Some machine learning applications are intended to learn properties of data sets where the correct answers are not already known to human users. It is challenging to test such ML ...
Some Statistical Software Testing approaches rely on sampling the feasible paths in the control flow graph of the program; the difficulty comes from the tiny ratio of feasible p...
Structural Statistical Software Testing (SSST) exploits the control flow graph of the program being tested to construct test cases. Specifically, SSST exploits the feasible paths...
It is challenging to test applications and functions for which the correct output for arbitrary input cannot be known in advance, e.g. some computational science or machine learni...
Although many of the software engineering activities can now be model-supported, the model is often missing in software development. We are interested in retrieving statemachine m...