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Using Source Code Metrics for Predicting Metamorphic Relations at Method Level
- Source :
- 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
- Publication Year :
- 2022
-
Abstract
- Metamorphic testing (TM) examines the relations between inputs and outputs of test runs. These relations are known as metamorphic relations (MR). Currently, MRs are handpicked and require in-depth knowledge of the System Under Test (SUT), as well as its problem domain. As a result, the identification and selection of high-quality MRs is a challenge. \citeauthor{PMR1} suggested the Predicting Metamorphic Relations (PMR) approach for automatic prediction of applicable MRs picked from a predefined list. PMR is based on a Support Vector Machine (SVM) model using features derived from the Control Flow Graphs (CFGs) of 100 Java methods. The original study of \citeauthor{PMR1} showed encouraging results, but developing classification models from CFG-related features is costly. In this paper, we aim at developing a PMR approach that is less costly without losing performance. We complement the original PMR approach by considering other than CFG-related features. We define 21 features that can be directly extracted from source code and build several classifiers, including SVM models. Our results indicate that using the original CFG-based method-level features, in particular for a SVM with random walk kernel (RWK), achieve better predictions in terms of AUC-ROC for most of the candidate MRs than our models. However, for one of the candidate MRs, using source code features achieved the best AUC-ROC result (greater than 0.8).<br />Comment: 8 pages, 6 tables, 3 figures
- Subjects :
- Computer Science - Software Engineering
D.2.5
Subjects
Details
- Database :
- arXiv
- Journal :
- 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
- Publication Type :
- Report
- Accession number :
- edsarx.2205.15835
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/SANER53432.2022.0013