This is the fourth post in a four-part series from the article: Embracing AI First Software Quality Platforms: Transforming the Future of Software Testing
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Introduction
Implementing an AI First testing platform requires a strategic approach that balances automation, human oversight, and continuous learning. By carefully dividing tasks between human engineers and AI-generated tests, businesses can maximize efficiency while ensuring critical features are thoroughly validated. It is essential to articulate business rules clearly, allowing AI to accurately assess application behaviors and validations. Striking the right balance between exploratory AI training and fine-tuning helps minimize false positives and negatives. Additionally, recognizing patterns in test results and adopting an iterative, cumulative training process are crucial for aligning AI capabilities with the application’s intricacies. This guide outlines five best practices for effectively leveraging an AI First testing platform.
Best Practices for an AI First Testing Platform
In crafting an AI First testing platform, careful consideration should be given to dividing automation tasks, with high-value and high-risk features typically best-suited for human automation engineers, while others can be entrusted to AI-generated tests. It’s crucial to express business rules as behaviors, validations, and data requirements, providing the AI with the necessary context to navigate application events and perform accurate “Asserts”. Striking a balance between too fine-tuned and exploratory AI training is essential, with opportunities for iterative tuning as false positives or negatives emerge. Pattern recognition categorizes results, generating comprehensive reports without overwhelming development teams. It’s also important to recognize that AI training is an interactive and cumulative process, requiring multiple sessions to align the AI with the application’s intricacies and ensuring a continual learning process, with human intervention for challenging tasks. Here are the five best practices:
- Divide the automation into those tests which must be written by Human Automation engineers because they represent the high-value/high-risk features of the application. Often these are the ones the compliance officer, auditor, product owner, SecOps, etc. teams require to be certified. The rest can be left safely to the AI to generate
- When creating the requirements remember that the business rules underlying the application need to be expressed as behaviors, validations, and data needs because the AI will need to know what to do when it encounters events in the application. What to content to test for, essentially the “Asserts”, both specific and general. And the data needed to progress through a user flow, valid credit card, Zip Code, etc.
- Don’t train the AI too finely grained to begin with. Let the AI explore and discover the app. Then tune the training when you know it is starting to get false negatives and false positives.
- Look for patterns in the results can categorize them and report the general problem to dev backed up with the individual specifics otherwise you’ll overwhelm dev.
- Training is interactive and cumulative. It might take 10-20 training sessions to get the AI attuned to the app. From that point on it will learn, adapt, and when it can’t do those, it will ask questions from the human.
Summary
For a few decades now, testing tools have been written and rewritten with limited incremental value. The transition from thick clients to browsers delivered a net reduction of centralized capabilities. With cloud-based infrastructures stabilized and AI over its honeymoon stage, AI First testing platforms introduce the single greatest opportunity for organizations to reduce business risks while enhancing the end-user experience. To achieve the promised benefits organizations must be open to fundamentally changing their approach to testing. An AI First testing platform not only eliminates many traditional STLC tasks but also requires the organization to focus on activities that are not currently intuitive – for example training the AI.
As stated during any technical inflection point, a tool alone will not deliver the intended value. Adopting an AI First testing platform requires the organization to adopt a vision for the future with short and medium-term milestones to measure the success of process transformation.
Fourth post of a four-part series. Download the full eGuide here.
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