Introducing random mutant-selection #31
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…lection: selecting a fixed number of mutants, and selecting a percentage of all the mutants. All of this with some tests
…m and percentage random, with tests
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Implemented another type of random selection. |
| 1 to: numberOfMutants do: [ :i | | ||
| operator := operators at: | ||
| (random nextIntegerBetween: 1 and: operators size). | ||
| [ (dict at: operator) allSatisfy: [ :each | newColl includes: each ] ] |
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Nice check! but it would be nice it comes with a comment :)
It's important to understand that if you already selected all the mutants in an operator, then you need to filter the operator out, otherwise this will loop forever looking for mutants (and they are all used)
| whileTrue: [ | ||
| operator := operators at: | ||
| (random nextIntegerBetween: 1 and: operators size) ]. | ||
| size := (dict at: operator) size. |
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This looks like the same code as above. How can we enhance it a bit and avoid the repeated code?
| size := aCollection size. | ||
| newColl := aCollection copyEmpty. | ||
| random := Random new. | ||
| numberOfMutants := size * percentageOfMutants / 100. |
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Cool, so you transformed a percentage into an absolute number of mutants.
Now you can reuse the code from the other class!! How could you do it? :)
| dict := aCollection groupedBy: [ :e | e operator ]. | ||
| operators := dict keys. | ||
| 1 to: numberOfMutants do: [ :i | | ||
| operator := operators at: |
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I did some refactoring.
An example: |
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Better! I think there are still duplications we can remove and simplify, let's discuss them later.
Thanks @DurieuxPol !
Added a random mutant-selection decorator. It decorates a mutations-generation strategy and it randomly selects a mutant among all mutants generated.
Also added two types of concrete random mutant-selectors.
The first one randomly selects a fixed number of distinct mutants (
FixedRandomMutantSelection), while the second one selects a percentage of them (PercentRandomMutantSelection).An example of how to use it:
