是否有可能忽视障碍性要求？软件工作量估计的一族实验
Is it Possible to Disregard Obsolete Requirements? A Family of Experiments in Software Effort Estimation
摘要：
背景：专家判断是当今实践中软件工作量估计的常用方法。评估人员经常会看到额外的过时需求和真正需要实现的需求。以前只进行了一项研究，以确定这种做法是否会使估计产生偏差。目的：我们进行了六个实验，学生和从业人员的研究，并量化，过时的需求对软件评估的影响。方法通过进行一个家庭的六个实验，学生和从业者作为研究对象（N = 461），并通过使用贝叶斯数据分析方法，我们调查了不同方面的这种效果。我们还主张，并展示了一个例子，我们如何通过使用贝叶斯方法可以更有信心，在我们的结果，并使进一步的研究与小样本量。结果：我们发现，在所有实验中，过时的需求的存在引发了对努力的高估。然而，与以学生为受试者相比，在实地环境中的效果较小。尽管如此，由过时需求引发的高估系统性地大约是包含过时需求的百分比的两倍，但具有95%的可信区间。结论：研究结果和实践中发现的系统误差应占在软件估计的研究，也许更重要的是，在估计的做法，以避免过度估计，由于这种系统误差的影响。我们部分地解释了这个错误是源于锚定和调整的认知偏差，即过时的需求锚定了一个更大的软件。然而，需要进一步的研究来准确预测这种影响。
英文摘要：
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.
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