Gartner defines AI-augmented software testing tools as tools that provide fully integrated and orchestrated capabilities to enable continuous, self-optimizing and highly autonomous testing in the software development life cycle (SDLC) through the use of AI. Capabilities include the generation and maintenance of test scenarios, test cases, test automation, test suite optimization, test prioritization, test analysis, and test value scoring. As part of the larger toolset for AI-augmented development that aids software engineers in designing, coding and testing applications, AI-augmented software testing tools integrate with AI code assistants, chat interfaces, DevOps platforms, planning and deployment tools. They are delivered primarily as cloud-hosted services with some options for on-premises deployment. AI-augmented software testing tools are designed to simplify and accelerate the creation, maintenance and management of test artifacts throughout the SDLC. They help software engineering teams to increase the efficiency, effectiveness and fidelity of tests by reducing human intervention. Teams can build confidence in the quality of their release candidates and support software engineering leaders in making informed decisions regarding product releases.
Gartner defines adversarial exposure validation (AEV) as technologies that deliver consistent, continuous and automated evidence of the feasibility of an attack. These technologies confirm how potential attack techniques would successfully exploit an organization and circumvent prevention and detection security controls. They achieve this by performing attack scenarios and modeling or measuring the outcome to prove the existence and exploitability of exposures. AEV is generally delivered as a SaaS solution with or without on-premises agents. AEV as a market category replaces breach and attack simulation (BAS) and automated penetration testing and red teaming technology from the 2023 Gartner Hype Cycle (Hype Cycle for Security Operations, 2023). AEV technologies provide automated execution of both simplified and/or extensible attack scenarios. Results data from an executed attack scenario is used for various outcomes, such as: validating a theoretical exposure as real, automating frequent controls testing, improving preventive security posture or improving detection and response capabilities.
IT Security refers to products and services that protect digital systems and data from cyber threats and unauthorized access. This category includes markets that focus on network security, identity management, data protection, and cloud security, enabling organizations to reduce risk, ensure compliance, and operate securely in a digital world.
Performance testing tools establish metrics for application throughput, latency and resource consumption, enabling teams to compare results across different releases or configurations. Performance testing enables delivery teams to quickly experiment and analyze performance, guiding future development and derisking upgrades. By simulating concurrent users and transactions, these tools help pinpoint performance bottlenecks in the application stack. These tools assess how an application behaves as user load increases, ensuring that it can scale without degradation in service quality. Stress tests performed by these tools verify that applications remain stable and reliable over extended periods and under peak load conditions. Test results inform infrastructure and software architecture decisions, helping organizations meet anticipated demand.