Unit Testing Challenges with Automated Marking
October 10, 2023 Β· Declared Dead Β· π Asia-Pacific Software Engineering Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Chakkrit Tantithamthavorn, Norman Chen
arXiv ID
2310.06308
Category
cs.SE: Software Engineering
Citations
1
Venue
Asia-Pacific Software Engineering Conference
Last Checked
5 months ago
Abstract
Teaching software testing presents difficulties due to its abstract and conceptual nature. The lack of tangible outcomes and limited emphasis on hands-on experience further compound the challenge, often leading to difficulties in comprehension for students. This can result in waning engagement and diminishing motivation over time. In this paper, we introduce online unit testing challenges with automated marking as a learning tool via the EdStem platform to enhance students' software testing skills and understanding of software testing concepts. Then, we conducted a survey to investigate the impact of the unit testing challenges with automated marking on student learning. The results from 92 participants showed that our unit testing challenges have kept students more engaged and motivated, fostering deeper understanding and learning, while the automated marking mechanism enhanced students' learning progress, helping them to understand their mistakes and misconceptions quicker than traditional-style human-written manual feedback. Consequently, these results inform educators that the online unit testing challenges with automated marking improve overall student learning experience, and are an effective pedagogical practice in software testing.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted