Software engineering metrics and models
Software engineering metrics and models
Kendall's advanced theory of statistics
Kendall's advanced theory of statistics
Introduction to neural networks
Introduction to neural networks
Case-based reasoning
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
The nature of statistical learning theory
The nature of statistical learning theory
Neural network design
An empirical study of software maintenance tasks
Journal of Software Maintenance: Research and Practice
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
An assessment and comparison of common software cost estimation modeling techniques
Proceedings of the 21st international conference on Software engineering
A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models
IEEE Transactions on Software Engineering
An investigation of machine learning based prediction systems
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
Software Cost Estimation with Incomplete Data
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Comparing Software Prediction Techniques Using Simulation
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
An Empirical Study of Analogy-based Software Effort Estimation
Empirical Software Engineering
A Simulation Tool for Efficient Analogy Based Cost Estimation
Empirical Software Engineering
Web Metrics Estimating Design and Authoring Effort
IEEE MultiMedia
A Further Empirical Investigation of the Relationship Between MRE and Project Size
Empirical Software Engineering
A Comparative Study of Cost Estimation Models for Web Hypermedia Applications
Empirical Software Engineering
Using Public Domain Metrics To Estimate Software Development Effort
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
A Simulation Study of the Model Evaluation Criterion MMRE
IEEE Transactions on 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
Preliminary Data Analysis Methods in Software Estimation
Software Quality Control
Reliability and Validity in Comparative Studies of Software Prediction Models
IEEE Transactions on Software Engineering
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
Evidence-Based Guidelines for Assessment of Software Development Cost Uncertainty
IEEE Transactions on Software Engineering
Optimal Project Feature Weights in Analogy-Based Cost Estimation: Improvement and Limitations
IEEE Transactions on Software Engineering
Categorical missing data imputation for software cost estimation by multinomial logistic regression
Journal of Systems and Software
Benchmarking k-nearest neighbour imputation with homogeneous Likert data
Empirical Software Engineering
A new imputation method for small software project data sets
Journal of Systems and Software
The adjusted analogy-based software effort estimation based on similarity distances
Journal of Systems and Software
A flexible method for software effort estimation by analogy
Empirical Software Engineering
IEEE Transactions on Software Engineering
Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+
Empirical Software Engineering
Journal of Systems and Software
A study of project selection and feature weighting for analogy based software cost estimation
Journal of Systems and Software
Expert Systems with Applications: An International Journal
Least modification principle for case-based reasoning: a software project planning experience
Expert Systems with Applications: An International Journal
An empirical analysis of linear adaptation techniques for case-based prediction
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
A review of studies on expert estimation of software development effort
Journal of Systems and Software
Stable rankings for different effort models
Automated Software Engineering
Information and Software Technology
Adaptive ridge regression system for software cost estimating on multi-collinear datasets
Journal of Systems and Software
Software effort estimation based on optimized model tree
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Adjusted case-based software effort estimation using bees optimization algorithm
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation
Empirical Software Engineering
Hybrid morphological methodology for software development cost estimation
Expert Systems with Applications: An International Journal
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
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Cost estimation is one of the most important but most difficult tasks in software project management. Many methods have been proposed for software cost estimation. Analogy Based Estimation (ABE), which is essentially a case-based reasoning (CBR) approach, is one popular technique. To improve the accuracy of ABE method, several studies have been focusing on the adjustments to the original solutions. However, most published adjustment mechanisms are based on linear forms and are restricted to numerical type of project features. On the other hand, software project datasets often exhibit non-normal characteristics with large proportions of categorical features. To explore the possibilities for a better adjustment mechanism, this paper proposes Artificial Neural Network (ANN) for Non-linear adjustment to ABE (NABE) with the learning ability to approximate complex relationships and incorporating the categorical features. The proposed NABE is validated on four real world datasets and compared against the linear adjusted ABEs, CART, ANN and SWR. Subsequently, eight artificial datasets are generated for a systematic investigation on the relationship between model accuracies and dataset properties. The comparisons and analysis show that non-linear adjustment could generally extend ABE's flexibility on complex datasets with large number of categorical features and improve the accuracies of adjustment techniques.