This study assessed hybrid (dynamic and static) techniques for software verification.
This dissertation investigates several hybrid techniques that explore dynamic (with program execution), static (without program execution) as well as the synergies of multiple approaches in software verification from the perspectives of testing and model checking. Experimental results demonstrated the effectiveness of the methods in improving the quality as well as performance of software verification. In order to validate the effectiveness of the proposed hybrid approaches, a wide range of experiments on academic and real-world programs were designed and conducted, with results compared against the original as well as the relevant verification methods. For software testing, the newly proposed coverage metric constructed based on dynamic program execution data is able to improve the quality of test cases generated in terms of mutation killing — a widely applied measurement for error detection. For software model checking, the proposed hybrid techniques greatly take advantage of the complementary benefits from both dynamic and static approaches: the lightweight dynamic techniques provide flexibility in extracting valuable high-level information that can be used to guide the scope and the direction of static reasoning process. It consequently results in significant performance improvement in software model checking. On the other hand, the static techniques guarantee the completeness of the verification results, compensating the weakness of dynamic methods. (Published Abstract Provided)
Downloads
Similar Publications
- Technology-Facilitated Abuse in Intimate Partner Violence (IPV): An Exploration of Costs and Consequences, Final Report
- Machine Learning and the Prevention of Mass Shooting in the United States
- Psychological Safety Among K-12 Educators: Patterns Over Time, and Associations with Staff Well-being and Organizational Context