Process anti-pattern detection – a case study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43967863" target="_blank" >RIV/49777513:23520/22:43967863 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3551902.3551965" target="_blank" >https://dl.acm.org/doi/10.1145/3551902.3551965</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3551902.3551965" target="_blank" >10.1145/3551902.3551965</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Process anti-pattern detection – a case study

Popis výsledku v původním jazyce

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, startingfrom 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.

Název v anglickém jazyce

Process anti-pattern detection – a case study

Popis výsledku anglicky

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, startingfrom 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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_048%2F0007267" target="_blank" >EF17_048/0007267: VaV inteligentních komponent pokročilých technologií pro plzeňskou metropolitní oblast</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 27th European Conference on Pattern Languages of Programs 2022

ISBN

978-1-4503-9594-6

ISSN

—

e-ISSN

—

Počet stran výsledku

18

Strana od-do

1-18

Název nakladatele

The Association for Computing Machinery

Místo vydání

1601 Broadway, New York, New York 10019, USA

Místo konání akce

Irsee, Německo

Datum konání akce

6. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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