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. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. 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. 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. 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-estimations 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.

专家判断是当今实践中软件工作量估算的常用方法。通常会向估算人员显示额外的过时要求以及要实施的实际要求。以前仅进行过一项研究，以了解这种做法是否会使估计值产生偏差。我们与学生和从业人员进行了六个实验，以研究和量化过时需求对软件评估的影响。通过以学生和从业人员作为研究对象的六口之家进行实验（\（N = 461 \）），并使用贝叶斯数据分析方法，我们研究了这种影响的不同方面。我们还主张并举例说明如何通过贝叶斯方法使我们对我们的结果更有信心，并能以较小的样本量进行进一步的研究。我们发现过时的需求的存在在所有实验中都导致了对工作的高估。但是，与以学生为主体相比，在田野环境中的效果较小。尽管如此，过时的需求引发的高估在系统上大约是所包括的过时需求的百分比的两倍，但可信区间较大，为95％。结果对研究和实践都有影响，因为在软件估计的两项研究中都应考虑发现的系统错误，也许更重要的是，在估算方法中应避免由于此系统错误而导致的过度估算。我们部分解释此错误是由于锚定和调整的认知偏差引起的，即过时的要求锚定了一个更大的软件。但是，需要进行进一步的研究才能准确预测这种效果。