In The Field Monitoring of Interactive Applications
May 18, 2017 Β· Declared Dead Β· π 2017 IEEE/ACM 39th International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Oscar Cornejo, Daniela Briola, Daniela Micucci, Leonardo Mariani
arXiv ID
1705.06511
Category
cs.SE: Software Engineering
Citations
3
Venue
2017 IEEE/ACM 39th International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER)
Last Checked
4 months ago
Abstract
Monitoring techniques can extract accurate data about the behavior of software systems. When used in the field, they can reveal how applications behave in real-world contexts and how programs are actually exercised by their users. Nevertheless, since monitoring might need significant storage and computational resources, it may interfere with users activities degrading the quality of the user experience. While the impact of monitoring has been typically studied by measuring the overhead that it may introduce in a monitored application, there is little knowledge about how monitoring solutions may actually impact on the user experience and to what extent users may recognize their presence. In this paper, we present our investigation on how collecting data in the field may impact the quality of the user experience. Our initial results show that non-trivial overhead can be tolerated by users, depending on the kind of activity that is performed. This opens interesting opportunities for research in monitoring solutions, which could be designed to opportunistically
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