  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.
