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TensorMutation: Learning Weight-Space Mutations of Deep Learning Models on Neural Graphs
Zaheed Ahmed, Philip Makedonski, Jens Grabowski
Mutation testing for deep learning (DL) faces a cost-realism trade-off. The pre-training approach produces realistic but computationally expensive mutants, whereas existing post-tr…
Quality-Driven Selective Mutation for Deep Learning
Zaheed Ahmed, Emmanuel Charleson Dapaah, Philip Makedonski +1
Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better guidance for test improvement, wh…
An Empirical Study of the Realism of Mutants in Deep Learning
Zaheed Ahmed, Philip Makedonski, Jens Grabowski
The core assumption behind using mutants as substitutes for real faults is that they represent them faithfully. In traditional software testing, this representativeness has been em…
A new perspective on the competent programmer hypothesis through the reproduction of bugs with repeated mutations
Zaheed Ahmed, Eike Stein, Steffen Herbold +2
The competent programmer hypothesis states that most programmers are competent enough to create correct or almost correct source code. Because this implies that bugs should usually…