Abstract:
Vulnerability prediction is one of the critical issues
for researchers in the software industry. Technically, a vulnerability
predictor is a machine learning model trained to identify
vulnerable and non-vulnerable modules. Recent studies have
shown that the performance of these models can be affected when
the default parameter settings are used. Unfortunately, most
studies in literature present their results using the default parameter
settings. This study investigates the extent to which
parameter optimization affect the performance of vulnerability
prediction models. To evaluate our procedure, we conducted an
empirical study on three open-source vulnerability datasets,
namely Drupal, Moodle and PHPMyAdmin using five machine
learning algorithms. Surprisingly, we found that in all cases of
the 3 datasets studied, our models provided a significant increase
in precision and accuracy against the benchmark study. In conclusion,
software engineers can use the results obtained from this
study when building data miners for identifying vulnerable modules.