Software test amplification is the act of strength-ening manually written test-cases to exercise the boundary conditions of the system under test. It has been demonstrated by the research community to work for the programming language Java, relying on the static type system to safely transform the code under test. In dynamically typed languages, such type decla-rations are not available, and as a consequence test amplification has yet to find its way to programming languages like Smalltalk, Python, Ruby and Javascript. The AnSyMo research group has created two proof of concept tools for languages without a static type system: AmPyfier (for Python) and Small-Amp (for Pharo-Smalltalk). In this tool demonstration paper we explain how we relied on profiling libraries present in the respective eco-systems to infer the necessary type information for enabling full-blown test amplification.

Abstract Test amplification aims to automatically improve a test suite. One technique generates new test methods through transformations of the original tests. These test amplification tools heavily rely on analysis techniques that benefit a lot from type declarations present in the source code of projects written in statically typed languages. In dynamically typed languages, such type declarations are not available, and therefore, research regarding test amplification for those languages is sparse. Recent work has brought test amplification to the dynamically typed language Pharo Smalltalk by introducing the concept of dynamic type profiling. The technique is dependent on Pharo‐specific frameworks and has not yet been generalized to other languages. Another significant downside in test amplification tools based on the mutation score of a test suite is their high time cost. In this paper, we present AmPyfier, a tool that brings test amplification and type profiling to the dynamically typed language Python. AmPyfier introduces multi‐metric selection in order to increase the time efficiency of test amplification. We evaluated AmPyfier on 11 open‐source projects and found that AmPyfier could strengthen 37 out of 54 test classes. Multi‐metric selection decreased the time cost ranging from 17% to 98% as opposed to selection based on the full mutation score.