Gartner defines AI-augmented software testing tools as enablers of continuous, self-optimizing and adaptive automated testing through the use of AI technologies. The capabilities run the gamut of the testing life cycle including test scenario and test case generation, test automation generation, test suite optimization and prioritization, test analysis and defect prediction as well as test effort estimation and decision making. These tools help software engineering teams to increase test coverage, test efficacy and robustness. They assist humans in their testing efforts and reduce the need for human intervention in the different phases of testing.
'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 a variety of 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 uses of integration technology: Data consistency: The ability to monitor for or be notified by applications, services and data sources about changes, and to propagate those changes to the appropriate applications and data destinations (for example, “synchronize customer data” or “ingest into data lake”). Multistep process: The ability to implement multistep processes between applications, services and data sources (for example, to “onboard employee” or “process insurance claim”). Composite service: The ability to create composite services exposed as APIs or events and composed from existing applications, services and data sources (for example, to create a “credit check” service or to create a “generate fraud score” service). These integration processes, data pipelines, workflows, automations and composite services are most commonly created 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 enabling automated extraction of data from multiple formats and varying 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 numbers of documents, the content of which is designed for people to comprehend rather than machines to process. 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.
Process mining platforms are designed to discover, monitor and improve processes by extracting knowledge from events captured in information systems to continuously deliver visibility and insights. Process mining 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 by advanced process analytics, process improvement detection and process improvement recommendations, partly driven by AI and generative AI (GenAI).
Gartner defines robotic process automation (RPA) as the software to automate tasks within business and IT processes via 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 by 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. RPA is used across numerous business functions for tactical task automation. Business and IT users can leverage RPA to: Move data in or out of application systems without human interaction (unattended automation). Scripts are designed to replicate the actions of a human interacting with those systems or documents, which usually do not have available APIs. The goal is to automate and complete a task successfully without human intervention. Typically, unattended automation is triggered by a system and bots executed on a server. Automate tasks with a human in the loop (attended automation). RPA can extract information from systems and related documents, shaping it and preparing it for consumption by a human at the point of need. Typically, attended automation is triggered by a human and bots executed on a local device.
Task mining is a technique by which enterprises can infer meaningful information by scraping desktop-level event data. This data may be from individual users or a cohort of individuals (e.g., in a call center) and takes the form of screen recordings, keystrokes, mouse clicks and data entries. Additional mining capabilities interpret the data by applying natural language processing and optical character recognition to correlate data in different ways. Task mining helps an enterprise identify inefficiencies and automation or AI potential, improve task execution and enhance the employee experience.