AI Impact in Software Quality Assurance
Software QA needs to evolve as users evaluate software more on experience than on functionality. To meet user expectations, software quality assurance teams need to strengthen their methods and processes as well as empower them with the latest technologies. Artificial Intelligence (AI) is the next step of evolution in software QA.
In the Software industry, automation has been widely adopted to expedite software testing and ensure high quality and desired user experience. However, as the software products become more complex and the volume of testing increases, software quality assurance needs to move to the next level to deliver a better customer experience and a faster time to market.
Introduction of AI into Software Quality Assurance
Many companies have begun using AI based testing tools, even though the process of AI is still relatively new. By introducing AI into software QA, we can not only expand the scope of testing but can also automate many activities to expedite the testing process. This can significantly reduce the time and other resources without sacrificing scope or quality. This will help ease the process of performing high-level quality checks on software products and can even help uncover bugs that may not be detectable by human testers.
Moreover, QA till now is focused on validating the functional and business requirements of software. It largely ignores the usage patterns which results in not meeting the customer expectations. To meet customer expectations in the experience economy, software QA needs to get customer focussed. AI can help them break the traditional QA mold and shift from functionality to experience by helping them extract insights from defect logs, production incidents and usage patterns, and user feedback.
How Will AI impact Software QA?
AI can be applied in many ways in the software QA process-
- Visual testing– Also known as UI testing in software quality assurance, visual testing ensures that software appears to the end-users the way it was intended to design. Visual Testing tools can recognize text, images, and videos, and capture gaps and differences. An AI-driven visual testing tool can support the human side and identify what is right and what is wrong in a UI of software.
- AI Codeless Testing- Codeless testing is the process of creating automated tests without writing a single line of code. It automates the process of writing test scripts. AI-driven codeless testing will help people with no programming or coding experience to create end-to-end tests.
- User Analytics- The biggest impact of AI will be in driving end-user experience and meeting customer expectations. AI will process and analyze data from defect logs, incident logs, and other sources and will extract real-time insights on user behavior and usage patterns to provide a 360-degree view of how a user is interacting with the software and identify issues impacting his experience. This will help software quality assurance teams quickly fix defects and bugs, address customer concerns and improve the software experience.
To sum up
Artificial Intelligence and Machine Learning (ML) are two of the most promising technologies that will help the software testing industry overcome some of the challenges that it is faced with.
However, there are a few challenges to overcome before AI can be mainstreamed into software QA processes like ML algorithms not being flexible and adaptable, high cost of implementation, and lack of strategies to implement and scale AI in a project.
Our product, AIMS Software Quality Intelligence, addresses all the above-mentioned challenges and makes it simple and easy for any software QA team to leverage AI in their processes. Our platform can help you monitor user behavior, glean data from various sources and deliver real-time insights to help your QA testers quickly identify and fix defects and bugs in your software. For a free demo or to know more about AIMS Software Quality Intelligence, contact us here.
