Evaluating the Effects of Different Requirements Representations on Writing Test Cases
Paper i proceeding, 2020
One must test a system to ensure that the requirements are met, thus, tests are often derived manually from requirements. However, requirements representations are diverse; from traditional IEEE-style text, to models, to agile user stories, the RE community of research and practice has explored various ways to capture requirements.
Question/problem
But, do these different representations influence the quality or coverage of test suites? The state-of-the-art does not provide insights on whether or not the representation of requirements has an impact on the coverage, quality, or size of the resulting test suite.
Results
In this paper, we report on a family of three experiment replications conducted with 148 students which examines the effect of different requirements representations on test creation. We find that, in general, the different requirements representations have no statistically significant impact on the number of derived tests, but specific affordances of the representation effect test quality, e.g., traditional textual requirements make it easier to derive less abstract tests, whereas goal models yield less inconsistent test purpose descriptions.
Contribution
Our findings give insights on the effects of requirements representation on test derivation for novice testers. Our work is limited in the use of students.
Requirements representation
Test design
Experiment
Författare
Francisco Gomes
Göteborgs universitet
Jennifer Horkoff
Göteborgs universitet
Richard Berntsson Svensson
Göteborgs universitet
David Issa Mattos
Chalmers, Data- och informationsteknik, Software Engineering
Alessia Knauss
Chalmers, Data- och informationsteknik, Software Engineering
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
03029743 (ISSN) 16113349 (eISSN)
Vol. 12045 LNCS 257-2749783030444280 (ISBN)
Pisa, Italy,
Ämneskategorier
Tillförlitlighets- och kvalitetsteknik
Programvaruteknik
Datavetenskap (datalogi)
DOI
10.1007/978-3-030-44429-7_18