ICST 2024
Mon 27 - Fri 31 May 2024 Canada
Mon 27 May 2024 17:00 - 17:30 at Room 1 - Session 4 Chair(s): Martin Tappler
Several studies have shown Model-based Testing (MBT) as an efficient technique for generating fault-effective test cases. However, the automatic generation of test cases is compromised with redundant test cases providing no additional value to the coverage or fault detection effectiveness while impacting test execution efficiency, especially, in a dynamic development environment where providing timely feedback is crucial. These redundant test cases need to be discarded to minimize the test suite size and their effect on the execution cost and efficiency of a test suite. Reducing a test suite becomes challenging for black box testing at the system level when no information regarding the coverage and fault detection effectiveness of the test suite exists. Hence, in this paper, we have presented a test suite optimization approach leveraging different machine learning algorithms, a greedy algorithm, and a similarity measure. The proposed approach generates a reduced test suite by identifying and eliminating redundant test cases from an MBT-generated test suite while having minimal impact on the fault detection rate. We have also performed a comparative evaluation of the optimized test suites with the MBT-generated and manually created test suites in terms of fault detection effectiveness and test execution efficiency using an industrial case study from Alstom Rail AB, Sweden. The results show a significant reduction of 85% to 92% in the size of the test suite. Moreover, we also found the test execution time of the optimized test suite equivalent to the manually created tests and a fault detection rate within the range of 95% to 100% for all test suites under observation.

Mon 27 May

Displayed time zone: Eastern Time (US & Canada) change

16:00 - 17:30
Session 4A-MOST at Room 1
Chair(s): Martin Tappler TU Wien, Austria
16:00
30m
Short-paper
Coverage measurement in model-based testing of web applications: Tool support and an industrial experience report
A-MOST
16:30
30m
Short-paper
Modeling and Safety Analysis of Autonomous Underwater Vehicles Behaviors
A-MOST
Sergio Quijano IT University of Copenhagen, Mahsa Varshosaz IT University of Copenhagen, Denmark, Andrzej Wąsowski IT University of Copenhagen, Denmark
17:00
30m
Full-paper
Optimizing Model-based Generated Tests: Leveraging Machine Learning for Test Reduction
A-MOST
Muhammad Nouman Zafar Malardalen University, Wasif Afzal Mälardalen University, Eduard Paul Enoiu Mälardalen University, Zulqarnain Haider , Inderjeet Singh Alstom