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How to use generate_mutations_from method in hypothesis

Best Python code snippet using hypothesis

engine.py

Source:engine.py

...633            else:634                max_length = BUFFER_SIZE635            data = self.new_conjecture_data(prefix=prefix, max_length=max_length)636            self.test_function(data)637            self.generate_mutations_from(data)638            # Although the optimisations are logically a distinct phase, we639            # actually normally run them as part of example generation. The640            # reason for this is that we cannot guarantee that optimisation641            # actually exhausts our budget: It might finish running and we642            # discover that actually we still could run a bunch more test cases643            # if we want.644            if (645                self.valid_examples >= max(small_example_cap, optimise_at)646                and not ran_optimisations647            ):648                ran_optimisations = True649                self.optimise_targets()650    def generate_mutations_from(self, data):651        # A thing that is often useful but rarely happens by accident is652        # to generate the same value at multiple different points in the653        # test case.654        #655        # Rather than make this the responsibility of individual strategies656        # we implement a small mutator that just takes parts of the test657        # case with the same label and tries replacing one of them with a658        # copy of the other and tries running it. If we've made a good659        # guess about what to put where, this will run a similar generated660        # test case with more duplication.661        if (662            # An OVERRUN doesn't have enough information about the test663            # case to mutate, so we just skip those.664            data.status >= Status.INVALID...

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