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Some test amplification tools extend a manually created test suite withadditional test cases to increase the code coverage. The technique iseffective, in the sense that it suggests strong and understandable test cases,generally adopted by software engineers. Unfortunately, the currentstate-of-the-art for test amplification heavily relies on program analysistechniques which benefit a lot from explicit type declarations present instatically typed languages. In dynamically typed languages, such typedeclarations are not available and as a consequence test amplification has yetto find its way to programming languages like Smalltalk, Python, Ruby andJavascript. We propose to exploit profiling information --readily obtainable byexecuting the associated test suite-- to infer the necessary type informationcreating special test inputs with corresponding assertions. We evaluated thisapproach on 52 selected test classes from 13 mature projects in the Pharoecosystem containing approximately 400 test methods. We show the improvement inkilling 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 of11 pull-requests submitted were merged into the main code base (72%). Theseresults are comparable to the state-of-the-art, hence we conclude that testamplification is feasible for dynamically typed languages.

Small-Amp: Test Amplification in a Dynamically Typed Language