Activity-Based Detection of (Anti-)Patterns: An Embedded Case Study of the Fire Drill

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43971999" target="_blank" >RIV/49777513:23520/24:43971999 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.e-informatyka.pl/index.php/einformatica/volumes/volume-2024/issue-1/article-6/" target="_blank" >https://www.e-informatyka.pl/index.php/einformatica/volumes/volume-2024/issue-1/article-6/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.37190/e-Inf240106" target="_blank" >10.37190/e-Inf240106</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Activity-Based Detection of (Anti-)Patterns: An Embedded Case Study of the Fire Drill

Popis výsledku v původním jazyce

Background: Nowadays, expensive, error-prone, expert-based evaluations are needed to identify and assess software process anti-patterns. Process artifacts cannot be automatically used to quantitatively analyze and train prediction models without exact ground truth. Aim: Develop a replicable methodology for organizational learning from process (anti-)patterns, demonstrating the mining of reliable ground truth and exploitation of process artifacts. Method: We conduct an embedded case study to find manifestations of the Fire Drill anti-pattern in n = 15 projects. To ensure quality, three human experts agree. Their evaluation and the process’ artifacts are utilized to establish a quantitative understanding and train a prediction model. Results: Qualitative review shows many project issues. (i) Expert assessments consistently provide credible ground truth. (ii) Fire Drill phenomenological descriptions match project activity time (for example, development). (iii) Regression models trained on ≈ 12–25 examples are sufficiently stable. Conclusion: The approach is data source-independent (source code or issue-tracking). It allows leveraging process artifacts for establishing additional phenomenon knowledge and training robust predictive models. The results indicate the aptness of the methodology for the identification of the Fire Drill and similar anti-pattern instances modeled using activities. Such identification could be used in post mortem process analysis supporting organizational learning for improving processes.

Název v anglickém jazyce

Activity-Based Detection of (Anti-)Patterns: An Embedded Case Study of the Fire Drill

Popis výsledku anglicky

Background: Nowadays, expensive, error-prone, expert-based evaluations are needed to identify and assess software process anti-patterns. Process artifacts cannot be automatically used to quantitatively analyze and train prediction models without exact ground truth. Aim: Develop a replicable methodology for organizational learning from process (anti-)patterns, demonstrating the mining of reliable ground truth and exploitation of process artifacts. Method: We conduct an embedded case study to find manifestations of the Fire Drill anti-pattern in n = 15 projects. To ensure quality, three human experts agree. Their evaluation and the process’ artifacts are utilized to establish a quantitative understanding and train a prediction model. Results: Qualitative review shows many project issues. (i) Expert assessments consistently provide credible ground truth. (ii) Fire Drill phenomenological descriptions match project activity time (for example, development). (iii) Regression models trained on ≈ 12–25 examples are sufficiently stable. Conclusion: The approach is data source-independent (source code or issue-tracking). It allows leveraging process artifacts for establishing additional phenomenon knowledge and training robust predictive models. The results indicate the aptness of the methodology for the identification of the Fire Drill and similar anti-pattern instances modeled using activities. Such identification could be used in post mortem process analysis supporting organizational learning for improving processes.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

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

2024

Kód důvěrnosti údajů

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

Název periodika

e-Informatica Software Engineering Journal (EISEJ)

ISSN

1897-7979

e-ISSN

2084-4840

Svazek periodika

18

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

49

Strana od-do

—

Kód UT WoS článku

001229559900001

EID výsledku v databázi Scopus

2-s2.0-85188276370