Software engineering metrics and models
Software engineering metrics and models
Estimating Software Project Effort Using Analogies
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
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
A Comparative Study of Cost Estimation Models for Web Hypermedia Applications
Empirical Software Engineering
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Combining techniques to optimize effort predictions in software project management
Journal of Systems and Software
A Simulation Study of the Model Evaluation Criterion MMRE
IEEE Transactions on Software Engineering
An empirical study of process-related attributes in segmented software cost-estimation relationships
Journal of Systems and Software
The adjusted analogy-based software effort estimation based on similarity distances
Journal of Systems and Software
Selecting Best Practices for Effort Estimation
IEEE Transactions on Software Engineering
Segmented software cost estimation models based on fuzzy clustering
Journal of Systems and Software
Reducing biases in individual software effort estimations: a combining approach
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
A study of project selection and feature weighting for analogy based software cost estimation
Journal of Systems and Software
Applying fuzzy neural network to estimate software development effort
Applied Intelligence
A study of the non-linear adjustment for analogy based software cost estimation
Empirical Software Engineering
APSEC '09 Proceedings of the 2009 16th Asia-Pacific Software Engineering Conference
Fuzzy grey relational analysis for software effort estimation
Empirical Software Engineering
Software Cost Estimation with COCOMO II
Software Cost Estimation with COCOMO II
Empirical Software Engineering
Functional networks as a novel data mining paradigm in forecasting software development efforts
Expert Systems with Applications: An International Journal
A Study of Improving the Accuracy of Software Effort Estimation Using Linearly Weighted Combinations
COMPSACW '10 Proceedings of the 2010 IEEE 34th Annual Computer Software and Applications Conference Workshops
Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions
SSBSE '10 Proceedings of the 2nd International Symposium on Search Based Software Engineering
Comparison of weighted grey relational analysis for software effort estimation
Software Quality Control
Predicting software project effort: A grey relational analysis based method
Expert Systems with Applications: An International Journal
Software effort estimation based on optimized model tree
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Segmentation of software engineering datasets using the m5 algorithm
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
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
Local vs. global models for effort estimation and defect prediction
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Ecological inference in empirical software engineering
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Data Mining Techniques for Software Effort Estimation: A Comparative Study
IEEE Transactions on Software Engineering
Exploiting the Essential Assumptions of Analogy-Based Effort Estimation
IEEE Transactions on Software Engineering
Analyzing software effort estimation using k means clustered regression approach
ACM SIGSOFT Software Engineering Notes
Software effort prediction using fuzzy clustering and functional link artificial neural networks
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
A PSO-based model to increase the accuracy of software development effort estimation
Software Quality Control
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Accurate estimation of software development effort is strongly associated with the success or failure of software projects. The clear lack of convincing accuracy and flexibility in this area has attracted the attention of researchers over the past few years. Despite improvements achieved in effort estimating, there is no strong agreement as to which individual model is the best. Recent studies have found that an accurate estimation of development effort in software projects is unreachable in global space, meaning that proposing a high performance estimation model for use in different types of software projects is likely impossible. In this paper, a localized multi-estimator model, called LMES, is proposed in which software projects are classified based on underlying attributes. Different clusters of projects are then locally investigated so that the most accurate estimators are selected for each cluster. Unlike prior models, LMES does not rely on only one individual estimator in a cluster of projects. Rather, an exhaustive investigation is conducted to find the best combination of estimators to assign to each cluster. The investigation domain includes 10 estimators combined using four combination methods, which results in 4017 different combinations. ISBSG, Maxwell and COCOMO datasets are utilized for evaluation purposes, which include a total of 573 real software projects. The promising results show that the estimate accuracy is improved through localization of estimation process and allocation of appropriate estimators. Besides increased accuracy, the significant contribution of LMES is its adaptability and flexibility to deal with the complexity and uncertainty that exist in the field of software development effort estimation.