ICST 2024
Mon 27 - Fri 31 May 2024 Canada

This program is tentative and subject to change.

Mon 27 May 2024 16:00 - 16:30 at Room 1 - Session 4 Chair(s): Martin Tappler

There are many widely used tools for measuring test-coverage and code-coverage. Test coverage is the ratio of requirements or other non-code artifacts covered by a test suite, while code-coverage is the ratio of source code covered by tests. Almost all coverage tools show a few certain subset of coverage values, and almost always either test-coverage or code-coverage measures. In a large-scale industrial web-application-testing setting, we were faced with the need to “integrate” several types of coverage data (including front-end and back-end code coverage with requirements coverage), and to see all of them “live” as large model-based test suites were running. By being unable to find any off-the-shelf toolset to address the above need, we have developed an open-source test coverage tool, specific for MBT, named MBTCover. In addition to code coverage, the tool measures and reports requirements and model coverage, “live” as a given MBT test suite is executing. In this paper, we present the features of the MBTCover tool and our experience from using it in multiple large test-automation projects in practice. Other software test engineers, who conduct web application testing and MBT, may find the tool useful in their projects.

This program is tentative and subject to change.

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
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