Mining Similar Methods for Test Adaptation

doi:10.1109/TSE.2021.3057163

Mining Similar Methods for Test Adaptation

2022-07-01

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DOI：10.1109/TSE.2021.3057163

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Developers may choose to implement a library despite the existence of similar libraries, considering factors such as computational performance, language or platform dependency, accuracy, convenience, and completeness of an API. As a result, GitHub hosts several library projects that have overlaps in their functionalities. These overlaps have been of interest to developers from the perspective of code reuse or the preference of one implementation over the other. Through an empirical study, we explore the extent and nature of existence of these similarities in the library functions. We have further studied whether the similarity of functions across different libraries and their associated test suites can be leveraged to reveal defects in one another. We see scope for effectively using the mining of test suites from the perspective of revealing defects in a program or its documentation. Another noteworthy observation made in the study is that similar functions may exist across libraries implemented in the same language as well as in different languages. Identifying the challenges that lie in building a testing tool, we automate the entire process in Metallicus, a test mining and recommendation tool. Metallicus returns a test suite for the given input of a query function and a template for its test suite. On a dataset of query functions taken from libraries implemented in Java or Python, Metallicus revealed 46 defects.

Keyword:

Libraries

Testing

Tools

Software development management

Python

Documentation

Open source software

Test suites

mining

software testing

function similarity

Mining Similar Methods for Test Adaptation

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