Wandercode: An Interaction Design for Code Recommenders to Reduce Information Overload, Ease Exploration, and Save Screen Space
August 26, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Austin Z. Henley, David Shepherd, Scott D. Fleming
arXiv ID
2408.14589
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In this paper, we present Wandercode, a novel interaction design for recommender systems that recommend code locations to aid programmers in software development tasks. In particular, our design aims to improve upon prior designs by reducing information overload, by better supporting the exploration of recommendations, and by making more efficient use of screen space. During our design process, we developed a set of design dimensions to aid others in the design of code recommenders. To validate our design, we implemented a prototype of our design as an Atom code editor extension with support for the Java programming language, and conducted an empirical user evaluation comparing our graph-based Wandercode design to a control design representative of prior list-based interaction designs for code recommenders. The results showed that, compared with the control design, Wandercode helped participants complete tasks more quickly, reduced their cognitive load, and was viewed more favorably by participants.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
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