A pragmatic look at education and training of software test engineers: Further cooperation of academia and industry is needed
- URL: http://arxiv.org/abs/2408.06144v1
- Date: Mon, 12 Aug 2024 13:39:52 GMT
- Title: A pragmatic look at education and training of software test engineers: Further cooperation of academia and industry is needed
- Authors: Vahid Garousi, Alper Buğra Keleş,
- Abstract summary: It is important for both university educators and trainers in industry to be aware of the status of software testing education in academia.
This paper provides a pragmatic overview of the issue, presents several recommendations, and hopes to trigger further discussions.
- Score: 1.516251872371896
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Alongside software testing education in universities, a great extent of effort and resources are spent on software-testing training activities in industry. For example, there are several international certification schemes in testing, such as those provided by the International Software Testing Qualifications Board (ISTQB), which have been issued to more than 914K testers so far. To train the highly qualified test engineers of tomorrow, it is important for both university educators and trainers in industry to be aware of the status of software testing education in academia versus its training in industry, to analyze the relationships of these two approaches, and to assess ways on how to improve the education / training landscape. For that purpose, this paper provides a pragmatic overview of the issue, presents several recommendations, and hopes to trigger further discussions in the community, between industry and academia, on how to further improve the status-quo, and to find further best practices for more effective education and training of software testers. The paper is based on combined ~40 years of the two authors' technical experience in test engineering, and their ~30 years of experience in providing testing education and training in more than six countries.
Related papers
- SEAlign: Alignment Training for Software Engineering Agent [38.05820118124528]
We propose SEAlign to bridge the gap between code generation models and real-world software development tasks.
We evaluate SEAlign on three standard agentic benchmarks for real-world software engineering, including HumanEvalFix, SWE-Bench-Lite, and SWE-Bench-Verified.
We develop an agent-based software development platform using SEAlign, which successfully automates the creation of several small applications.
arXiv Detail & Related papers (2025-03-24T08:59:21Z) - Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants [175.9723801486487]
We evaluate whether two AI assistants, GPT-3.5 and GPT-4, can adequately answer assessment questions.
GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions.
Our results call for revising program-level assessment design in higher education in light of advances in generative AI.
arXiv Detail & Related papers (2024-08-07T12:11:49Z) - Bridging Theory to Practice in Software Testing Teaching through Team-based Learning (TBL) and Open Source Software (OSS) Contribution [3.190574537106449]
This paper presents a teaching approach for a software testing course that integrates theory and practical experience.
The paper reports on our experience implementing the pedagogical approach over four consecutive semesters of a Software Testing course within an undergraduate Software Engineering program.
arXiv Detail & Related papers (2024-04-16T21:16:17Z) - Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE.
We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - Seeking Enlightenment: Incorporating Evidence-Based Practice Techniques in a Research Software Engineering Team [0.7340017786387767]
Evidence-based practice (EBP) in software engineering aims to improve decision-making in software development by complementing practitioners' professional judgment with high-quality evidence from research.
We believe the use of EBP techniques may be helpful for research software engineers (RSEs) in their work to bring software engineering best practices to scientific software development.
We present an experience report on the use of a particular EBP technique, rapid reviews, within an RSE team at Sandia National Laboratories, and present practical recommendations for how to address barriers to EBP adoption within the RSE community.
arXiv Detail & Related papers (2024-03-25T14:52:18Z) - 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) - Are You a Real Software Engineer? Best Practices in Online Recruitment
for Software Engineering Studies [4.247193377317027]
Previous studies reported mixed outcomes and challenges in leveraging online platforms for the recruitment of qualified software engineers.
We propose best practices for recruiting and screening participants to enhance the quality and relevance of both qualitative and quantitative software engineering (SE) research samples.
arXiv Detail & Related papers (2024-02-02T21:53:28Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - Software Testing and Code Refactoring: A Survey with Practitioners [3.977213079821398]
This study aims to explore how software testing professionals deal with code to understand the benefits and limitations of this practice in the context of software testing.
We concluded that in the context of software testing, offers several benefits, such as supporting the maintenance of automated tests and improving the performance of the testing team.
Our study raises discussions on the importance of having testing professionals implement in the code of automated tests, allowing them to improve their coding abilities.
arXiv Detail & Related papers (2023-10-03T01:07:39Z) - 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) - An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago.
We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
arXiv Detail & Related papers (2022-03-08T15:46:52Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
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.