Assessing the Reliability of a Human Estimator

Authors:
Gary D. Boetticher;Nazim Lokhandwala
Affiliations:
University of Houston, USA;University of Houston, USA
Venue:
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Year:
2007

Citing 6
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Abstract

Human-based estimation remains the predominant methodology of choice [1]. Understanding the human estimator is critical for improving the effort estimation process. Every human estimator draws upon their background in terms of domain knowledge, technical knowledge, experience, and education in formulating an estimate. This research uses estimator demographic information to construct over 4000 classifiers which distinguish between the best and worst types of estimators. Various attribute techniques are applied to determine most significant demographics. Best case models produce accuracy rates ranging from 74 to 80 percent. Some of the best case models are presented for gaining insight into how demographics impact effort estimation.