Do estimators learn?

On the effect of a positively skewed distribution of effort data on software portfolio productivity

Conference paper (2016)

Authors

H.K.M. Huijgens Software Engineering - EEMCS , Goverdson
Research Group
Software Engineering (EEMCS) (TU Delft)
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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Software Engineering

Abstract

We study whether an assumed positively skewed distribution of effort data prevents software estimators to learn over time; leading to increasing differences between planned and actual effort and a deteriorating (worsening) trend on productivity. We analyze data of 25 software releases of one application, collected over a period of six years in a public sector institution in The Netherlands. We statistically test for distribution, trend on differences between planned versus actual effort over time, and productivity of software portfolios. The key contributions of this paper are that we show that a proposed assumption that assumes any relation between a positively skewed distribution of effort data and a deteriorating productivity is not applicable to the subject dataset. We find that the effort data is to be characterized as positively skewed distributed, and we do see a shift over time from under-estimation to over-estimation. We do not find evidence for a deteriorating productivity; on the contrary productivity improves over time, indicating that estimators in the subject organization did learn.