A Hybrid Approach to Cleansing Software Measurement Data

Authors:
Taghi M. Khoshgoftaar;Jason Van Hulse;Chris Seiffert
Affiliations:
Florida Atlantic University, USA;-;-
Venue:
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Year:
2006

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Abstract

Data is extremely important in empirical software engineering. Techniques that provide insight into potential anomalies or inaccuracies in a dataset are becoming an increasingly important way for a data analyst to cope with flawed data. We present a novel hybrid procedure for quantitative outcome correction along with controlled experiments using a real-world software measurement dataset to demonstrate the usefulness of our technique. Instances that are deemed to be noisy relative to the dependent variable, which represents the number of faults recorded in the program module, are cleansed by replacing the original value with a more appropriate alternative value.