The Impact of the Object-Oriented Software Evolution on Software Metrics: The Iris Approach
March 15, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ra'Fat Al-Msie'deen, Anas H. Blasi
arXiv ID
1803.09823
Category
cs.SE: Software Engineering
Citations
7
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The Object-Oriented (OO) software system evolves over the time to meet the new requirements. Based on the initial release of software, the continuous modification of software code leads to software evolution. Software needs to evolve over the time to meet the new user's requirements. Software companies often develop variant software of the original one depends on customers' needs. The main hypothesis of this paper states that the software when it evolves over the time, its code continues to grow, change and become more complex. This paper proposes an automatic approach (Iris) to examine the proposed hypothesis. Originality of this approach is the exploiting of the software variants to study the impact of software evolution on the software metrics. This paper presents the results of experiments conducted on three releases of drawing shapes software, sixteen releases of rhino software, eight releases of mobile media software and ten releases of ArgoUML software. Based on the extracted software metrics, It has been found that Iris hypothesis is supported by the computed metrics.
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