The constant and high demands of our consumers require us to look at non-traditional methods to meet these demands. We must evolve, be agile, intuitive and produce greater benefits. When a new product or application needs to get to market quickly yet go through the needed rigorous testing before moving to market can be difficult meeting both quality and deployment. Typically, in software testing, we follow a series of checklists and parameters to ensure we cover every possible scenario that a consumer may encounter.
Knowing that for every action or input we should get an expected result or output. The sooner we can get through every possible scenario without an unexpected result, or bug, the sooner we can get it to the consumer. It is important to know what testers need to understand as well. Having test metrics are an integral and critical piece to your testing process.
So how do we overcome this challenge with traditional testing techniques?
Soon we won’t be able to keep up with the overwhelming demand or deadlines. The good news today, however, we are already employing advanced machine learning to validate our new systems and applications. Furthermore, we are employing chatbots in parallel to improve response time to customer inquiries or requests. Employing machine learning and artificial intelligence (AI) methods, businesses across the globe are improving the customer experience, and enhancing their product offering. Additionally, businesses are employing predictive analytics to guess what other products or features customers want based on previously provided information.
Predictive analytics go together with machine learning, especially when the consumer is involved. In our ever-connected planet, we rely on precise and actionable analytics to operate it.
As our environment becomes larger and more complex with massive processing requirements, we can no longer look to conventional automation methods that would ultimately create latency, impediments and mediocre customer experience.
Unlocking the power of automation
We covered the challenges and some of the examples of where machine learning and predictive analytics are employed to produce faster yet more quality output. With the power of machine learning, we can pull from additional sources like test objects, project documents, results and defect registers, where this wasn’t previously possible in traditional human testing.
As we just discussed, bringing machine learning and analytics together will release the true power of this information and push innovative automation, to improve software testing efficiencies beyond anywhere traditional testing techniques could go. Machine learning algorithms can learn from test data and results to deliver intelligent discernments.
For example, it can provide information on common or typical defects, malfunction predictions or patterns, and software stability. Having this type of insight will assist in prediction and automation, providing quality sooner in your project lifecycle. If you don’t have the resources to develop this strategy in-house, you may want to employ a trusted partner. Additionally, you’ll want to have a platform that is adaptable to the diverse client technology environment, preferably built on an open source stack.
Reduce time to market
When considering a machine learning testing strategy, think accuracy and efficiency as your end goal in the quality assurance journey. Benefits such as detecting redundant unsuccessful tests, prediction and prevention, and keeping untested code out of production ultimately reduce much of the risk in your deployment phase. Some of the key contributions in machine learning led quality assurance include:
- Enhanced analytics
- Faster predictions
- Improved optimization
- Cleaner traceability
- Real-time feedback
- Defect alerts
The sooner you can implement an in-house AI platform to assist you in your application testing, you will discover a more accurate and efficient deployment with reduced effort. Using defined test metrics and analytics will launch your application development to new heights.
https://www.cio.com/article/3251850/application-testing/digital-disruption-software-testing-with-machine-learning.html