Biblio

The increasingly intensive use of software systems in diverse sectors, especially in business, government, healthcare, and critical infrastructures, makes it essential to deliver code that is secure. In this work, we present two sets of experiments aiming at helping developers to improve software security from the early development stages. The first experiment is focused on using software metrics to build prediction models to distinguish vulnerable from non-vulnerable code. The second experiment studies the hypothesis of developing a consensus-based decision-making approach on top of several machine learning-based prediction models, trained using software metrics data to categorize code units with respect to their security. Such categories suggest a priority (ranking) of software code units based on the potential existence of security vulnerabilities. Results show that software metrics do not constitute sufficient evidence of security issues and cannot effectively be used to build a prediction model to distinguish vulnerable from non-vulnerable code. However, with a consensus-based decision-making approach, it is possible to classify code units from a security perspective, which allows developers to decide (considering the criticality of the system under development and the available resources) which parts of the software should be the focal point for the detection and removal of security vulnerabilities.

An effective way for building secure software is to embed security into software in the early stages of software development. Thus, we aim to study several evidences of code anomalies introduced during the software development phase, that may be indicators of security issues in software, such as code smells, structural complexity represented by diverse software metrics, the issues detected by static code analysers, and finally missing security best practices. To use such evidences for vulnerability prediction and removal, we first need to understand how they are correlated with security issues. Then, we need to discover how these imperfect raw data can be integrated to achieve a reliable, accurate and valuable decision about a portion of code. Finally, we need to construct a security actuator providing suggestions to the developers to remove or fix the detected issues from the code. All of these will lead to the construction of a framework, including security monitoring, security analyzer, and security actuator platforms, that are necessary for a security-aware integrated development environment (SIDE).