The adoption of Artificial Intelligence (AI) and Machine Learning (ML) techniques has proliferated every aspect of the SDLC (Software Development Lifecycle). Speaking of the software testing process, too, this is true. Traditional software testing requires the manual creation and execution of test cases to identify defects and issues and then ensure those are fixed before the software is finally shipped. With AI-enabled testing, software quality analysis aficionados have embraced intelligent automation, predictive qualities, and data analysis capabilities. This has improved the efficiency and the general efficacy of software quality testing.
Evolution of AI-enabled software testing
If we study the evolution of AI-enabled software testing, we find that the period of growth can be captured in three pivotal phases:
Early adoption (2010-2015): AI usage was chiefly for test automation. AI-powered tools were adopted early on to automate tasks known to be repetitive and time-consuming. Testers used innovative technology to automate the generation of test cases and further execution. Efficiency improved, and when viewed from a fundamental position, there was no change in how the software testing process was imagined.
Example: Organizations such as Facebook and Microsoft started using AI-powered tools to automate the generation of test cases for their software products. These tools could be used to create a wide range of test scenarios by analyzing code, identifying potential execution paths, and generating test cases covering different conditions. This increased test efficiency by automating the repetitive and time-consuming process of test case creation.
Growth stage (2015-2020): At this time, AI use was for more sophisticated tasks and not just automation of repetitive tasks. Testers began to use AI for defect detection and analysis-related tasks. AI had developed to be leveraged to generate test cases that could find defects. Consequently, software quality improved vastly.
Example: Companies like Google integrated AI algorithms into their testing processes to detect defects in software more effectively. These AI-powered tools could analyze vast amounts of data, code changes, user feedback, logs to identify patterns that lead to the discovery of potential defects – helping testers make amends.
Universality and general use (2020-current date): AI is becoming all-pervasive in this stage of its growth. AI tools are widely used for a wide variety of testing-related tasks. In this stage, AI has transformed how software testing is approached, totally reimagining the landscape, and building efficiency and agility.
Example:
Entertainment software companies have embraced AI-driven continuous testing as part of their IT efforts to continuously monitor performance and functionality of software in real-world scenarios. It can simulate user interactions, monitor system behavior, and trigger alerts if anomalies are detected, so that timely corrective actions can be taken.
Critical Trends in AI-enabled software testing
AI-enabled software testing has continuously evolved since its inception with the proliferation of modern technology, techniques, and processes. The key trends in AI-enabled software testing that have garnered remarkable attention in recent years are as follows:
Growing utilization of machine learning: Machine learning algorithms have enabled AI-enabled testing to learn from historical test data to identify patterns, predict issues, and offer helpful diagnostic tools. Machine learning techniques such as code generation and auto-completion allow developers to rely on suggestions like code snippets, auto-completing code, and generation of entire functions based on the context. Further, machine learning algorithms help developers to identify code patterns that may cause bugs, making code more reliable. Moreover, Natural Language Processing (NLP)-powered systems can extract and understand requirements from natural language documents. This builds efficiencies and bridges the gap between non-technical stakeholders and technical staff. Finally, machine learning assists in code review tasks by identifying potential issues and assessing code quality based on accepted best practices. Machine learning also predicts when maintenance will be required by studying historical data.
The increased use of machine intelligent automation: Intelligent automation leverages AI to automate repetitive and time-consuming testing tasks. These tools can learn swiftly from human tests and comprehend complex user interactions.
AI + DevOps paradigm: The software testing process has benefitted from the convergence of AI and DevOps, enhancing and optimizing the DevOps paradigm. AI and DevOps combine continuous testing, test automation, test case prioritization, predictive analytics, log analysis and monitoring, performance testing, and A/B testing. In terms of the benefits of Converging AI and DevOps in Testing, there are several. Faster feedback, increased test coverage efficiency, and improved resource optimization are some of the clinching merits of this convergence and the paradigm shift.
Reimagining healthcare IT software testing
Healthcare IT software testing can be reimagined by leveraging AI-enabled testing. The benefits are clear – like accuracy, security, and reliability. The following are two real-world use cases that demonstrate how the integration of AI into testing within the healthcare IT landscape:
- Using AI to automate testing of CDS systems
IBM Watson Health (now known as Merative L.P.) has leveraged AI to automate CDS (clinical decision support) systems. These systems are utilized by healthcare providers to make better-informed decisions about patient care. Through AI-powered testing, IBM Watson Health can automatically generate test cases, execute them, and detect bugs. Consequently, AI-powered testing helps to reduce time and cost related to CDS testing. This ensures that these systems are providing accurate and dependable guidance to healthcare providers.
- Using AI to test EHR
Cerner uses AI to test its EHR (electronic health record) software used by healthcare providers to store and manage patients’ medical records. Cerner’s AI-powered testing platform can automatically generate test cases that simulate real-world clinical workflows. This ensures that EHR software is up to date with the dynamic needs of healthcare providers. Further, Cerner’s AI platform can also identify potential defects in EHR software by analyzing the software’s code and user interface. This helps to detect defects and eliminate them. In addition, the AI platform can generate detailed test reports that identify potential defects and provide recommendations for remediation. This offers a head start, helping healthcare organizations to prioritize their testing efforts.
HealthAsyst: Eye on technology + rich domain experience
At HealthAsyst, our technologists keep a keen eye on evolving paradigms in the software industry. Especially regarding software in the healthcare industry, this ensures that our clients are always on top of the game. If you have any concerns or questions about your product engineering or software testing and maintenance needs, please write to us at itservices@healthasyst.com.