Black-box tree test case generation through diversity

To identify defects and security risks in many real-world applications structured test cases, including test cases structured as trees are required. A simple approach is to generate random trees as test cases [random testing (RT)]; however, the RT approach is not very effective. In this work, we inv...

Full description

Saved in:
Bibliographic Details
Published in:Automated software engineering 2018-09, Vol.25 (3), p.531-568
Main Authors: Shahbazi, Ali, Panahandeh, Mahsa, Miller, James
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Black boxes
Computer Science
Multiple objective analysis
Optimization algorithms
Optimization techniques
Software Engineering/Programming and Operating Systems
Trees
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To identify defects and security risks in many real-world applications structured test cases, including test cases structured as trees are required. A simple approach is to generate random trees as test cases [random testing (RT)]; however, the RT approach is not very effective. In this work, we investigate and extend the black-box tree test case generation approaches. We introduce a novel model to produce superior test case generation based around the idea of measuring the diversity of a tree test set. This initial approach is further extended by adding a second model which describes the distribution of tree sizes. Both models are realized via a multi-objective optimization algorithm. An empirical study is performed with four real-world programs indicating that the generated tree test cases outperform test cases generated by other methods.
ISSN:0928-8910
1573-7535
DOI:10.1007/s10515-018-0232-y