Empirical Evaluation of Test Driven Modeling

Empirical Evaluation of Test Driven Modeling

Stefan Zugal (Department of Computer Science, University of Innsbruck, Innsburck, Austria), Cornelia Haisjackl (Department of Computer Science, University of Innsbruck, Innsburck, Austria), Jakob Pinggera (Department of Computer Science, University of Innsbruck, Innsburck, Austria) and Barbara Weber (Department of Computer Science, University of Innsbruck, Innsburck, Austria)
Copyright: © 2013 |Pages: 21
DOI: 10.4018/jismd.2013040102
OnDemand PDF Download:
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Declarative approaches to process modeling are regarded well suited for highly volatile environments as they provide a high degree of flexibility. However, problems in understanding and maintaining declarative process models impede their usage. To compensate for these shortcomings, Test Driven Modeling (TDM) has been proposed. This paper reports on an empirical investigation in which TDM is viewed from two different angles. First, the impact of TDM on communication is explored in a case study. Results indicate that domain experts are inclined to use test cases for communicating with the model builder (system analyst) and prefer them over the process model. The second part of the investigation, a controlled experiment, investigates the impact of TDM on process model maintenance. Data gathered in this experiment indicates that the adoption of test cases significantly lowers cognitive load and increases the perceived quality of changes.
Article Preview
Top

Background

In this section background information is provided. First, declarative processes and associated problems are discussed. Then, it is sketched how TDM aims at resolving these problems.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 12: 4 Issues (2021): 1 Released, 3 Forthcoming
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing