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The purpose of this paper is to investigate the relationships between some aspect‐oriented metrics and aspect fault proneness, content and fixing effort.

An exploratory case study was conducted using an open source aspect‐oriented software consisting of 76 aspects, and 13 aspect‐oriented metrics were investigated that measure different structural properties of an aspect: size, coupling, cohesion, and inheritance. In addition, different prediction models for aspect fault proneness, content and fixing effort were built using different combinations of metrics' categories.

The results obtained from this study indicate statistically significant correlation between most of the size metrics and aspect fault proneness, content and fixing effort. The cohesion metric was also found to be significantly correlated with the same. Moreover, it was observed that the best accuracy in aspect fault proneness, content and fixing effort prediction can be achieved as a function of some size metrics.

Fault prediction helps software developers to focus their quality assurance activities and to allocate the needed resources for these activities more effectively and efficiently; thus improving software reliability. In literature, some aspect‐oriented metrics have been evaluated for aspect fault proneness prediction, but not for other fault‐related prediction problems such as aspect fault content and fixing effort.

Effective and efficient software security inspection is crucial as the existence of vulnerabilities represents severe risks to software users. The purpose of this paper is to empirically evaluate the potential application of Stochastic Gradient Boosting Trees (SGBT) as a novel model for enhanced prediction of vulnerable Web components compared to common, popular and recent machine learning models.

An empirical study was conducted where the SGBT and 16 other prediction models have been trained, optimized and cross validated using vulnerability data sets from multiple versions of two open-source Web applications written in PHP. The prediction performance of these models have been evaluated and compared based on accuracy, precision, recall and F-measure.

The results indicate that the SGBT models offer improved prediction over the other 16 models and thus are more effective and reliable in predicting vulnerable Web components.

This paper proposed a novel application of SGBT for enhanced prediction of vulnerable Web components and showed its effectiveness.