How Do Programmers Express High-Level Concepts using Primitive Data Types?
March 18, 2022 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
Yusuke Shinyama, Yoshitaka Arahori, Katsuhiko Gondow
arXiv ID
2203.09959
Category
cs.SE: Software Engineering
Citations
0
Venue
Asia-Pacific Software Engineering Conference
Last Checked
5 months ago
Abstract
We investigated how programmers express high-level concepts such as path names and coordinates using primitive data types. While relying too much on primitive data types is sometimes criticized as a bad smell, it is still a common practice among programmers. We propose a novel way to accurately identify expressions for certain predefined concepts by examining API calls. We defined twelve conceptual types used in the Java Standard API. We then obtained expressions for each conceptual type from 26 open source projects. Based on the expressions obtained, we trained a decision tree-based classifier. It achieved 83% F-score for correctly predicting the conceptual type for a given expression. Our result indicates that it is possible to infer a conceptual type from a source code reasonably well once enough examples are given. The obtained classifier can be used for potential bug detection, test case generation and documentation.
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