Process anti-pattern detection – a case study
Authors: 
Petr Pícha, Sebastian Hönel, Přemek Brada, Morgan Ericsson, Welf Löwe, Anna Wingkvist, Jakub DaněkAuthors Info & Claims
Petr Pícha, Sebastian Hönel, Přemek Brada, Morgan Ericsson, Welf Löwe, Anna Wingkvist, Jakub DaněkAuthors Info & ClaimsArticle No.: 5, Pages 1 - 18
Abstract
Anti-patterns are harmful phenomena repeatedly occurring, e.g., in software development projects. Though widely recognized and well-known, their descriptions are traditionally not fit for automated detection. The detection is usually performed by manual audits, or on business process models. Both options are time-, effort- and expertise-heavy, prone to biases, and/or omissions. Meanwhile, collaborative software projects produce much data as a natural side product, capturing their status and day-to-day history. Long-term, our research aims at deriving models for the automated detection of process and project management anti-patterns, applicable to project data. Here, we present a general approach for studies investigating occurrences of these types of anti-patterns in projects and discuss the entire process of such studies in detail, starting from the anti-pattern descriptions in literature. We demonstrate and verify our approach with the Fire Drill anti-pattern detection as a case study, applying it to data from 15 student projects. The results of our study suggest that reliable detection of at least some process and project management anti-patterns in project data is possible, with 13 projects assessed accurately for Fire Drill presence by our automated detection when compared to the ground truth gathered from independent data. The overall approach can be similarly applied to detecting patterns and other phenomena with manifestations in Application Lifecycle Management data.
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July 2022
338 pages
ISBN:9781450395946
DOI:10.1145/3551902
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Published: 07 February 2023
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EuroPLop '22
EuroPLop '22: 27th European Conference on Pattern Languages of Programs
July 6 - 10, 2022
Irsee, Germany
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