Small-Amp: Test Amplification in a Dynamically Typed Language
Mehrdad Abdi, Henrique Rocha, Serge Demeyer, Alexandre Bergel

TL;DR
This paper introduces a method for test amplification in dynamically typed languages by using profiling data to infer types and generate effective, understandable tests, achieving significant coverage improvements and human acceptance.
Contribution
It presents a novel approach to test amplification in dynamically typed languages using profiling to infer types, enabling effective test generation without explicit type info.
Findings
53% of test classes showed improved mutation coverage
72% of submitted pull requests were merged
Test amplification is feasible for dynamically typed languages
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoftware Engineering Research · Software Testing and Debugging Techniques · Advanced Malware Detection Techniques
