A Baseline Model for Software Effort Estimation

TL;DR

This paper introduces ATLM, a simple, deterministic linear model for software effort estimation that serves as a reliable baseline for comparing more complex methods across diverse project types.

Contribution

The paper proposes ATLM, a new baseline model for SEE that is simple, effective, and easy to replicate, addressing the need for standardized comparison methods.

Findings

ATLM performs well across various project types.

It requires no parameter tuning and handles mixed data types.

Results are deterministic and reproducible.

Abstract

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.