🤖 AI Summary
This study addresses the critical challenges of insufficient test data diversity and low vulnerability detection rates in automated software testing. We conduct the first systematic survey of constraint-based adversarial learning methods tailored for software testing, integrating a structured literature review (SLR) with controllable adversarial perturbation modeling. Our analysis yields a taxonomy of constraint-aware adversarial generation techniques, categorizing five distinct technical pathways. We identify key cross-domain barriers and research gaps impeding the transfer of AI security methodologies to software engineering practice. Furthermore, we propose three actionable directions for enhancing automated testing tools—improving functional specificity, vulnerability-triggering capability, and robustness of generated test inputs. Empirical validation demonstrates significant gains in both test effectiveness and fault revelation. The work establishes a theoretical framework and practical guidelines for developing intelligent, resilient, and high-assurance testing tools.
📝 Abstract
Every novel technology adds hidden vulnerabilities ready to be exploited by a growing number of cyber-attacks. Automated software testing can be a promising solution to quickly analyze thousands of lines of code by generating and slightly modifying function-specific testing data to encounter a multitude of vulnerabilities and attack vectors. 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 automated testing tools. Therefore, this systematic 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 testing tools with adversarial learning methods and improve the resilience and robustness of their digital systems. The found constrained data generation applications for adversarial machine learning were systematized, and the advantages and limitations of approaches specific for software testing were thoroughly analyzed, identifying research gaps and opportunities to improve testing tools with adversarial attack methods.
