  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).
