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Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no "test oracle" to indicate what the correct output should...
Previous studies showed that adaptive random testing is an effective alternative to random testing method, but requires additional overheads to evenly spread test cases. Mirroring was introduced to reduce the overheads of adaptive random testing. This paper is the follow-up work to a previous study on the integration of mirroring and adaptive random testing, namely the mirror adaptive random testing...
Increased size and complexity of software requires better methods for different activities in the software development lifecycle. Quality assurance of software is primarily done by means of testing, an activity that faces constraints of both time and resources. Hence, there is need to test effectively within the constraints in order to maximize throughput i.e. rate of fault detection, coverage, etc...
Software products released into the field typically have some number of residual defects that either were not detected or could not have been detected during testing. This may be the result of flaws in the test cases themselves, incorrect assumptions made during the creation of test cases, or the infeasibility of testing the sheer number of possible configurations for a complex system; these defects...
Risk-based testing is an approach that consists of a set of activities regarding risk factors identification related to software requirements. Once identified, the risks are prioritized according to its likelihood and impact, and the test cases are projected based on the strategies for treatment of the identified risk factors. Then, test efforts are continuously adjusted according the risk monitoring...
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