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Test Case Generation for Javascript with Unsupervised Probabilistic Type Inference

Conference paper (2022)

Authors

D.M. Stallenberg Software Engineering - EEMCS
Mitchell Olsthoorn Software Engineering - EEMCS
A. Panichella Software Engineering - EEMCS
Research Group
Software Engineering (EEMCS) (TU Delft)
Copyright: © 2022 D.M. Stallenberg, Mitchell Olsthoorn, A. Panichella
DOI: https://doi.org/10.1007/978-3-031-21251-2_5
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Software Engineering

Abstract

Search-based test case generation approaches make use of static type information to determine which data types should be used for the creation of new test cases. Dynamically typed languages like JavaScript, however, do not have this type information. In this paper, we propose an unsupervised probabilistic type inference approach to infer data types within the test case generation process. We evaluated the proposed approach on a benchmark of 98~units under test (i.e., exported classes and functions) compared to random type sampling w.r.t. branch coverage. Our results show that our type inference approach achieves a statistically significant increase in 56% of the test files with up to 71% of branch coverage compared to the baseline.

Files

978_3_031_21251_2_5.pdf
(pdf | 0.539 Mb)
- Embargo expired in 15-05-2023
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