Gartner defines AI code assistants as tools that assist in generating and analyzing software code and configuration. The assistants use foundation models such as large language models (LLMs) that have been optionally fine-tuned for code, or program-understanding technologies, or a combination of both. Software developers prompt the code assistants to generate, analyze, debug, fix, and refactor code, to create documentation, and to translate code between languages. Code assistants integrate into developer tools like code editors, command-line terminals and chat interfaces. Some can be customized to an organization’s specific codebase and documentation. AI code assistants can enhance a software developer’s experience by boosting efficiency, accelerating application development, minimizing cognitive overload, amplifying problem-solving skills, accelerating learning pace, fostering creativity and maintaining state of flow.
Reviews for 'Application Development, Integration and Management - Others'
Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling. These platforms support the independent use and collaboration among data scientists and their business and IT counterparts, with automation and AI assistance through all stages of the data science life cycle, including business understanding, data access and preparation, model creation and sharing of insights. They also support engineering workflows, including the creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances or as a fully managed cloud offering.
Gartner defines DevOps platforms as those that provide fully integrated capabilities to enable continuous delivery of software using Agile and DevOps practices. The capabilities span the development and delivery life cycle built around the continuous integration/continuous delivery (CI/CD) pipeline and include aspects such as versioning, testing, security, documentation and compliance. DevOps platforms support team collaboration, consistency, tool simplification and measurement of software delivery metrics. DevOps platforms simplify the creation, maintenance and management of the components required for the delivery of modern software applications. Platforms create common workflows and data models, simplify user access, and provide a consistent user experience (UX) to reduce cognitive load. They lead to improved visibility, auditability and traceability into the software development value stream. This end-to-end view encourages a systems-thinking mindset and accelerates feedback loops.
Integrated Development Environment (IDE) software provides a unified interface to write, test, and debug code efficiently. It typically includes a source code editor, compiler or interpreter, debugger, and build tools, all within a graphical user interface (GUI) that simplifies navigation and development workflows. Most IDEs also support features like intelligent code completion, syntax highlighting, version control integration, and project management tools. By offering everything in one place, IDEs reduce setup time, minimize context switching, and help developers detect and fix errors quickly. This results in faster, more organized, and more accurate software development. IDEs are widely used by programmers, software developers, data analysts, and engineers working across various programming languages and platforms, from web and mobile apps to enterprise systems.
The workstream collaboration (WSC) market consists of products that deliver a conversational workspace based on a persistent group chat. Products in this market are primarily used to organize, coordinate, and execute outcome-driven teamwork such as that associated with the project- or process-related activities. Secondary uses can include ad hoc collaboration and community discussions.