How Artificial Intelligence is Improving Quality Assurance
Quality assurance (QA) is an important part of the software development process. It is the process of testing a software application to ensure that it meets the standards and requirements that users expect.
QA can be a time- and money-intensive process. Teams are typically forced to thread the needle between conducting enough testing to ensure an app is ready to launch and staying within a budget and deadline. However, there are several ways that strategically deployed use of artificial intelligence (AI) in QA can help balance time and cost.
Let’s take a look at a few of the ways that AI is changing the QA game.
The Evolution of Quality Assurance Testing
Quality assurance has undergone rapid changes as technology has evolved. As recently as the 1980s, software testers performed most QA testing manually. This manual process was costly, time-intensive, and reliant on the capabilities of human testers.
In the 1990s, testing automation tools were introduced. Though the initial versions of the tools could be quite finicky and required a lot of manual intervention, each iteration continued to increase the efficiency and reliability of QA testing procedures. Between the 2000s and 2010s, Agile testing methodologies and continuous integration/ continuous deployment (CI/CD) strategies began to dominate the QA landscape.
The introduction of machine learning and AI has redefined the QA landscape yet again. The software industry is in the process of moving from test automation towards autonomous testing, a process in which AI tools completely create, manage, and analyze QA tests.
AI Implementation in Quality Assurance
As new AI tools are developed, the applications for AI in QA continue to expand. Today, some of the most common uses of AI within QA include:
Developing a Test Suite
A large part of the QA process involves developing tests and generating test data. Where these tests were historically created by hand, AI tools can now create testing suites in seconds based on detailed prompts.
While this collaboration has drastically increased the speed at which tests can be authored and executed, the success of the approach is still largely dependent on the testers. QA experts must now master not only QA but also AI-based communication. After all, the tests will only be as successful as the testers’ instructions.
Optimizing Test Coverage
Test coverage is an analysis of how much of your code is being used during testing. In an ideal world, every line of code would be carefully reviewed. Yet, as every QA expert can tell you, complete test coverage is rarely (if ever) a reality.
However, with AI’s ability to review and analyze large amounts of data quickly, optimizing test coverage has become much easier. AI can be used to quickly review test plans, identify areas of code without coverage, and create additional tests to optimize coverage. This allows developers to create a more comprehensive testing structure and decreases the number of bugs that reach deployment.
Performing Predictive Analytics
Predictive analytics refers to the process of using current data to predict future outcomes. Where QA has historically focused on identifying and addressing existing bugs, AI is now allowing developers to analyze current user trends, testing outcomes, and code repositories to predict future user behaviors and standards. This allows them to address potential problems before they occur, thus improving experiences for both users and developers.
Automating Repetitive Tasks
Finally, no list would be complete without discussing AI’s ability to automate repetitive tasks. QA engineers have historically spent valuable hours of QA testing on things like reporting bugs and managing test cases. With AI, testers are able to automate these time-consuming tasks, freeing them to focus on the more analytically complex aspects of QA.
Benefits of AI Integration
While the benefits of AI integration are numerous, some of the most positive for QA include:
- Decreasing Time to Market: More efficient practices allow teams to shorten the QA process and get apps to market more quickly.
- Increasing Consistency: It’s no secret that humans make errors. Automation with AI decreases human errors and increases the percentage of bugs identified before apps are deployed. This elevated consistency directly improves both the user experience and the app’s overall success.
- Improving Scalability: As technology continues to evolve, the apps we are creating continue to increase in complexity. Whether you are hoping your app will grow as your business does or you plan to continue adding more features,
Challenges of AI Implementation
Like every new technology, AI implementation in QA is not without its challenges. Some of the most pressing challenges users must address include:
- Investing in the Learning Curve: As we mentioned above, for AI to benefit QA experts, they must become experts in AI communication. Whether writing prompts for their AI tools or understanding the best implementation methods for the AI tools they have, integrating AI requires QA engineers to expand their skill set and stay up to date on the latest AI tools and trends. It also requires businesses to invest time and money into training and AI integration.
- Developing a Dataset: AI’s success is largely based on the quality of its dataset. If the dataset contains inaccurate or biased information, it will provide inaccurate or biased results. Companies need to choose AI tools with reliable datasets or invest in creating their own datasets.
- Addressing Security and Privacy Concerns: When creating a new app, developers are frequently dealing with proprietary or personal information. Developers must, therefore, make sure that security and privacy are not compromised when AI is used for QA.
Summary
As with every new technology, teams are examining the benefits and challenges of AI integration at every step of the software development lifecycle. Here at Grio, our QA experts are leveraging AI to continue producing beautiful, high-quality software. Contact Grio today to learn more about our quality assurance offerings and to see how we can make your big idea the next great app.
