The week's pick
Reseach Article
Symbolic Execution-based Code Coverage Framework for Augmented Software Testing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 37 |
| Year of Publication: 2024 |
| Authors: Rachel Glockenmeier, Varghese Vaidyan, Yong Wang |
10.5120/ijca2024923951
|
Rachel Glockenmeier, Varghese Vaidyan, Yong Wang . Symbolic Execution-based Code Coverage Framework for Augmented Software Testing. International Journal of Computer Applications. 186, 37 ( Sep 2024), 1-7. DOI=10.5120/ijca2024923951
Abstract
Although extensive research has been done on automated software comprehension, no analysis framework exists that is free of limitations and constraints to address the challenge of comprehending software without manually investigating it. This introduces several challenges. One important task to software researchers is to identify all the sets of paths inside a target program. Answering this will offer further information about the target program and allow for understanding, and ultimately further security and quality analysis. Introducing a comprehensive framework for code coverage analysis, a notable gap in the existing works, is addressed by offering empirical evidence and an analysis framework to evaluate the impacts of enhancements to symbolic execution techniques in target programs. Using angr as the symbolic execution engine, several code exploration approaches based on prior research and angr’s capabilities are implemented. To analyze the implications of these changes on code coverage, the proposed approach performs a comparative investigation over a wide variety of binary programs, accounting for varying complexity levels and memory restrictions. The experimental findings show a wide coverage range ranging from 0.3185% to 16.7093%, depending on the testing circumstances. By developing a benchmark for code coverage under symbolic execution, the framework not only elucidates the interaction of testing variables, but also offers a full analytical framework for assessing coverage expectations in respective contexts.
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