Artificial intelligence (AI) is making a massive impact on the world, working its way into apps and systems used by consumers and businesses on a daily basis. Competition is also heating up, with more startups and entrepreneurs funneling time, money, and resources into the possibility of more advanced, faster, and more profitable AI systems. Everyone is looking for the technology that will provide the next big leap forward, and it’s only a matter of time before they find it.
In the meantime, developers and businesses leaders are relying on a new mode of testing-continuous testing-to increase the efficiency of their development while lowering costs. If executed properly, it has the potential to drastically improve the development cycle of AI-based applications.
The Basics of Continuous Testing
Continuous testing is a process that attempts to solve one of the biggest limiting factors in development: the speed of software releases. The faster a company can improve the efficiency or performance of its software, the more competitive it can be.
This has led to the adoption of lean-minded development philosophies, like agile development, which allows developers to create software in short, iterative bursts, rather than a comprehensive, bottom-up build from scratch. This allows software to be developed over the course of weeks, rather than months, and helps identify potential problems before they become major problems.
One issue with this approach has, historically, been the fact that traditional testing processes simply can’t keep up. Testing, when employed manually and only after a major iteration is completed, can slow things down and cost lots of money. Continuous testing, on the other hand, offers organizations a way to test the product as it’s being developed.
The basic idea is to use a series of automated tests to evaluate a product as it’s being developed, oftentimes in a parallel environment, thanks to virtualization. This helps developers proactively identify areas that need improvement, while they’re still working on it (rather than being forced to go back and change things once the entire product is complete) and gives business leaders a chance to assess risk in advance.
The Benefits for Businesses
Organizations that adopt a continuous testing system have several advantages over others:
- Parallel work. In a traditional testing environment, developers are forced to stop working during the testing process. This creates a major holdup, bringing development to a total standstill. With continuous testing, the application can be emulated in a virtual environment and tested, allowing developers to continue working as they discover new issues or tweaks that could be made.
- Increased testing speed. Another major advantage is the speed of continuous testing. Continuous testing is automated and designed to work as quickly as possible. It’s also used throughout the development cycle of a given application or product, rather than only starting once a segment of the application is completed. This allows testing to take up much less time, and allows the application to be completed and released much faster overall.
- Higher-level risk analysis. Continuous testing isn’t just about finding potential bugs and issues important to developers. It’s also important for business leaders and managers to identify and analyze potential risks. This high-level risk analysis gives them a chance to analyze the application’s performance in dimensions like compliance, security, and overall performance. If it isn’t meeting certain risk standards, leaders have a chance to make a decision about it, long before the application is finished.
- More consistent application quality. Continuous testing allows developers to work constantly on improving the application. Instead of building a product, then using testing to highlight areas that need to be improved or debugged, they get a near-constant stream of information they can use to develop the application as efficiently as possible.
- More documentation. Continuous testing also arms developers and managers with more documentation. Testing results and improvements are documented in sequence, leading to better high-level analyses overall.
A key possibility for the future of continuous testing is the use of AI-driven algorithms to employ the testing itself. Already, continuous testing depends on automation to handle the bulk of the necessary tests. If advanced AI can make that automation, for lack of a better word, smarter, it could test applications even more thoroughly, and uncover risks and issues even more efficiently. It’s a way to use AI for the development of faster, even better AI.
Continuous testing is still in its early stages of integration, and isn’t used by every organization working on AI projects, but it is growing in influence. One recent survey found that 65 percent of organizations are now using continuous delivery on at least some projects, and 28 percent are using it for all software.
As more businesses discover the enormous potential of the approach, expect those numbers to skyrocket.