Though the concept of Quality Assurance is nothing new to the software development industry, the need for creating effective testing and QA strategies has grown significantly during the past few years. The emerging competition, large number of device exposure, and hyped demand have all made it important to deliver quality assured technology.
However, the resistance to QA services is natural to some extent. Especially, when businesses are operating on some pre-defined legacy practices, adopting or aligning an all-new QA process drives several challenges.
Nevertheless, most modern organizations that understand the need for advanced Quality Assurance services have made their way to adopting new technologies to compensate for the downtime from the past. And Artificial Intelligence is a concept that has dramatically made its way to the Quality Assurance process, either through automation tools or by simply improving the development lifecycle.
In this blog, we will aim at highlighting the whole idea of integrating Artificial Intelligence into the Quality Assurance model while understanding the production process and how AI power could be unleashed in real-time projects. Let’s begin.
Understanding Various Existing QA Approaches
As per the present scenario and insights from the past, there are mainly three different approaches to Quality Assurance adopted by various companies. These include:
An all-manual approach to Quality Assurance
The second approach involves the combined use of automation and manual QA techniques
The third group includes the active integration of test alternatives and new technologies like AI.
As long as it is concerned with Automation, the process involves the use of different solutions such as machine vision. The original concept of machine vision was established around the predetermined guidelines of the experts on faulty and satisfactory benchmarks. But the modern approach involves the use of AI technology to improve system efficiency on checking a product at various dimensions.
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AI, Quality Assurance, & Validations
Since AI is not something that humans have perfected, integrating AI into QA models needs extensive training on data. The role of AI in the Quality Assurance model is all about testing the training data provided to meet the goals of the job defined. However, the process to train AI has actually become easy after all the years of research and time spent on improving AI models.
When it comes to QA, the engineers aim at selecting a portion of training data and applying it to the crafted scenario to check the algorithm’s performance on returning the predictive results. Furthermore, the system is checked for consistency and accuracy in the approach and behaviour to data.
If errors are detected in the validation process, the AI is made to rework the entire development process until the desired outcomes are obtained as per the QA guidelines. However, the concept of using AI in Quality Assurance is not a one-time task that works on a standard process of software development. It is all about more thorough research that is worked for arbitrary resource availability at the end of the disposal team or organization. Such an approach creates more space for improving the AI model to hit the production environment.
The process, on the whole, is all about the training phase where DevOps teams aim at multiple algorithms to check AI capabilities on different grounds. The approach involves assumptions on the use of AI algorithms to project perfection which usually needs testers and developers using AI to presume that AI works exactly it was supposed to do.
Though the process requires QA engineers to deal with training data and hyperparameter configuration, the hyper configuration factor is worked through validation tests to check the accuracy of hyperparameter settings. As AI algorithms feed on test data, the QA teams also work on checking the completeness of data on their way to fostering measurable results. Some of the most common queries that testers may aim at checking AI accuracy in the QA model includes:
The training model must be designed to represent the real-time predictions of the algorithm
To check any influence on training data from data-based or human-based biases.
To check any blind spots for algorithm fails in the real world compared to training environment success.
All these measures allow ensuring the quality of training data with the progress of the project. Besides, it is necessary that quality assurance service providers must have focus on all the expected and possible queries that can help create comprehensive solutions. This necessarily should aim at overcoming the bias caused by AI and sticking to ethics.
Suggested Read: 7 Ways Testers Could Help Leverage The Quality Assurance Process
Why AI Must Be Tested for QA In the Production Environment?
The QA team is responsible for checking the efficiency of the AI software for validating all the training data and delivering the results as per the expectations of the QA executives. But every AI project is unique when it comes to managing and processing data that keeps on changing and growing. Therefore, it becomes necessary to check AI for QA at the production level.
Once the AI model works well in meeting the aforementioned factors, the Quality Assurance team could work on testing the performance and behaviour of AI on new data. The approach is meant to encourage the evolution capability of the AI process for QA by QA practices themselves.
The process is widely understood as ML Ops or Machine Learning operations where QA engineers work at version control, iteration process, discovery stages, cybersecurity, and overall software management.
Unleashing The Power Of AI
Even when you are done perfecting the AI model for Quality goals, integrating the same into the production environment may get tricky. The problem is nothing about the potential of AI technology or the capacity of the QA team to integrate AI into the process, but the reasons are associated with the business view of pursuing AI. However, overcoming the hesitation on AI in the Quality Assurance process could be worked in two ways:
The primary stage to bringing AI into a QA environment where engineers are already working on DevOps and CI/CD pipeline is to justify the investment made in AI. It might require quantifying the financial aspect of adopting AI through strategic thinking that can help foresee clear goals in regard to holistic development.
The next step involves understanding the right role of new technologies like AI in the development and QA process with the help of internal expertise. Since every project is different and an intelligent approach is required to get forward on such objectives, testers could work with reliable quality assurance service providers.
Also Read: Digital Business Acceleration With Quality Assurance
All the best!
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