An empirical validation of software cost estimation models
Communications of the ACM
Software engineering metrics and models
Software engineering metrics and models
Learning from Examples: Generation and Evaluation of Decision Trees for Software Resource Analysis
IEEE Transactions on Software Engineering - Special Issue on Artificial Intelligence in Software 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
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
A Procedure for Analyzing Unbalanced Datasets
IEEE Transactions on Software Engineering
A replicated assessment and comparison of common software cost modeling techniques
Proceedings of the 22nd international conference on Software engineering
A Vector-Based Approach to Software Size Measurement and Effort Estimation
IEEE Transactions on Software Engineering
ACM SIGSOFT Software Engineering Notes
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
A Simulation Tool for Efficient Analogy Based Cost Estimation
Empirical Software Engineering
Using Neural Networks in Reliability Prediction
IEEE Software
IEEE Transactions on Software Engineering
Predicting Testability of Program Modules Using a Neural Network
ASSET '00 Proceedings of the 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSET'00)
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
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
Analyzing Software Measurement Data with Clustering Techniques
IEEE Intelligent Systems
Estimating Software Costs
A General Empirical Solution to the Macro Software Sizing and Estimating Problem
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Improved estimation of software project effort using multiple additive regression trees
Expert Systems with Applications: An International Journal
Why comparative effort prediction studies may be invalid
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
A study of the non-linear adjustment for analogy based software cost estimation
Empirical Software Engineering
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
Information and Software Technology
Functional networks as a novel data mining paradigm in forecasting software development efforts
Expert Systems with Applications: An International Journal
A shift-invariant morphological system for software development cost estimation
Expert Systems with Applications: An International Journal
ACM SIGSOFT Software Engineering Notes
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
Empirical Software Engineering
Towards an early software estimation using log-linear regression and a multilayer perceptron model
Journal of Systems and Software
Estimating software project effort for manufacturing firms
Computers in Industry
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A critical issue in software project management is the accurate estimation of size, effort, resources, cost, and time spent in the development process. Underestimates may lead to time pressures that may compromise full functional development and the software testing process. Likewise, overestimates can result in noncompetitive budgets. In this paper, artificial neural network and stepwise regression based predictive models are investigated, aiming at offering alternative methods for those who do not believe in estimation models. The results presented in this paper compare the performance of both methods and indicate that these techniques are competitive with the APF, SLIM, and COCOMO methods.