Bayesian Analysis of Empirical Software Engineering Cost Models
IEEE Transactions on Software Engineering
Software Engineering Economics
Software Engineering Economics
Validation methods for calibrating software effort models
Proceedings of the 27th international conference on Software engineering
Column Pruning Beats Stratification in Effort Estimation
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Predicting software project effort: A grey relational analysis based method
Expert Systems with Applications: An International Journal
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Corporations invest over 300 billion dollars annually in software production. A key question in the software development process is, When will it be done? Estimating techniques include human-based (expert and analogy), algorithmic (Function Point Analysis and Cocomo [Cost Constructive Model], and machine learner-based. Human-based techniques are the most popular. However, machine learner-based techniques have generated impressive results, including accuracy rates within 25 percent, 83 percent of the time in the software life cycle's early stages. This article presents details from three machine learner successes in software effort estimation.