It’s not a matter of whether to use AI for testing, it’s how to use it strategically. Though the benefits and risks artificial intelligence (AI) brings to a business can be controversial, the massive leaps AI is experiencing right now are setting the stage for processing large data swathes that will help companies predict outcomes, spot patterns, and offer solutions.

Many testing-related tasks rely on manual input, which can be time-consuming and prone to errors and inefficiencies. With AI-powered engineering workflows, the human involvement in these monotonous yet critical processes is optimized to the point that most work happens automatically, only interrupting the more pressing work of your developers if oversight or intervention is necessary. That’s how AI is able to enhance your existing engineering capacity and allow you to gain an advantage over competitors.

Using AI-Powered Testing Strategically

Companies can harness the data-driven power of AI tools to deploy better solutions faster by using the information in project documentation, test artifacts, defect logs, test results, and production incidents to train machine learning algorithms. In turn, these ML models can help businesses: 

Prioritize test cases: Teams often work against competing priorities without clear visibility of what’s urgent and when. Through a combination of AI, ML, and natural language processing, test cases can be generated and prioritized automatically, and even modified as the source code or environment changes. This information can also be used across various statistical methods (like Taguchi orthogonal array) to reduce test cases and increase efficiency.

Support developers: AI-powered chatbots can be the go-to resource for developers who need real-time assistance, leveraging historical data and information on how applications are performing and behaving during live testing. These chatbots can be deployed at scale without additional engineering resources.

Faster bug triaging: A well-trained AI system spots issues well in advance. This can be used to set up a smart alert system where notifications are sent to a specific person to be handled in time. These solutions can also analyze data from multiple sources for a more holistic overview of larger systemic issues, potentially preventing failures proactively. 

Task automation: AI can automate monotonous and repetitive tasks that aren’t the best use of your development team’s time. Things like testing, reporting, and deployment can all be automated to maximize your engineering efforts while streamlining key processes and resolving issues that may otherwise go undiscovered with traditional methods.

Mitigating the Challenges

Any AI solution lives and dies by its data. We’d be remiss not to emphasize the challenges and risks with deploying AI in any aspect of your business, especially when it comes to testing. 

Biased solutions and decision-making: Biased and incomplete data will lead to biased and inadequate solutions. Using synthetic data and thoroughly testing models before and after deployment can help you eliminate bias. 

Lack of transparency: Relying too heavily on AI can remove some transparency around why and how a decision has been made. This can lead to concerns around false negatives or positives, reducing trust in AI models. Taking strides to implement “explainable AI” from the get-go will help prevent this, and it begins with educating all involved on what data is being used and how the model was trained. 

System integration: Systems do not always easily integrate. It can create unnecessary work for your developers as they struggle to make them work together harmoniously while learning to navigate starkly different environments. As with any new integration, careful planning is a must.

Moving Forward

AI holds great potential in streamlining the development pipeline, but its deployment will depend on your organization’s maturity. While you may feel pressured to implement AI quickly, the most effective way to harness its immense potential is to search for gaps in expertise and data; plan an intricate pilot launch; and roll this promising but complex technology out gradually.

For most companies, there is simply not enough internal guidance to effectively integrate AI into day-to-day processes, which is where a partner like Quest Global proves invaluable. As an engineering and research partner specializing in AI, Quest Global is helping companies realize the full capacity of AI through strategic planning, implementation, and industry-specific prototypes and pilot projects. Our solution for HMI test automation, using computer vision and deep learning, is unparalleled. With an experienced partner and a proof-of-concept in place, you can forge a clear path forward with regard to AI testing.

Written by Sindhu Ramachandran S

on 12 Jun 2023

Director - Technology, CoE Leader for Artificial Intelligence,

Quest Global