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.
'Application integration platforms enable independently designed applications, apps and services to work together. Key capabilities of application integration technologies include: • Communication functionality that reliably moves messages/data among endpoints. • Support for fundamental web and web services standards. • Functionality that dynamically binds consumer and provider endpoints. • Message validation, mapping, transformation and enrichment. • Orchestration. • Support for multiple interaction patterns, content-based routing and typed messages.
Generative AI (GenAI) model providers focus on developing and providing generative AI technologies and make them available to other developers, businesses and general public through APIs or commercial licenses. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. This layer of vendors offers access to commercial or open-source foundation models such as LLMs and other types of generative algorithms (such as GANs, genetic/evolutionary algorithms or simulations). These models can be provided for developers to embed into their applications or be used as base models for fine-tuning customized models for their software offerings or internal enterprise use cases. This helps businesses gain the benefits of advanced generative AI technologies while avoiding the high costs, expertise requirements and time needed to develop these technologies in-house. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.
Gartner defines integration platform as a service (iPaaS) as a vendor-managed cloud service that enables end users to implement integrations between applications, services and data sources, both internal and external to their organization. iPaaS enables end users of the platform to integrate a variety of internal and external applications, services and data sources for at least one of the three main patterns of integration technology use: data consistency, multistep process and composite services. These integration use cases are most commonly implemented via intuitive low-code or no-code developer environments, though some vendors provide more complex developer tooling.
Gartner defines intelligent document processing (IDP) solutions as specialized data integration tools that enable automated extraction of data from multiple formats and various layouts of document content. IDP solutions ingest data for dependent applications and workflows and can be provided as a software product and/or as a service. Organizations receive and process documents in multiple formats to enable activities such as onboarding new suppliers, receiving applications for loans or insurance claims. This results in large volumes of documents, the content of which is designed for human comprehension rather than machine processing. Extracting data from content is essential for document processing and the automated activities this supports. IDP solutions fulfill this role, augmented by and potentially replacing people. Documents are received in physical form, typically paper, which must be scanned for digitization, or in digital form, such as emails and PDFs. The content of these documents has varying layouts, ranging from structured formats, such as tabular or outline (e.g., list or hierarchy of headings) or invoices or contracts, to unstructured formats (i.e., free-flowing, such as an email). Layouts that fall between structured and unstructured, or mixing the two, are often referred to as semistructured.
Process mining platforms offer comprehensive analysis of end-to-end processes by extracting event data from information systems. This includes automated process discovery (extracting process models from an event log), conformance checking (monitoring deviations by comparing model and log), social network/organizational mining, automated construction of simulation models, model extension, model repair, case prediction and history-based recommendations. Process mining platforms extend process mining capabilities via advanced process analytics, process improvement detection and process improvement recommendations.
Gartner defines robotic process automation (RPA) as software that automates tasks within business and IT processes using software scripts that emulate human interaction with the application UI. RPA enables a manual task to be recorded or programmed into a software script, which users can develop through programming or by using the RPA platform’s low-code and no-code GUIs. This script can then be deployed and executed into different runtimes. The runtime executable of the deployed script is referred to as a bot or robot.
Gartner defines task mining as a combination of techniques to infer useful information from low-level event data available in UI logs derived from the underlying operating system or through observing application UI interactions. This data comes from individual users or a cohort in the form of screen recordings, keystrokes, mouse clicks and data entries. Additional mining capabilities interpret the data by applying natural language processing (NLP), optical character recognition (OCR) and artificial intelligence (AI) techniques to correlate data in different ways. Task mining helps an enterprise identify inefficiencies and automation opportunities, increase worker productivity, and enhance the employee experience.