  Software effort estimation (SEE) is a core activity in all software processes
and development lifecycles. A range of increasingly complex methods has been
considered in the past 30 years for the prediction of effort, often with mixed
and contradictory results. The comparative assessment of effort prediction
methods has therefore become a common approach when considering how best to
predict effort over a range of project types. Unfortunately, these assessments
use a variety of sampling methods and error measurements, making comparison
with other work difficult. This article proposes an automatically transformed
linear model (ATLM) as a suitable baseline model for comparison against SEE
methods. ATLM is simple yet performs well over a range of different project
types. In addition, ATLM may be used with mixed numeric and categorical data
and requires no parameter tuning. It is also deterministic, meaning that
results obtained are amenable to replication. These and other arguments for
using ATLM as a baseline model are presented, and a reference implementation
described and made available. We suggest that ATLM should be used as a baseline
of effort prediction quality for all future model comparisons in SEE.
