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Good software cost models can significantly help software project managers. With good models, project stakeholders can make informed decisions about how to manage resources, how to control and plan the project, or how to deliver the project on time, on schedule, and on budget. Real-world data sets, such as those coming from software engineering projects, often contain noisy, irrelevant, or redundant variables. We propose that cost modelers should perform data-pruning experiments after data collection and before model building. Such pruning experiments are simple and fast.