Context: Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. Objective: We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. Method By conducting a family of six experiments using both students and practitioners as research subjects (N = 461), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. Results: We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. Conclusions: The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimation due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect.
翻译:专家判断是当今实践中软件工作估算的一种常见方法; 估计者往往被显示为额外过时的要求以及实际需要执行的要求; 仅进行了先前的一项研究,研究这些做法是否偏向了估算。 目标:我们与学生和从业者进行了六次实验,以研究和量化软件估计方面过时要求的影响。 方法: 将学生和从业者作为研究科目进行六次实验(N=461),并采用巴耶斯数据分析方法,我们调查了这一效果的不同方面。 我们还主张并展示了一个实例,说明我们如何通过采用巴耶斯方法对结果更有信心,并能够用小的样本进行进一步研究。 结果:我们发现,由于存在过时的要求,在所有实验中,结果都会引起过高的估算。 然而,在实地环境中,与将学生作为研究科目相比,效果要小一些。 然而,过时要求引起的过高估计是包括过时要求的百分比的两倍左右,但有较大的95%的间隔。 结论: 研究结果对于我们的结果对于我们的结果和实践中的更大程度的研究和做法都具有更大的信心,但是系统估计必须解释这个错误。 我们发现,在系统估算中可能要解释这个错误的顺序。