Topics of Concern: Identifying User Issues in Reviews of IoT Apps and Devices
February 18, 2019 Β· Declared Dead Β· π International Workshop on Software Engineering Research & Practices for the Internet of Things
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Andrew Truelove, Farah Naz Chowdhury, Omprakash Gnawali, Mohammad Amin Alipour
arXiv ID
1902.06384
Category
cs.HC: Human-Computer Interaction
Citations
6
Venue
International Workshop on Software Engineering Research & Practices for the Internet of Things
Last Checked
4 months ago
Abstract
Internet of Things (IoT) systems are bundles of networked sensors and actuators that are deployed in an environment and act upon the sensory data that they receive. These systems, especially consumer electronics, have two main cooperating components: a device and a mobile app. The unique combination of hardware and software in IoT systems presents challenges that are lesser known to mainstream software developers. They might require innovative solutions to support the development and integration of such systems. In this paper, we analyze more than 90,000 reviews of ten IoT devices and their corresponding apps and extract the issues that users encountered while using these systems. Our results indicate that issues with connectivity, timing, and updates are particularly prevalent in the reviews. Our results call for a new software-hardware development framework to assist the development of reliable IoT systems.
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