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Injecting software faults in Python applications The OpenStack case study
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DOI：10.1007/s10664-021-10047-9.png)
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Software fault injection techniques have been largely used as means for evaluating the dependability of systems in presence of certain types of faults. Despite the large diversity of tools offering the possibility of emulating the presence of software faults, there is little practical support for emulating the presence of software faults in Python applications, which are increasingly being used to support business critical cloud services. In this paper, we present FIT4Python, a tool for injecting software faults in Python code and then use it, in a mutation testing campaign, to analyse the effectiveness of OpenStack's test suite against new probable software faults. We begin by analysing the types of faults affecting Nova Compute, the core component of OpenStack. We use our tool to emulate the presence of new faults in Nova Compute API to understand how well OpenStack's battery of unit, functional, and integration tests cover these new, but probable, situations. Results show clear limitations in the effectiveness of OpenStack developers' test suites, with many cases of injected faults passing undetected through all three types of tests and that nearly half of the analysed problems could be detected with trivial changes or additions to the unit tests.
Keyword:
Software faults
Bug reports
Orthogonal defect classification
Fault injection
Mutation testing
Dependability evaluation
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