Abstract
dc:description.abstractMutation analysis is extensively used for the comparison of state-based testing methods that work from a finite state machine (FSM); In this thesis, we report on results from an experiment during which we compared different mutation operators used to generate FSM mutants. We randomly generated multiple synthetic FSMs and selected real-world FSMs; We created all the possible mutants from a complete set of FSM mutation operators; We generated multiple test suites using a pool of test paths; We executed all those test suites on FSM mutants while using different types of oracles, and compared mutation operators using a metric we define. Results help us identify mutation operators that lead to easy-to-reveal faults, possibly too easy to help discriminate between test suite construction techniques, as well as study the impact of various oracle strategies. We have developed a tool infrastructure called μFSM that automates the whole process of this experiment.
Degree
thesis:*- Name thesis:degree_name
- Master of Applied Science (M.App.Sc.)
- Level thesis:degree_level
- Master's
- Discipline thesis:degree_discipline
- Engineering, Electrical and Computer
- Grantor dc:publisher
- Carleton University
- Year dc:date.issued
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Nikbin Azmoudeh, Danial
Rights
dc:rights- Statement dc:rights
-
- Copyright © 2024 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- OAI identifier oai:identifier
- oai:carleton.scholaris.ca:20.500.14718/41783