Development teams are being pushed to ship higher-quality software at a faster pace and simultaneously there is constant pressure from demanding and finicky end users and from fierce competition. The arrival of tools offering AI capabilities will deliver a sweet relief.
Gartner’s Research Vice President Mark Driver predicts that, by 2022, at least 40% of new application development projects will have an AI-powered ‘virtual developer’ on their team. “This is because AI and ML have immediate potential in the realm of quality assurance and application testing. Automating these time-consuming tasks will support new ways of working in DevOps, mobile and Internet of Things environments.”
“Application development workloads will increase exponentially in the coming years, and as businesses grapple with new technologies and how to turn them into productive use or competitive advantage,” says Driver. So development leaders are left with a simple choice: Hire exponentially more employees to cope with the increased workload in a market already short on these skills, or increase developer productivity exponentially.
As we all know, the first option is out of the question in the current business environment, where software is eating the world and skilled programmers are globally in short supply. The only choice is to increase the level of automation in every stage of the development. Artificial intelligence and machine learning will be necessary for automated testing to keep pace with software development and deployment under these circumstances.
Expectations from an AI-based Test Automation Tool
The expected features that an AI-based testing tool should have are numerous and overwhelming. Following the industry discussions and research, a good tool should preferably:
- Identify and understand the environment
- Offer Cloud-based support with low cost
- Multi-platform support for a wide range of test executions including web and mobile
- Generate test cases and test scripts automatically, with an easy machine learning system
- Codeless test automation
- Identify and auto-correct the machine learned UI or element changes in the recurring test
- Execute and report the auto corrected changes for further script enhancement and maintenance.
- Reduce the script development and maintenance effort, with inbuilt features/tools
- Intelligent trace logging to analyze the test flow and failures
- Integrated with a built-in build and release system
- Better reporting mechanism for quick decision-making
- Better execution history maintenance and metrics availability
- Customizable metrics projection.
Naturally these wishes from the bottomless wishing well have not been realized yet by the industry. Various vendors have taken their own approaches to help development teams and testers to solve their everyday issues with AI. Eggplant, for example, has introduced an AI-powered test case auto-generation, SmartBear is in beta with its TestComplete AI-driven visual technique for object recognition making the test automation scripts less brittle, and Applitools uses AI to intelligently verify UI test results.
Introducing AI Testbot
We are rolling out the AI Testbot today on Bitbar Testing Cloud. We provide a very cost-efficient cloud-based AI tool (see our pricing) with codeless test automation with reports that help analysts to extremely quickly verify the correct behavior of the application under test on a huge number of end user devices. They can spot fast any issues in the user flows, get logs, performance data, videos and screenshots to validate the new build is green and ready to go to the next phase in the process.
Fast development demands fast testing
Mobile test analysts are assigned the impossible task of making sure the developed app works on all mobile devices and all operating systems. Testing each new or updated feature on a wide range of devices is a very expensive exercise but is necessary to satisfy the business demand of fast-paced releases and end-user demand of flawless user experience.
The AI Testbot helps our customers dramatically tighten their continuous integration loop with scriptless AI-driven exploratory testing for compatibility and application performance testing on real devices. There is no need to keep updating the test automation code for each new feature, change in the UI leading to a much higher velocity of development with lower cost.
Below is a quick video of how to execute AI Testbot on Bitbar Device Cloud.
Tools Will Need to Evolve or Die
We at Bitbar are committed to helping our customers to deliver more software with fewer people and to make them successful in their projects and bringing the cloud-based AI Testbot is a significant step to dramatically lower the cost of the current manual exploratory testing and make it very large scale.
AI Testbot is available on Bitbar Public Cloud, Private Cloud and On-Premise customers.
If you are new to Bitbar Device Cloud and are interested to give AI Testbot a try, sign up for a free account and start more testing with less code.