Draw This Object: A Study of Debugging Representations
November 11, 2019 Β· Declared Dead Β· π International Conference on the Art, Science and Engineering of Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
MatΓΊΕ‘ SulΓr, JΓ‘n JuhΓ‘r
arXiv ID
1911.04422
Category
cs.SE: Software Engineering
Cross-listed
cs.PL
Citations
2
Venue
International Conference on the Art, Science and Engineering of Programming
Last Checked
4 months ago
Abstract
Domain-specific debugging visualizations try to provide a view of a runtime object tailored to a specific domain and highlighting its important properties. The research in this area has focused mainly on the technical aspects of the creation of such views so far. However, we still lack answers to questions such as what properties of objects are considered important for these visualizations, whether all objects have an appropriate domain-specific view, or what clues could help us to construct these views fully automatically. In this paper, we describe an exploratory study where the participants were asked to inspect runtime states of objects displayed in a traditional debugger and draw ideal domain-specific views of these objects on paper. We describe interesting observations and findings obtained during this study and a preliminary taxonomy of these visualizations.
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