Share your vision — get an instant tech & budget estimateTell us your vision and get an instant Tech & Budget Estimation

BesTest is designed to streamline the test management process directly within Jira Cloud, enabling users to capture, organize, and trace requirements with ease. Users can clearly define complexity, impact, and significance of requirements, linking them to corresponding test cases for comprehensive traceability. The tool facilitates the complete test case lifecycle, allowing teams to organize their test cases using folders and collections, define detailed execution steps, and link work items seamlessly. With Built-in Behavior-Driven Development (BDD) support using Gherkin syntax, it accommodates both new and experienced testing teams. Real-time progress tracking during test cycles enhances execution efficiency, enabling teams to instantly log defects, monitor results, and generate visual reports that offer insights into quality and coverage. BesTest is crafted by testers for testers, focusing on user experience with minimal context switching, making it accessible even for smaller teams.

The future of software development appears to be a blend of evolving frameworks and the adaptation of automation in programming practices. As we navigate advancements in language paradigms and communication protocols, notable developments are anticipated in the areas of service boundaries and interface definitions. Despite the challenges faced with current frameworks like DropWizard and Spring REST, gRPC stands out as a promising generic definition language that could facilitate better integration methods. The inconsistent performance of OpenAPI's generators poses a concern, placing emphasis on the need for robust testing to validate generated outputs. With an observable trend towards automation in service boundaries and the push for functional programming, developers are encouraged to embrace evolution in their coding practices. Future developments will likely focus heavily on effective testing techniques, especially in the growing context of AI-generated code, ensuring reliability and performance in service applications.

As advancements in artificial intelligence have enhanced the speed of individual developers, the software development process remains encumbered by traditional inefficiencies. The Agentic Software Development Life Cycle (SDLC) proposes a transformative approach, emphasizing the transition from isolated AI assistance to fully automated workflows that maintain human oversight. This model aims to reduce the slow, manual, and fragmented nature of software development, advocating for policy-driven processes that optimize collaboration and enhance productivity. By integrating AI into the core of development workflows, teams can achieve greater efficiency and responsiveness. The Agentic framework encourages continuous integration and deployment, which not only diminishes the time spent on repetitive tasks but also allows for more time dedicated to innovation and quality assurance. This shift is critical for tech leaders looking to maintain competitiveness in an increasingly fast-paced market. For EU and UK B2B tech buyers, adopting the Agentic SDLC can represent a strategic leap forward in their digital transformation journeys, paving the way for scalable solutions that adapt to changing business needs. Companies can leverage these automated processes to enhance their product offerings while ensuring compliance with evolving regulatory standards, thus fostering sustainable growth in a challenging economic environment.

The creation of Grain OS, a sophisticated RISC-V64 operating system, showcases the power of AI-assisted development. Utilizing Cursor Ultra in Auto Mode, the team has successfully produced over 170,000 lines of code, achieving milestones that highlight the efficiency of AI in software engineering. By leveraging autonomous AI agents, this project exemplifies a paradigm shift in coding practices, shifting from traditional methods to an advanced, collaborative model with AI as a tireless partner. Key insights from this endeavor emphasize the importance of well-defined style guides, extensive testing, comprehensive documentation, and immediate refactoring capabilities that ensure quality and maintainability. The unique economics of using AI for development allow for significant cost savings, transforming what could have taken months into a matter of weeks. With continuous iterations and improvements, the project aims for reliability and safety in critical applications, particularly in dispatch systems for emergency services. Looking ahead, the design of Aurora—an open-source iOS alternative—is set to further expand the accessibility of AI-assisted development. By supporting both cloud-based and local inference backends, Aurora exemplifies a commitment to open-source values while ensuring that safety-critical applications benefit from cutting-edge technology. This project serves as a testament to the transformative potential of AI in software development, challenging conventional practices and paving the way for future innovations.