敏捷工作量估算：我们已经解决了这个问题吗？复制研究的见解
Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study
摘要：
在过去的十年中，一些研究探索了自动化技术来评估敏捷软件开发的工作量。我们对一项开创性的工作进行了密切的复制和扩展，该工作提出了使用深度学习进行敏捷工作量估计（即Deep-SE），该工作量估计自那时以来一直处于最先进的水平。具体来说，我们复制了三个原始的研究问题，旨在调查Deep-SE在项目内和跨项目工作量估计方面的有效性。我们将Deep-SE与三条基线（即，随机，平均和中位数的努力估计）和以前提出的方法来估计敏捷软件项目开发工作（称为TF/IDF-SVM），在原来的研究中所做的。为此，我们使用了原始研究的数据和从TAWOS挖掘的31，960个问题的额外数据集，因为使用更多数据可以增强对结果的信心，并进一步减轻外部有效性威胁。我们的复制结果表明，Deep-SE仅在极少数具有统计学显著性的病例中优于中位数基线估计值和TF/IDF-SVM（分别为8/42和9/32例），因此混淆了先前关于Deep-SE疗效的发现。两个额外的RQ显示，无论是增加训练集还是预训练Deep-SE播放都不会提高其准确性和收敛速度。这些结果表明，使用语义相似性是不够的，以区分用户的故事，他们的故事点，因此，未来的工作还有待探索和找到新的技术和功能，获得准确的敏捷软件开发的估计。
英文摘要：
In the last decade, several studies have explored automated techniques to estimate the effort of agile software development. We perform a close replication and extension of a seminal work proposing the use of Deep Learning for Agile Effort Estimation (namely Deep-SE), which has set the state-of-the-art since. Specifically, we replicate three of the original research questions aiming at investigating the effectiveness of Deep-SE for both within-project and cross-project effort estimation. We benchmark Deep-SE against three baselines (i.e., Random, Mean and Median effort estimators) and a previously proposed method to estimate agile software project development effort (dubbed TF/IDF-SVM), as done in the original study. To this end, we use the data from the original study and an additional dataset of 31,960 issues mined from TAWOS, as using more data allows us to strengthen the confidence in the results, and to further mitigate external validity threats. The results of our replication show that Deep-SE outperforms the Median baseline estimator and TF/IDF-SVM in only very few cases with statistical significance (8/42 and 9/32 cases, respectively), thus confounding previous findings on the efficacy of Deep-SE. The two additional RQs revealed that neither augmenting the training set nor pre-training Deep-SE play lead to an improvement of its accuracy and convergence speed. These results suggest that using semantic similarity is not enough to differentiate user stories with respect to their story points; thus, future work has yet to explore and find new techniques and features that obtain accurate agile software development estimates.
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