In the modern world, software development is the sun around which our lives revolve and developers are under constant pressure to produce at breakneck speed. Unfortunately fast work is often associated with substandard work as traditional approaches to testing would rely heavily on manual, time-consuming efforts - but not any more.
Businesses who refuse to compromise on quality - but still need fast results - are discovering the power of AI software testing tools. Even the most complex Agile and DevOps workflows can now be managed quickly and efficiently through advanced software testing processes, allowing for seamless adoption with superior speed and quality.
Understanding The Role Of AI In Software Testing
OK, I get it - artificial intelligence is all we hear about these days; but there’s a very good reason for that. As great as us humans are, we simply cannot compete with the superpowers of AI when it comes to speed and accuracy. AI allows us to analyse, predict and adapt within the software lifecycle by using historical data to identify patterns and make decisions. This is in direct contrast to traditional automation which executes predefined scripts.
Advanced
AI software testing tools mean that we can now do away with manual intervention as this tech can:
- Generate intelligent test cases
- Dynamically maintain test scripts
- Superior maintenance of anomaly detection and predictive analytics
- Identify areas of higher risk
- Suggest optimal test paths
- Autonomous adapt to application changes
With AI doing the heavy lifting with the above critical needs, bottle necks are reduced and software developers can focus their talents on more creative tasks.
What’s the deal for project managers?
But wait, I hear you cry - are we replacing humans with machines?
Absolutely not. AI software testing tools work hand in hand with real life developers to create a dream team of speed, quality and efficiency. One of the biggest headaches for project managers is prolonged testing cycles which can delay releases and bust budgets through inflated project costs. All of these things cost time and money and frustrate stakeholders who may not always understand the dire consequences of incomplete or rushed quality checks.
Driving efficiency and reducing bumps in the road
Traditionally, crafting test cases for each and every possible user interaction was a mammoth task - and one that was wide open to errors. Not only that but updating test scripts for user interface changes is a great way of draining valuable QA resources.
AI tools are set to change all that by dynamically analysing application behaviour and user patterns, allowing developers to create and adjust test cases on the fly. This technology can also easily analyse risk factors including recent codebase changes, historical bug density and feature criticality as well as suggesting which tests should be prioritised. This helps to ensure that crucial functionalities are verified early and that testing efforts are laser-focused where they’re needed most.
AI is the tech that keeps on giving and, in addition to all the other good stuff I’ve mentioned, it can also aid in expanding test coverage without having to match this with increased effort. Automated cross-platform and cross-browser testing ensure that the widest possible range of user environments is validated which means fewer post-release failures.
Building an AI strategy for success
So, we’ve talked about the considerable benefits of AI driven testing and now you’re all set to get started, right?
Not so fast - before embarking on this new way of doing things, there are a few things you need to know and we’ll take a look at these in this section:
First off, project managers will need to examine their current processes to identify any sticking points such as:
- Are there bottlenecks?
- Are manual regression tests delaying releases?
- Is test maintenance earring into valuable sprint time
By figuring out your pain points, you’ll find it much easier to laser target your AI testing. It’s also really important to establish metrics from the get go to track indicators like test cycle time, defect detection rates and production incident rates as these will form the foundation of your testing.
Next up is choosing the right tool for the job. For this you’ll need to weigh up factors like integration with your existing CI/CD pipelines, support for other platforms, cost effectiveness and how user friendly it will be for your QA team. A solution like testRigor’s AI testing tool offers codeless test generation and powerful self-healing capabilities which will make the transition easier for your team.
Making the shift to AI testing can be a massive game changer for your business but, a word of warning - it is essential to
invest in training and change management for your team as AI is not just a new tool, it’s an entirely new mindset. By starting small and making training an integral part of your transition you can ensure that you’re up and running with minimal disruption.
Challenges in adopting AI testing - and how to overcome them
As great as AI software testing is, you may of course encounter the odd bump in the road to begin with and these can include:
Cost - Switching to any new technology involves an initial financial outlay however, this is absolutely an investment which will pay for itself many times over by speeding up test cycles and reducing errors and issues.
Data quality - In order to make your testing system super-effective, you do need to make sure that your data is clean, organised and relevant.
Complexity - It will almost certainly take some time for your team to get to grips with this new tech - and some people may be resistant at first. Again, training is key here along with choosing intuitive tools that will help your employees during the transition.
A real-world example of the tech in action
Now that we’ve given you a pretty good idea of what this advanced technology can do for you, it’s time to take a peek at a real life example. Here, we have a mid-sized e-commerce company who is gearing up for a major mobile app update ahead of the holiday season. In the past, the company’s regression testing would take over three weeks for each cycle, leaving them at risk of missing their release dates.
Once the company brought AI testing into the equation they were able to automate a whopping 75% of their regression suite as their new tools dynamically adjusted tests with every sprint and caught bugs super early. With less manual intervention needed - even when significant UI changes occurred - the company was able to reduce cycles to just one week. The result? The app was released on schedule, customer satisfaction soared and everybody was happy.
Tapping into trends - today and tomorrow
Artificial intelligence is already pretty clever stuff but it’s also constantly evolving and, in this section, we’re tapping into some trends that you might want to keep an eye on.
Hyperautomation - As the name suggests, this is automation on steroids - here, the tech not only automates test execution but can also orchestrate entire testing pipelines; from environment provisioning to test result analysis.
Security testing - In the not-so-distant future, these tools will be able to predict and detect vulnerabilities at early development stages, giving project managers greater control over security from the outset.
Codeless automation - Yep, as natural language processing (NLP) becomes more sophisticated it may actually eliminate the need for coding - making test creation accessible to everyone; not just techy types.
Continuous test optimisation - Here, AI actively monitors application usage patterns and adjusts test suites in accordance with this - thereby resulting in happier customers and more efficient business.
Conclusion
In the modern world it’s time to stop thinking of artificial intelligence as something new and scary and start understanding the ways in which it can supercharge our businesses. For software teams this isn’t just a technical upgrade - it’s a strategic asset that will transform delivery of projects. Because of this it does require an investment in time and money and a focus on overcoming resistance for long term success.
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