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.
翻译:软件努力估计(SEE)是所有软件过程和发展生命周期的一项核心活动。在过去30年中,为预测工作而考虑了一系列日益复杂的方法,往往结果混杂和相互矛盾。因此,对工作预测方法的比较性评估在考虑如何最好地预测一系列项目类型的工作时已成为一种共同的方法。不幸的是,这些评估使用了各种抽样方法和差错测量,与其他工作比较困难。本条款建议自动转换线性模型(ATLM)作为与SEE方法比较的合适基准模型。ATLM简单,但在不同的项目类型中运行良好。此外,ATLM可能与数字和绝对数据混合使用,不需要参数调整。它还具有确定性,意味着所获得的结果是可以复制的。这些论点和其他一些论点都提出使用ATLM作为基线模型,并描述和提供了参考执行情况。我们建议,应该将ATLM作为SEE所有未来模型比较工作质量的基准。