Reasoning with Noisy Software Effort Data
Bhekisipho Twala · Applied Artificial Intelligence · 2014
Constructing an accurate effort prediction model remains a challenge in software engineering. Recently, machine learning classifiers have been used successfully for software effort evaluation decisions. However, the development and validation of these classifiers (and other modes) require good quality data. Most research on machine learning assumes that the attributes of training and tests instances are not only completely specified but are also free from noise. Real-world industrial datasets, however, suffer from corruption or noise that is not always known. However, blindly applying such machine learning techniques to noisy software effort evaluation data may fail to make very good or perfect predictions, resulting in poor decisions and ineffective project management. This article investigates the effect of noisy domains on the learning accuracy of eight machine learning and statistical pattern recognition algorithms. We further derive solutions for the problem of noisy domains in software effort predic...