Automated Test Generation from Program Documentation Encoded in Code Comments
April 29, 2025 Β· Declared Dead Β· π International Conference/Workshop on Automation of Software Test
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Giovanni Denaro, Luca Guglielmo
arXiv ID
2504.21161
Category
cs.SE: Software Engineering
Citations
0
Venue
International Conference/Workshop on Automation of Software Test
Last Checked
5 months ago
Abstract
Documenting the functionality of software units with code comments, e.g., Javadoc comments, is a common programmer best-practice in software engineering. This paper introduces a novel test generation technique that exploits the code-comment documentation constructively. We originally address those behaviors as test objectives, which we pursue in search-based fashion. We deliver test cases with names and oracles properly contextualized on the target behaviors. Our experiments against a benchmark of 118 Java classes indicate that the proposed approach successfully tests many software behaviors that may remain untested with coverage-driven test generation approaches, and distinctively detects unknown failures.
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