AI-augmented, software-testing tools provide capabilities for advanced, self-optimizing and adaptive automated testing through the use of AI technologies, such as machine learning (ML), self-healing heuristics or computer vision. They assist humans in their testing efforts and reduce the need for human intervention in the following areas: Test case and test data generation Test suite optimization and coverage detection Test efficacy and robustness Test analysis and defect prediction Test effort estimation and decision making AI-augmented testing tools streamline, accelerate and improve the test workflow, as models can be retrained based on the data collected from the activities they perform — thereby enhancing human productivity.
The application development life cycle management (ADLM) tool market focuses on the planning and governance activities of the software development life cycle (SDLC). ADLM products focus on the 'development' portion of an application's life. Key elements of an ADLM solution include: software requirements definition and management, software change and configuration management, software project planning, with a current focus on agile planning, work item management, quality management, including defect management. Other key capabilities include: reporting, workflow, integration to version management, support for wikis and collaboration, strong facilities for integration to other ADLM tools.
Reviews for 'Application Development, Integration and Management - Others'
Elevating Test Data Management for DevOps is the process of providing DevOps teams with test data to evaluate the performance, and functionality of applications. This process typically includes copying production data, anonymization or masking, and, sometimes, virtualization. In some cases, specialized techniques, such as synthetic data generation, are appropriate. With that, it applies data masking techniques to protect sensitive data, including PII, PHI, PCI, and other corporate confidential information, from fraud and unauthorized access while preserving contextual meaning.
Load Testing Tools determine the performance of a system, software product, or software application under real-life based load conditions and resource utilization levels. The goal of load testing is to improve performance bottlenecks and to ensure stability and smooth functioning of software application before deployment. Through specialized testing software,various scenarios are simulated to test the system’s behavior under different load conditions. The software places a simulated “load” or demand from multiple sources on applications to ensure it remains stable during operation and peak load. It enables test analysts to evaluate application performance and maximize the operating capacity of the application.