How To Leverage AI For Improved Performance Testing

The more, we are getting closer to the digital era, the need for performance-driven solutions is constantly expanding. Earlier, the focus of the tech firms was only restricted to developing technology, however, the exposure to applications and software has enabled users to understand the need for performance.   

Luckily, performance testing came to the rescue allowing IT solutions providers as well as enterprises working on business-specific solutions to create more dynamic products.  

The early phase of performance testing was limited to manual techniques only. However, the introduction of test automation frameworks and advanced testing methodologies have all changed things for good.  

And one such technology that is likely to redefine the entire future of performance testing, as well as other testing practices, is Artificial Intelligence. The authentic approach to performance testing works by analyzing the UI and developing test scripts that can work on fetching the right response time, CPU utilization, throughput, as well as memory utilization, etc. 

Since BugRaptors always aim at embracing modern solutions and advanced tech to leverage our Quality Assurance services, this blog will aim at highlighting the potential of AI technology in improving the performance testing practices for futuristic solutions.  

Let’s begin! 

Putting AI & Performance Testing Together 

In its simplest form, AI technology has got some very significant answers on improving performance testing benchmarks in terms of application designs as well as other benchmarks of quality. These include drawing insights on potential factors like: 

• Expectations from an application when in production 

• Likelihood of bottlenecks 

• Parameters to maximize performance. 

Since most advanced apps and tech solutions need a mature approach to performance testing, AI allows simplification of tasks like test designing, implementation, and all other creative measures to upgrade the process. To underline how AI could aid in performance testing procedures, here are a few points that might help you develop an understanding of the process:  

• As AI brings pattern recognition features, the data retrieved from load testing could be used to model the performance of the application under test. The inference made could then be used to anticipate problems related to system efficiency as well as the likelihood of failures to ensure optimum system behaviour can be harnessed.  

• Another significant benefit of using AI for performance testing can be an advantage to attain the SLAs more conveniently compared to human-powered systems. From the monitoring of the granularity to identifying the system complexity, it could allow QA testers to observe any bottlenecks that might affect the UX. Moreover, AI can even be used to enhance the predictability of the issues present at any tier of the app development process which might be left unobserved when worked manually.  

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• Last but most importantly, AI in performance testing could simplify tasks like scripting which can further aid the monitoring process to process real-time results, enabling the development of products with greater impact.  

On that note, let us quickly jump on exploring the broader perspective of how Artificial Intelligence can aid software testing procedures for developing products with richer performance.  

Artificial Intelligence, Performance, & Software Testing 

Artificial Intelligence driven into testing solutions contains all the potential to get over problems related to performance parameters. It works by encouraging the automation of three major operations:  

• Training 

This is the primary phase of AI implementation for software testing that can then be worked to counter performance issues. At this point, the AI algorithms feed on data such as codebase, interface, logs, and test cases while processing the code practices of the organization. Besides, AI at this stage works on understanding the expected behaviour of the elements and therefore leads the tests accordingly. 

• Test Case Generation 

Once done with training, the trained algorithm is then taken for generating test cases for various test parameters. These include accuracy, code coverage, and completeness of the test cases to meet specific performance goals. Once done, testers work on the output developed and aim at creating a more usable outcome. 

• Continuous Improvement 

Continual learning is all about continuously retraining the existing test process and modelling with new data to avoid any bias that might occur due to the use of the initial dataset. It ultimately aims at fetching greater accuracy and quality from trained networks.  

Applications Of AI In Performance Testing 

With insights on how AI could be integrated into the testing procedures, let us take a look at applications of AI in the automation of the performance testing process. 

• Unit Testing: Since unit tests are a vital part of any test strategy, especially the modern CI/CD integration goals, AI could be used to work over conventional template-based automated unit tests. AI can not only help in faster setup and deployment of the tests but can even aid in modifications for improved production. 

• Automated Maintenance: The test maintenance process is generally worked to process UI changes that do not break the test suite. However, larger projects make it difficult to track such changes and backing the process with AI algorithm could allow easy test fixing to keep the test process aligned.  

• Test Confidence: Another significant aspect of applying AI or ML-powered technology to unseen test data is fostering test confidence. However, it requires developers to work on capturing dynamic attributes and elements related to functionality and performance to feed the machine-learning model. Such systems could even be trained to handle any deviations and still yield the expected results without affecting the test process.  

• Bug Clustering: AI systems can allow the clustering of detected bugs in order to classify them on severity levels. The process works by identifying the bugs that need immediate attention which is further assigned to an assignee that has past experience in handling the bug. It allows end-to-end automation which includes detection, classification, and rectification of the identified bugs based on their prioritization. 

To conclude, AI carries everything that is vital to attain performance in software and applications. It not only allows for saving time and resources on the testing process but enables the testers and developers to focus on other productivity tasks that might help boost the ROI and sustainability associated with a product.  

All in all, artificial intelligence carries everything necessary to alter the landscape of testing and fasten the research and time goals for creating high-tech software. More importantly, AI has the potential that testers of the future need to keep up with the pace of growing requirements and development, ensuring they are not left behind.  

Understanding The Significance of Performance Monitoring

Read our Ebook: Performance Testing: An Equally Important Practice As Performance Engineering

Good luck! 

And just in case, you are looking for some expert assistance to help you with AI testing services, feel free to reach our experts through info@bugraptors.com, we would love to assist you.