Engaging Developers in Exploratory Unit Testing through Gamification
- URL: http://arxiv.org/abs/2408.04918v1
- Date: Fri, 9 Aug 2024 08:00:41 GMT
- Title: Engaging Developers in Exploratory Unit Testing through Gamification
- Authors: Philipp Straubinger, Gordon Fraser,
- Abstract summary: We show challenges and quests generated by the Gamekins system to make testing more engaging and seamlessly blend it with regular coding tasks.
In a 60-minute experiment, we evaluated Gamekins' impact on test suite quality and bug detection.
Results show that participants actively interacted with the tool, achieving nearly 90% line coverage and detecting 11 out of 14 bugs.
- Score: 11.077232808482128
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Exploratory testing, known for its flexibility and ability to uncover unexpected issues, often faces challenges in maintaining systematic coverage and producing reproducible results. To address these challenges, we investigate whether gamification of testing directly in the Integrated Development Environment (IDE) can guide exploratory testing. We therefore show challenges and quests generated by the Gamekins gamification system to make testing more engaging and seamlessly blend it with regular coding tasks. In a 60-minute experiment, we evaluated Gamekins' impact on test suite quality and bug detection. The results show that participants actively interacted with the tool, achieving nearly 90% line coverage and detecting 11 out of 14 bugs. Additionally, participants reported enjoying the experience, indicating that gamification can enhance developer participation in testing and improve software quality.
Related papers
- SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories [55.161075901665946]
Super aims to capture the realistic challenges faced by researchers working with Machine Learning (ML) and Natural Language Processing (NLP) research repositories.
Our benchmark comprises three distinct problem sets: 45 end-to-end problems with annotated expert solutions, 152 sub problems derived from the expert set that focus on specific challenges, and 602 automatically generated problems for larger-scale development.
We show that state-of-the-art approaches struggle to solve these problems with the best model (GPT-4o) solving only 16.3% of the end-to-end set, and 46.1% of the scenarios.
arXiv Detail & Related papers (2024-09-11T17:37:48Z) - Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests [4.574205608859157]
We introduce UTGen, which combines search-based software testing and large language models to enhance the understandability of automatically generated test cases.
We observe that participants working on assignments with UTGen test cases fix up to 33% more bugs and use up to 20% less time when compared to baseline test cases.
arXiv Detail & Related papers (2024-08-21T15:35:34Z) - DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents [49.74065769505137]
We introduce DISCOVERYWORLD, the first virtual environment for developing and benchmarking an agent's ability to perform complete cycles of novel scientific discovery.
It includes 120 different challenge tasks spanning eight topics each with three levels of difficulty and several parametric variations.
We find that strong baseline agents, that perform well in prior published environments, struggle on most DISCOVERYWORLD tasks.
arXiv Detail & Related papers (2024-06-10T20:08:44Z) - Elevating Software Quality in Agile Environments: The Role of Testing Professionals in Unit Testing [0.0]
Testing is an essential quality activity in the software development process.
This paper explores the participation of test engineers in unit testing within an industrial context.
arXiv Detail & Related papers (2024-03-20T00:41:49Z) - Gamified GUI testing with Selenium in the IntelliJ IDE: A Prototype Plugin [0.559239450391449]
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.
arXiv Detail & Related papers (2024-03-14T20:11:11Z) - Observation-based unit test generation at Meta [52.4716552057909]
TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution.
TestGen has landed 518 tests into production, which have been executed 9,617,349 times in continuous integration, finding 5,702 faults.
Our evaluation reveals that, when carving its observations from 4,361 reliable end-to-end tests, TestGen was able to generate tests for at least 86% of the classes covered by end-to-end tests.
arXiv Detail & Related papers (2024-02-09T00:34:39Z) - Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors [57.7003399760813]
We explore advanced Large Language Models (LLMs) and their specialized variants, contributing to this field in several ways.
We uncover a significant correlation between topics and detection performance.
These investigations shed light on the adaptability and robustness of these detection methods across diverse topics.
arXiv Detail & Related papers (2023-12-20T10:53:53Z) - 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) - DIAMBRA Arena: a New Reinforcement Learning Platform for Research and
Experimentation [91.3755431537592]
This work presents DIAMBRA Arena, a new platform for reinforcement learning research and experimentation.
It features a collection of high-quality environments exposing a Python API fully compliant with OpenAI Gym standard.
They are episodic tasks with discrete actions and observations composed by raw pixels plus additional numerical values.
arXiv Detail & Related papers (2022-10-19T14:39:10Z) - SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video
Games Using Risk Based Testing and Machine Learning [62.997667081978825]
Testing video games is an increasingly difficult task as traditional methods fail to scale with growing software systems.
We present SUPERNOVA, a system responsible for test selection and defect prevention while also functioning as an automation hub.
The direct impact of this has been observed to be a reduction in 55% or more testing hours for an undisclosed sports game title.
arXiv Detail & Related papers (2022-03-10T00:47:46Z) - COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and
Misunderstandings [8.154109462429988]
We review the current landscape of COVID-19 testing, identify four key challenges, and discuss the consequences of the failure to address these challenges.
The current infrastructure around testing and information propagation is highly privacy-invasive and does not leverage scalable digital components.
arXiv Detail & Related papers (2021-01-05T18:37:26Z)
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.