How can feature usage be tracked across product variants? Implicit Feedback in Software Product Lines
September 08, 2023 Β· Declared Dead Β· + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Oscar DΓaz, Raul Medeiros, Mustafa Al-Hajjaji
arXiv ID
2309.04278
Category
cs.SE: Software Engineering
Citations
6
Last Checked
4 months ago
Abstract
Implicit feedback is collecting information about software usage to understand how and when the software is used. This research tackles implicit feedback in Software Product Lines (SPLs). The need for platform-centric feedback makes SPL feedback depart from one-off-application feedback in both the artefact to be tracked (the platform vs the variant) as well as the tracking approach (indirect coding vs direct coding). Traditionally, product feedback is achieved by embedding `usage trackers' into the software's code. Yet, products are now members of the SPL portfolio, and hence, this approach conflicts with one of the main SPL tenants: reducing, if not eliminating, coding directly into the variant's code. Thus, we advocate for Product Derivation to be subject to a second transformation that precedes the construction of the variant based on the configuration model. This approach is tested through FEACKER, an extension to pure::variants. We resorted to a TAM evaluation on pure-systems GmbH employees(n=8). Observed divergences were next tackled through a focus group (n=3). The results reveal agreement in the interest in conducting feedback analysis at the platform level (perceived usefulness) while regarding FEACKER as a seamless
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