The growing complexity of current software systems has intensified the need for sound and safedevelopment practices. Conventional quality assurance (QA) tends to be ill-equipped to copewith the fast-changing threat vectors, so it ends up with the vulnerability being unidentified andtaking too long to fix. Artificial Intelligence (AI) has a disruptive potential due to theautomatization of key processes of the Software Development Lifecycle (SDLC), which resultsin the increase of efficiency and security. Computer-assisted QA uses machine learning modelsand intelligent automation to conduct the code analysis (static and dynamic), anomaly detection,and prioritization of vulnerabilities in real-time. The process will reinforce continuousintegration and deployment (CI/CD) pipelines, allowing the proactive process of risk modelingand proactive security. In addition, AI-based test case generation and optimization minimizeshuman error, increases coverage and speed. Although these advantages exist, issues like dataquality, transparency of algorithms, and trust of the recommendations provided by AI have to bemitigated in order to guarantee the credibility of the results. With the introduction of AI to theQA processes, organizations are able to have safer, more resilient and cheaper softwaredevelopment lifecycle cycles besides being in compliance with the standards of security.
Mojisola Aderonke Ojuri (Sat,) studied this question.
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