The more Product Complexity, the more Actual Effort? An Empirical Investigation into Software Developments
September 28, 2015 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Zheng Li, Liam O'Brien, Ye Yang
arXiv ID
1509.08418
Category
cs.SE: Software Engineering
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
[Background:] Software effort prediction methods and models typically assume positive correlation between software product complexity and development effort. However, conflicting observations, i.e. negative correlation between product complexity and actual effort, have been witnessed from our experience with the COCOMO81 dataset. [Aim:] Given our doubt about whether the observed phenomenon is a coincidence, this study tries to investigate if an increase in product complexity can result in the abovementioned counter-intuitive trend in software development projects. [Method:] A modified association rule mining approach is applied to the transformed COCOMO81 dataset. To reduce noise of analysis, this approach uses a constant antecedent (Complexity increases while Effort decreases) to mine potential consequents with pruning. [Results:] The experiment has respectively mined four, five, and seven association rules from the general, embedded, and organic projects data. The consequents of the mined rules suggested two main aspects, namely human capability and product scale, to be particularly concerned in this study. [Conclusions:] The negative correlation between complexity and effort is not a coincidence under particular conditions. In a software project, interactions between product complexity and other factors, such as Programmer Capability and Analyst Capability, can inevitably play a "friction" role in weakening the practical influences of product complexity on actual development effort.
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