An empirical validation of software cost estimation models
Communications of the ACM
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A replicated assessment and comparison of common software cost modeling techniques
Proceedings of the 22nd international conference on Software engineering
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Software Engineering Economics
Software Engineering Economics
An Empirical Study of Analogy-based Software Effort Estimation
Empirical Software Engineering
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Issues on the Effective Use of CBR Technology for Software Project Prediction
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
When Will It Be Done? Machine Learner Answers to the 300-Billion-Dollar Question
IEEE Intelligent Systems
On-Demand Forecasting of Stock Prices Using a Real-Time Predictor
IEEE Transactions on Knowledge and Data Engineering
A Review of Surveys on Software Effort Estimation
ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
Software effort estimation by analogy and "regression toward the mean"
Journal of Systems and Software - Special issue: Best papers on Software Engineering from the SEKE'01 Conference
Validation methods for calibrating software effort models
Proceedings of the 27th international conference on Software engineering
Using Grey Relational Analysis to Predict Software Effort with Small Data Sets
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
A General Empirical Solution to the Macro Software Sizing and Estimating Problem
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
APSEC '07 Proceedings of the 14th Asia-Pacific Software Engineering Conference
Target tracking using a hierarchical grey-fuzzy motion decision-making method
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The gray prediction search algorithm for block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Recent methods for software effort estimation by analogy
ACM SIGSOFT Software Engineering Notes
Memory performance prediction of web server applications based on grey system theory
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
A PSO-based model to increase the accuracy of software development effort estimation
Software Quality Control
LMES: A localized multi-estimator model to estimate software development effort
Engineering Applications of Artificial Intelligence
Grey relational effort analysis technique using robust regression methods for individual projects
International Journal of Computational Intelligence Studies
Hi-index | 12.05 |
The inherent uncertainty of the software development process presents particular challenges for software effort prediction. We need to systematically address missing data values, outlier detection, feature subset selection and the continuous evolution of predictions as the project unfolds, and all of this in the context of data-starvation and noisy data. However, in this paper, we particularly focus on outlier detection, feature subset selection, and effort prediction at an early stage of a project. We propose a novel approach of using grey relational analysis (GRA) from grey system theory (GST), which is a recently developed system engineering theory based on the uncertainty of small samples. In this work we address some of the theoretical challenges in applying GRA to outlier detection, feature subset selection, and effort prediction, and then evaluate our approach on five publicly available industrial data sets using both stepwise regression and Analogy as benchmarks. The results are very encouraging in the sense of being comparable or better than other machine learning techniques and thus indicate that the method has considerable potential.