(Newswire.net — May 28, 2019) —
Machine learning and artificial intelligence (AI) companies are always striving to boost their main priorities: efficiency, innovation, and constant development. Pushing our greatest technological achievements to new heights is obviously challenging, but with the development of a new mode of testing, it may be even more possible.
Enter the world of continuous testing. This system works the way it sounds; developers employ a mode of continuous testing and feedback so they can tweak their products and expectations from the earliest stages to the end. It makes use of automated tests in the software delivery pipeline so it’s possible to get feedback and determine risks associated with a release long before it’s too late to take action on them.
So why is this so important for the future of AI?
The Key Benefits of Continuous Testing
There are several benefits to the AI development community:
- Compliance with the “lean” philosophy. Lean development paradigms have taken over the development community. Agile development, for example, encourages developers to create software in a series of short iterations, or bursts, rather than developing a product from top to bottom all at once. DevOps encourages the interaction between software development (Dev) teams and information technology operations teams (Ops) to make frequent updates and squash bugs faster, all while optimizing for faster delivery. Continuous testing complements and enhances the performance of these already-beneficial philosophies.
- Faster testing. Continuous testing is simply faster than traditional testing processes. The length of an iteration of development has increased in recent years, taking weeks or even days rather than months. If your team were to rely purely on thorough, large-scale, manual testing, there’s no way it would be able to keep up the pace. In other words, traditional testing would hold AI development back from its full potential. Continuous testing keeps things moving.
- Risk analysis. It’s necessary for managers to identify and analyze risks associated with a given product as it’s developed. Business leaders need to not only determine whether the product works and meets baseline expectations, but also whether or not it’s ready for release. With continuous testing, tests can be tailored to a company’s tolerance standards in areas like performance, security, compliance, and reliability.
- Embedded quality. There are two ways to ensure the quality of a given product, and one is drastically more efficient than the other. The first method relies on retrospective analysis and correction; you look at the product as a whole, identify areas that need improvement, and try to improve upon them. The second method is to embed quality into the product as you’re developing it. The latter is less resource-intensive and much more efficient, and it can be achieved through continuous testing.
The Scope of Continuous Testing
Continuous testing involves both functional and non-functional testing. For example, for functional requirements, a team might employ unit tests, integration testing, system testing, and if necessary, API testing. Non-functional requirements would involve tests like security testing, performance testing, and static code analysis to determine whether the product meets compliance standards, security standards, or other thresholds.
Integrating Continuous Testing
Introducing continuous testing to a new development environment can be a challenge, but if the team is already using other modes of agile development, the core philosophy may already be in place. There are, however, some key steps to introducing and maintaining a successful continuous testing process:
- Use a model-based approach. Using visuals and specific requirements will provide clear, unambiguous data to all teams involved with the process, including engineers and sub-teams of testers.
- Rely on service virtualization. With service virtualization, you’ll be able to emulate a system that would otherwise be unavailable, enabling your team to continue working on their development project while you test it in another environment. This enables both faster work and lower costs for the organization.
- Store data centrally. If you create test data on demand, each tester can have access to their own data for testing purposes. If your developers are relying on a central data storage platform, you can reuse assets between tests, avoid redundant testing, and help everyone work more efficiently.
Dev teams and companies dabbling in the world of AI can no longer rely on traditional forms of testing; at this point, they’re too slow, too inflexible, and too costly to stay competitive. Continuous testing may be in its infancy, but it’s already enabling developers to respond to issues and business leaders to analyze risk more thoroughly than ever before.