Identifying Characteristics of the Agile Development Process That Impact User Satisfaction
June 06, 2023 Β· Declared Dead Β· π International Conference on Evaluation & Assessment in Software Engineering
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Minshun Yang, Seiji Sato, Hironori Washizaki, Yoshiaki Fukazawa, Juichi Takahashi
arXiv ID
2306.03483
Category
cs.SE: Software Engineering
Citations
4
Venue
International Conference on Evaluation & Assessment in Software Engineering
Last Checked
4 months ago
Abstract
The purpose of this study is to identify the characteristics of Agile development processes that impact user satisfaction. We used user reviews of OSS smartphone apps and various data from version control systems to examine the relationships, especially time-series correlations, between user satisfaction and development metrics that are expected to be related to user satisfaction. Although no metrics conclusively indicate an improved user satisfaction, motivation of the development team, the ability to set appropriate work units, the appropriateness of work rules, and the improvement of code maintainability should be considered as they are correlated with improved user satisfaction. In contrast, changes in the release frequency and workload are not correlated.
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