Beta: a B based testing approach
Software systems are a big part of our lives and, more than ever, they require a high level of reliability. There are many software Verification and Validation (V&V) techniques that are concerned with quality control, security, robustness, and reliability; the most widely known are Software T...
Na minha lista:
| Autor principal: | |
|---|---|
| Outros Autores: | |
| Formato: | doctoralThesis |
| Idioma: | por |
| Publicado em: |
Brasil
|
| Assuntos: | |
| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/21417 |
| Tags: |
Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
|
| Resumo: | Software systems are a big part of our lives and, more than ever, they require a high level of
reliability. There are many software Verification and Validation (V&V) techniques that are
concerned with quality control, security, robustness, and reliability; the most widely known
are Software Testing and Formal Methods. Formal methods and testing are techniques that
can complement each other. While formal methods provide sound mechanisms to reason
about the system at a more abstract level, testing techniques are still necessary for a more
in-depth validation of the system and are often required by certification standards. Taking
this into consideration, BETA provides a tool-supported, model-based testing approach for
the B Method that is capable of generating unit tests from abstract B machines. In this thesis,
we present improvements made in the BETA approach and tool, and new cases studies
used to evaluate them. Among these improvements, we integrated logical coverage criteria
into the approach, reviewed the input space criteria that was already supported, and enhanced
the final steps of the test generation process. The approach now has support for
automatic generation of oracle data and test case preambles, it has a feature for test data
concretization, and a module that automatically generates executable test scripts. Another
objective of this thesis was to perform more complex case studies using BETA and assess the
quality of the test cases it produces. These case studies were the first to evaluate the test
generation process as a whole, from test case design to implementation and execution. In
our last experiments, we assessed the quality of the test cases generated by BETA, considering
each coverage criteria it supports, using code coverage metrics such as statement and
branch coverage. We also used mutation testing to evaluate the ability of the generated test
cases to identify faults in the model’s implementation. The results obtained were promising,
showing that BETA is capable of detecting faults introduced by a programmer or code
generation tool and that it can achieve good coverage results for a system’s implementation
based on a B model. |
|---|