11:40:55Z) - Applied Awareness: Test-Driven GUI Development using Computer Vision and
Cryptography [0.0]
Test-driven development is impractical: it generally requires an initial implementation of the GUI to generate golden images or to construct interactive test scenarios.
We demonstrate a novel and immediately applicable approach of interpreting GUI presentation in terms of backend communications.
This focus on backend communication circumvents deficiencies in typical testing methodologies that rely on platform-dependent UI affordances or accessibility features.
arXiv Detail & Related papers (2020-06-05T22:46:48Z) - 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.