  Some test amplification tools extend a manually created test suite with
additional test cases to increase the code coverage. The technique is
effective, in the sense that it suggests strong and understandable test cases,
generally adopted by software engineers. Unfortunately, the current
state-of-the-art for test amplification heavily relies on program analysis
techniques which benefit a lot from explicit type declarations present in
statically typed languages. In dynamically typed languages, such type
declarations are not available and as a consequence test amplification has yet
to find its way to programming languages like Smalltalk, Python, Ruby and
Javascript. We propose to exploit profiling information --readily obtainable by
executing the associated test suite-- to infer the necessary type information
creating special test inputs with corresponding assertions. We evaluated this
approach on 52 selected test classes from 13 mature projects in the Pharo
ecosystem containing approximately 400 test methods. We show the improvement in
killing new mutants and mutation coverage at least in 28 out of 52 test classes
(53%). Moreover, these generated tests are understandable by humans: 8 out of
11 pull-requests submitted were merged into the main code base (72%). These
results are comparable to the state-of-the-art, hence we conclude that test
amplification is feasible for dynamically typed languages.
