Sharing Experience Around Component Compositions
November 30, 2023 Β· Declared Dead Β· π Int. J. Distributed Syst. Technol.
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
GrΓ©gory Bourguin, Arnaud Lewandowski, Myriam Lewkowicz
arXiv ID
2311.18368
Category
cs.HC: Human-Computer Interaction
Citations
7
Venue
Int. J. Distributed Syst. Technol.
Last Checked
4 months ago
Abstract
Society currently lives in a world of tailorable systems in which end-users are able to transform their working environment while achieving their tasks, day to day and over the time. Tailorability is most of the time achieved through dynamic component integration thanks to a huge number of components available over the Internet. In this context, the main problem for users is not anymore the integration of new components, but how to find the most interesting set of components that will fulfill their needs. Facing this issue, the authors' assumption is that it would be helpful for users to take benefit of the experience of other users and our work aims at enhancing current software ecosystems to support this sharing of experience. The authors have applied this approach in the context of software development while considering Eclipse as one of the most advanced and used software ecosystem. The authors then offer ShareXP, an Eclipse feature that allows members of a group to share their expertise, this expertise being embodied in the ``compositions'' each of them has built. ShareXP was already presented in (Bourguin et al., 2012). The current paper is an extension where the authors deeper show that ShareXP is only a first step in their global approach trying to enhance not only the Eclipse ecosystem, but software ecosystems in general.
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