A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Power analysis of embedded software: a first step towards software power minimization
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low-power design
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Effort estimation using analogy
Proceedings of the 18th international conference on Software engineering
A detailed cost model for concurrent use with hardware/software co-design
Proceedings of the 39th annual Design Automation Conference
Software Engineering Economics
Software Engineering Economics
Embedded System Design: A Unified Hardware/Software Introduction
Embedded System Design: A Unified Hardware/Software Introduction
A Simulation Tool for Efficient Analogy Based Cost Estimation
Empirical Software Engineering
Incorporating Cost Modeling in Embedded-System Design
IEEE Design & Test
Human Performance Estimating with Analogy and Regression Models: An Empirical Validation
METRICS '98 Proceedings of the 5th International Symposium on Software Metrics
A Simulation Study of the Model Evaluation Criterion MMRE
IEEE Transactions on Software Engineering
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Normalization as a Preprocessing Engine for Data Mining and the Approach of Preference Matrix
DEPCOS-RELCOMEX '06 Proceedings of the International Conference on Dependability of Computer Systems
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Selecting Best Practices for Effort Estimation
IEEE Transactions on Software Engineering
Cross versus Within-Company Cost Estimation Studies: A Systematic Review
IEEE Transactions on Software Engineering
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Building Software Cost Estimation Models using Homogenous Data
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
A General Empirical Solution to the Macro Software Sizing and Estimating Problem
IEEE Transactions on Software Engineering
Confidence in software cost estimation results based on MMRE and PRED
Proceedings of the 4th international workshop on Predictor models in software engineering
A method of programming measurement and estimation
IBM Systems Journal
On the dataset shift problem in software engineering prediction models
Empirical Software Engineering
Size doesn't matter?: on the value of software size features for effort estimation
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
A PSO-based model to increase the accuracy of software development effort estimation
Software Quality Control
Finding conclusion stability for selecting the best effort predictor in software effort estimation
Automated Software Engineering
| Hi-index | 0.00 |
Cost estimation and effort allocation are the key challenges for successful project planning and management in software development. Therefore, both industry and the research community have been working on various models and techniques to accurately predict the cost of projects. Recently, researchers have started debating whether the prediction performance depends on the structure of data rather than the models used. In this article, we focus on a new aspect of data homogeneity, "cross- versus within-application domain", and investigate what kind of training data should be used for software cost estimation in the embedded systems domain. In addition, we try to find out the effect of training dataset size on the prediction performance. Based on our empirical results, we conclude that it is better to use cross-domain data for embedded software cost estimation and the optimum training data size depends on the method used.