Author
Gebizli, C. Ş., Kırkıcı, A., Sözer, Hasan
Publication Date
2018-10
Publication Place
-
Elsevier
Subject
Model-based testing, Model refinement, Statistical usage testing, Risk-based testing, Industrial case study, Software test automation
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0164-1212
Record ID
1138610f-4ad1-4197-afee-a79308564506
Library Location
Computer Science
Date
2018-10
Notes
Vestel Electronics ; Turkish Ministry of Science, Industry and Technology
Sample Text
We introduce an approach and a tool, RIMA, for adapting test models used for model-based testing to augment information regarding failure risk. We represent test models in the form of Markov chains. These models comprise a set of states and a set of state transitions that are annotated with probability values. These values steer the test case generation process, which aims at covering the most probable paths. RIMA refines these models in 3 steps. First, it updates transition probabilities based on a collected usage profile. Second, it updates the resulting models based on fault likelihood at each state, which is estimated based on static code analysis. Third, it performs updates based on error likelihood at each state, which is estimated with dynamic analysis. The approach is evaluated with two industrial case studies for testing digital TVs and smart phones. Results show that the approach increases test efficiency by revealing more faults in less testing time.
DOI
10.1016/j.jss.2018.06.080
Cilt
144
