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Generative AI (GenAI) has fast become a strategic priority for organisations, with copious amounts of initiatives being vetted and explored across business and IT functions. But how is GenAI impacting the delivery of quality and testing services?
Anyone in software development know that the demands are very high across the life cycle to increase velocity of releases whilst maintaining high quality of all products, against the increasing complexity of software systems to be engineered. Consumerisation and increased focus on customer experience, and the challenge in reliably testing applications to meet objectives, also adds difficulty and complexity to quality work.
As a result, hopes are high that GenAI technology can be used by quality professionals to accelerate delivery of software quality and testing. Gartner is predicting that by 2025, “30% of enterprises will have implemented an AI augmented development and testing strategy, up from 5% in 2021.”
AI such as GitHub Copilot and other large language models are also becoming embedded in other parts of the application life cycle, where it can help developers working under time pressure to modernise legacy applications, as well as create new applications with code creation for improvement in velocity and quality of work. Most integrated development environments (IDE) have some sort of generation code completion/prediction, enabling them to generate initial code faster and with less effort.
This will likely intensify the need to accelerate and improve testing and quality efforts to keep up with the pace of development, or you will hit bottlenecks. This is not lost on quality professionals, as GenAI is becoming part of their toolkit. GenAI represents one of the most transformational developments in recent times, although copious new technologies, methods, and processes are constantly explored and implemented to improve quality engineering.
When humans with skills, experience, and existing tools are augmented by GenAI and real-time, intelligent feedback and suggestions, it has the potential to revolutionise the delivery of quality. Here are some ways that GenAI can benefit quality delivery: Speed - The use of GenAI helps and supports quality professional to do their job faster.
Quality - GenAI can increase quality of work, as it frees up quality engineers to spend time on more complex and original tasks by helping and accelerating more mundane or repetitive ones.
Knowledge and skills - GenAI can be helpful for less experienced quality engineers to experiment and learn. It can also ensure that when staff leave, the knowledge is not leaving with them. AI can provide a constant, where all users learn to use the tool, and the tool ensures tacit knowledge is not lost.
Efficiency - Customised GenAI models can capture massive amounts of data for specific areas, which immediately raises the vantage point for all engineers. What is generated comes from the same source, so if you provide a high-quality source, what everyone starts with is a high-quality artefact.
Early insight - GenAI allows for earlier insight in the software life cycle to effectively move further “to the left”. This mitigates issues with quality work happening too late to have a positive impact, or risk involving major rework and the risk of becoming sidelined.
What GenAI is not going to do is provide a solution to all quality concerns. In addition, when organisations start implementing these technologies in their business for various use cases, it is anticipated to bring an increased complexity of code, data, integrations, and systems that can hide flaws and issues. In those instances, it would likely result in more quality and testing activities needed.
GenAI should be one of the tools in the toolbox to accelerate delivery of quality across projects. The key to success is to do so with a careful and considerate approach.
Interested in knowing more about how GenAI can accelerate delivery of software quality and testing? Then download our complete whitepaper for further insights into how this technology has the power to redefine business and provide a big impact on how we work.
As a Gartner analyst, Susanne owned the testing services Magic Quadrant for over 6 years, advising hundreds of CIOs, Application Leaders and Digital Transformation executives on software quality engineering related topics. At Planit, Susanne ensures our offering roadmap addresses current and future customer requirements, whilst incorporating emerging technologies and approaches.
Practice Director - Customer Insight & Advisory
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