Detecting the presence of project management anti-patterns (AP) currently requires experts on the matter and is an expensive endeavor. Worse, experts may introduce their individual subjectivity or bias. Using the Fire Drill AP, we first introduce a novel way to translate descriptions into detectable AP that are comprised of arbitrary metrics and events such as logged time or maintenance activities, which are mined from the underlying source code or issue-tracking data, thus making the description objective as it becomes data-based. Secondly, we demonstrate a novel method to quantify and score the deviations of real-world projects to data-based AP descriptions. Using fifteen real-world projects that exhibit a Fire Drill to some degree, we show how to further enhance the translated AP. The ground truth in these projects was extracted from two individual experts and consensus was found between them. We introduce a novel method called automatic calibration, that optimizes a pattern such that only necessary and important scores remain that suffice to confidently detect the degree to which the AP is present. Without automatic calibration, the proposed patterns show only weak potential for detecting the presence. Enriching the AP with data from real-world projects significantly improves the potential. We also introduce a no-pattern approach that exploits the ground truth for establishing a new, quantitative understanding of the phenomenon, as well as for finding gray-/black-box predictive models. We conclude that the presence detection and severity assessment of the Fire Drill anti-pattern, as well as some of its related and similar patterns, is certainly possible using some of the presented approaches.

翻译：检测项目管理反模式（AP）的存在通常需要领域专家，且成本高昂。更糟糕的是，专家可能引入个体主观性或偏见。以火警演习反模式为例，我们首先提出一种新颖的方法，将描述转化为可检测的AP，这些AP由任意指标和事件（如记录的工作时间或维护活动）组成，这些指标和事件来源于底层源代码或问题追踪数据，从而使描述基于数据而客观化。其次，我们展示了一种量化真实世界项目与基于数据的AP描述之间偏差并进行评分的新方法。利用15个在一定程度上表现出火警演习的真实项目，我们展示了如何进一步优化翻译后的AP。这些项目的真实标注来自两位独立专家，且两者之间达成共识。我们引入一种名为自动校准的新方法，该方法优化模式，仅保留必要且重要的分数，足以可靠地检测AP存在的程度。没有自动校准时，所提出的模式在检测存在性方面仅显示出较弱的潜力。通过真实项目数据丰富AP显著提高了这一潜力。我们还提出了一种无模式方法，利用真实标注来建立对该现象的定量新理解，并寻找灰盒/黑盒预测模型。我们得出结论，利用所提出的部分方法，火警演习反模式及其相关或相似模式的存在检测与严重性评估是可行的。