Constrained adversarial learning for automated software testing: a literature review

Abstract

It is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing can be a promising solution to quickly analyze many lines of code and detect vulnerabilities and possible attack vectors by generating function-specific testing data. This process draws similarities to the constrained adversarial examples generated by adversarial learning methods, so there could be significant benefits to the integration of these methods in testing tools. Therefore, this literature review is focused on the current state-of-the-art of constrained data generation methods applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance software testing tools with adversarial testing methods and improve the resilience and robustness of their information systems. The found constrained data generation applications were systematized, and the advantages and limitations of approaches specific for white-box, grey-box, and black-box testing were analyzed, identifying research gaps and opportunities to improve automated testing tools with data generated by adversarial attacks.