If you’re involved in quality assurance, which you most likely are or wouldn’t have started reading this article, then you know how important maintaining software quality is—especially in the modern digital landscape. Even a minor software issue can become amplified and lead to devastating results, such as financial and reputational losses, or even an existential threat to your business.
Fortunately, two concepts can work together to help us ensure software quality: AI-driven predictive analysis and software quality assurance (SQA).
In this article, we will take a close look at how these concepts work together. We will first look at the benefits, then look at some drawbacks and finally see some real-world examples. Let’s get started!
AI-Driven Predictive Analysis
Let’s examine what AI-driven predictive analysis actually is. To do so, we can break the phrase into its two constituent parts: artificial intelligence (AI) and predictive analysis.
As you probably know, AI is a recent phenomenon that has been taking the world by storm. It imitates human intelligence by using typical human traits (e.g., reasoning, problem-solving, learning and language abilities). Predictive analysis is the ability to use data to make informed predictions about future events. It may not be 100% accurate, but it is still a very useful tool.
When we put both AI and predictive analysis together, we can get useful predictions based on the vast amounts of data that have been fed to the AI.
What is SQA? It is the process of ensuring that software products meet high-quality standards before they are released to end users. To this end, it involves stringent procedures such as testing and validation—all in order to ensure that the software is free of defects. Traditionally, this is a time-intensive process that includes human error. That’s where AI comes in.
The Benefits Of AI And SQA
• More accurate results: Using vast amounts of data (orders of magnitude more), AI can account for more situations than just a human analyst can.
• Faster testing: AI can do its work in an instant, which means that software can be brought to end users quickly.
• Adaptable and efficient results: AI can learn from the process, which means it becomes better at its job as it goes on. This can lead to more efficiency.
What Are Some Possible Drawbacks?
We highlighted some benefits of using AI in the SQA process. However, as with all new technologies, there are new issues that arise—ones that we cannot ignore. These considerations include:
• Costs: Initially, using an AI-driven system can increase costs. This will be lowered, though, over time.
• Data requirements: AI requires a lot of high-quality data, and if this is not available, then predictions will be less reliable. Gathering and curating that data can take a lot of human effort.
• Trust: As AI is still in its infancy, it may produce errors that a human SQA practitioner would not. So, its use should be overseen by no less than qualified individuals.
How Are These Being Used In The Real World?
You might be wondering if this is all just theoretical or if people are actually using it. The answer to the latter is a resounding “Yes!” It is already being used out in the wild, so let’s now take a look at some examples.
We can imagine how important SQA is for an e-commerce platform. It’s absolutely essential, as any downtime directly equates to lost income. To stay in fashion, however, these platforms require constant updates. By using AI-driven predictive analysis, e-commerce sites can predict where issues will occur on their sites quickly with each update, ensuring that errors are discovered before they reach end users and keeping their site fresh.
Gaming companies are under constant pressure to release games with more and more features and better, more realistic graphics. AI can help predict how changes will impact the end user’s experience, such as realizing when a change will cause too much strain on a graphics card that will cause the game to lag. Optimizing resources is an essential aspect of game development to ensure smooth gameplay, and AI can help predict lag issues before they arise, too.
QA in the medical industry can be a matter of life or death. Proper use of patient data can help. Organizations in healthcare deal with a lot of this type of health data. Manufacturers of medical devices use this data with AI predictive analysis to better diagnose patient problems. This can help find illnesses before they become more serious.
Money makes the world go round. Companies that deal with financial transactions can process hundreds of millions of transactions per day—but some of these transactions are fraudulent. By using AI predictive analysis, a company can easily allow the AI to search for indicators of fraud. This gives consumers trust in the company while also directly saving the company money through less theft.
Nothing hogs up bandwidth more than streaming video. Video streaming platforms can be more efficient in their use of bandwidth, which they can do by being prepared for the situation of having their traffic surge based on public interest in their content. AI can help predict when these situations will occur beforehand, which will give the streaming platforms time to scale up or down so that end users do not notice any latency during high-demand periods.
Putting It All Together
AI has had a profound effect on the software development process. By integrating AI-driven predictive analysis with SQA, we can continue to make the process more efficient and accurate.