By 
What if I told you that AI was a part of our lives before we realized its influence? AI is truly useful when you don’t realize that AI is being used. Most of the content you consume on Netflix is recommended by an AI algorithm. To give you a perspective, more than 80% of what people watch on Netflix is discovered through its recommendation system. Just look at the Instagram or Facebook feed of another person. Completely different content type and information. In hindsight, it may not seem impressive given the advanced applications of AI that we are used to seeing now. AI has penetrated the application development lifecycle over the last few years. It accelerated the timelines and agility of software development. However, the possibilities of AI in quality engineering remain underutilized and worthy of exploration.
Quality engineering is all about bringing out the best customer experience possible. Hence, AI in quality engineering has become a necessity today, with customer experiences climbing up the priority list. Companies that fail to ship high-quality products at record speed are quickly losing relevance. It isn’t fearmongering but the fact of the day. Testing methods must advance with technological innovation.
First, let’s understand what AI in quality engineering entails.
AI in Quality Engineering Explained
Artificial Intelligence in quality engineering refers to the application of the transformative power of AI and machine learning (ML) to build software of superior quality in line with customer expectations. With AI, companies can change the way software testing is done. Teams can now implement advanced test automation, create test data that resonates with real world scenarios and optimize QA workflows to achieve faster and better applications.
Developers can overcome the challenges plaguing manual testing methods to ensure improved speed. AI as a copilot, arrests bad quality of code to be shipped for testing in the first place, thus minimizing hours of rework and costly mistakes. The typical process of writing thousands of test cases and running them can be replaced by AI tools with a click of a button.
But how does AI manage it?
AI can improve your development processes for better software output, with efficiency greater than test automation. When you automate testing, it follows a broad set of explicitly programmed rules. AI, on the other hand, tackles the issues with algorithms that first gather understanding of an application and then test it. It uses advanced statistical methodologies and ML models to decide an appropriate action. It leverages real world experiences in similar or related domains to create models that would have taken a humungous time to create otherwise. Instead of just looking at the effectiveness of the test, these data models work to ensure minimal to zero errors when users are interacting with the system for an intended task.
The role of AI in quality engineering can be classified into three different buckets:
- AI in Test Design
- AI in Execution
- AI for Insights (continuous improvement)
Let’s explore these briefly.
1. AI in Test Design
Unbiased requirement
By replacing human intervention with AI, you eliminate typical biases to have test objectives that represent diverse user journeys. The algorithms thoroughly study requirement documentation and analyze keywords, phrasings, and historical data to identify unintentional biases. It can also highlight areas that are not covered in the documentation leading to new test cases for comprehensive testing.
User-driven enhancements
AI can modify test cases based on user reviews, suggestions, and social media mentions tracking real-world usability issues. When AI algorithms are trained on user data, they can make predictions on how users will interact with the application and will be able to create use cases and potential performance issues.
Improving test enhancements
Instead of randomizing the test case designing, AI will prioritize vulnerabilities with high-risk scores by looking at the historical test data and risk reports to identify defective areas. With data-driven prioritization, you can improve the impact and efficiency of testing. The data models can also suggest potential edge cases looking at software code.
Co-Pilot automated test scripting
You can also speed up script development using AI tools like Co-Pilot that accurately depict your expectations and desired outcomes. It helps in saving time and effort to write error-free test scripts. These tools can also help you in detecting redundancy and simplifying complex steps for better performance.
2. AI in Execution
Computer Vision
AI uses a computer vision mechanism to test applications by tapping elements and swiping screens without human help. Additionally, it can detect and investigate subtle visual changes by comparing different versions of a user interface. AI allows you to visualize your outcomes on the screen and verify them against expected results in an automated manner. It will eliminate the need for manual assessment, saving time.
AI-driven Low Code No Code
Low-code/no-code technology has been truly revolutionary in democratizing testing allowing individuals with minimal coding experience to create automated test scripts. These solutions allow scripting through drag-and-drop functionality and natural language commands.
Test Data Creation
AI can create large test datasets across formats and test cases. Additionally, you can enable automatic data masking for sensitive information ensuring compliance with data privacy regulations. This test data changes dynamically to adapt to changing application states or user journeys ensuring realistic user actions.
Performance Testing
You can proactively identify resource bottlenecks and performance slowdowns by analyzing test data. By helping developers accurately identify these areas, AI ensures you rectify the performance issues quickly. It also allows you to better prepare for any eventualities by predicting fluctuations in load capacity. Using these insights, you can scale traffic capacity seamlessly, thereby ensuring optimal performance even during peak usage.
3. AI for Insights (Continuous Improvement)
Observability & Reliability Insights
Application monitoring involves going through volumes of logs, metrics, and traces. Doing this manually is not just impractical but also error prone. However, AI application automates anomaly detection by tracing irregularities and potential issues even before they affect users.
Auto-detect Coverage Gaps
Al algorithms analyze software code at a granular level detecting areas that lack testing coverage to reduce risk surface. They rely on user behavior to pinpoint features or areas that are critical for application performance. As AI automates test scripting, it covers edge cases through regression testing.
Root Cause Analysis
AI can be powerful in analyzing performance issues and system slowdowns by studying extensive data sources to uncover systemic issues. It groups similar incidents and occurrences and traces causal chains, to make troubleshooting faster and more effective.
Performance Tuning
AI acts as your application’s performance guardian by automating vulnerability detection, directing resources as per the need, and optimizing efficiency for consistently better applications.
Future of AI in Software Quality Engineering
As we move into the future, AI promises a growth trajectory marked by heightened accuracy, expanded testing scopes, improved software quality, faster turnarounds, and prolonged testing durations. By 2025, 30% of enterprises will have implemented an active use of AI-augmented testing, up from 5% in 2021, according to Gartner’s “Market Guide for AI-Augmented Software Testing Tools.”
Organizations that ignore the opportunity to utilize AI-augmented testing will spend twice as much effort on testing and defect remediation compared with their competitors that take advantage of AI.
About the Author
Shiladitya is a technology solution and delivery professional with a keen interest in blockchain and augmented reality. At QualityKiosk, he leads the quality engineering and DevSecOps service line. He is primarily responsible for driving quality engineering solutions across large customer’s transformational programs. Prior to QualityKiosk, Shiladitya was a Delivery Director with Ness Technologies. His passions include photography, traveling, public speaking, corporate training, reading, and playing drums.
