Introduction
Up until the 21st Century, the idea of automating machines and giving it power to ‘think’ was deemed supernatural. However with rapid technological evolution, intelligent machines have become an integral part of our reality. Artificial Intelligence is the field that focuses on imparting intelligence to inanimate and sometimes intangible objects. These intelligent machines are then capable of making decisions and predictions depending upon history, loads of data and machine learning algorithms.
Businesses, having realized various benefits of AI, are focusing on building Mobile and website applications integrated with Artificial intelligence. The applications are not only smart but also faster, cheaper and interactive and one step closer to human interaction. Apart from this, Artificial Intelligence and Machine Learning Techniques are also applied to the entire development and testing of these applications. One such application, which is gaining popularity, is Performance Testing. Let us study this particular field of AI application in detail.
AI and its role in Performance Testing
Everchanging product demands and expectations of customers fuel the need to develop a better quality IT product. Performance testing is one avenue which facilitates meeting these customer demands. What is performance testing? It is a part of software testing which checks and analyzes how an application is performing under workload. It does so by scrutinizing the user interface and creating test scripts. These scripts are then compared for various metrics such as Response time, CPU utilization, Memory allocation, etc. The entire cycle of end-to-end performance testing requires constant manual inputs and extensive testing. There are quite some challenges that one can observe in this cycle.
These include
- Time consuming process of scripting a real user experience
- Data analysis of huge amounts of Data
- Identifying and eradicating a performance bottleneck
- Manual test case generation
- Manual failure root cause testing
Apart from these, there are many manual, time consuming tasks which require constant inputs from developers and involve a lot of expenditure of valuable resources. However, these challenges can be dealt with quite efficiently with the help of AI. Automation at almost every level of the application Performance Testing phase helps to deliver the user-friendly product to the consumers in the shortest time while using least resources and manpower. Here are a few more benefits of AI integration in the Performance testing life cycle of a product.
Advantages of AI powered Performance Testing
When performance testing, AI delivers on various advantages that are bound to facilitate the software and app development cycle. Here are a few more:
- Accurate analysis and delivery of Data – Manual Testing techniques have proven to be less reliable when it comes to accuracy and delivery of data. On the other hand, when automated with AI, there is less scope of human error and more precision in product development.
- Saving Time and Money – Testing processes are iterative. Which means every trial and error attempt needs reconfiguration of code and retesting. This leads to longer product cycles and wastage of resources. However, when AI is delegated to automate testing tasks, there is no overhead charge and it takes much less time compared to manual inputs.
- Intricate Error Detection – AI detects errors and discrepancies more in depth compared to humans. It also has a bigger coverage of huge volumes of data compared to manual analysis and bug detection. This eventually simplifies bugs and defects tracing which manual and conventional testing methods miss out on.
- Pattern recognition and Visual Testing – Pattern recognition enables AI to inspect and address visual bugs while ensuring that all graphic components are functioning as intended.By identifying user interface components down to the last pixel, Artificial Intelligence helps resolve visual errors. This adds on to the flexibility of software performance testing.
- Flawless API generation– AI enabled performance testing tools can also be applied to generate and check whether an API (Application programming interface) is performing as per instructions. It ensures that communication between API tools and databases is sound and regulates this time and again. These tasks when done manually take up a lot of time and personnel unlike AI which is efficient.
- Superior Test cases– Test cases provided by AI are preferred for their superior quality. Developers using conventional Real-world test cases that are quick to use and simple to govern will be provided by artificial intelligence. The developers are unable to examine more test case scenarios using the conventional approach. With AI, the developers can implement smart, fresh methods to test cases in mere seconds.
Top 4 AI tools for Performance Testing
Tools mentioned below are tried and tested and have proven to be very efficient when it comes to testing the performance of the product. Meant for different applications with different requirements, these tools are worth every resource spent on them.
Mabl
Mabl is a popular DevTestOps platform which makes it easy for users to create and automate user interface tests. Some notable features Mabl has are:
- Unlimited testing and scaling scripts
- Simultaneous Testing
- Cloud platform for security
- Self correcting tests scripts
All the above features make Mabl a must have tool for performance testing!
TestCraft
As an AI-powered performance testing tool, TestCraft is known for reliable testing when coupled with Selenium. With the help of drag and drop feature, TestCraft can easily build Selenium based products capable of automation. TestCraft is different since it generates a test model of one’s actual scenario.Such a test mode enables easy editing of the codein. Also any modification to the test case also reflects in the scenarios thus reducing time taken for the same. Choose TestCraft if you are looking for low maintenance, 4x productivity and easy, automated debugging of test scripts.
Applitools
Applitools is a prominent AI enabled visual apps management testing and monitoring tool. It is quite popular for delivering an end-to-end, intelligent performance testing platform which is preferred by all software development professionals. Adaptive learning algorithms are trained to screen applications and scrutinize them in the way a human does. This feature is one of the major reasons that Applitools is considered for testing applications involving visual testing.
Functionize
Functionize is yet another cloud based, AI testing technology which is known for load and performance testing. True to its name, Functionize uses ML and AI techniques to reiterate and improve functions enhancing test script creation, debugging and script maintenance. Natural Language processing (NLP) implemented by Functionize enables it to receive commands in plain english while creating working test scripts.
Best AI powered Performance testing practices
AI automation simplifies the testing process to an extent which is not attainable by manual testing. There are also certain practices one can follow to get even better results. Here are a few of them!
Test for users
When working with AI testing tools, we tend to forget the product is meant for users and often delve into stringent metrics more than required. While testing, ensure that you are checking the product which is being developed for users. One can use AI testing tools extensively but must keep the user in mind while doing the same.
Use the right AI Tools
There are many AI tools in the market that facilitate the entire performance testing process. It is better to use the right combination of tools best suited for one’s product. Conduct in-depth research and understand your requirements before using any of these tools.
Using AI to develop codeless performance test scripts
When developing Automated scripts for performance testing, create code-less automated scripts.These codeless scripts help remove errors and minimize time taken. The dynamic content of test scripts are also well taken care of.
Conclusion
Including AI in testing is revolutionary. Businesses and software development companies alike are swearing by the quality of the process and product delivered. There are many software development companies in the market that have already adopted AI for its testing measures and more are doing so everyday. To be able to automate the cumbersome testing processes is a boon and one can only assume it to be a popular trend in coming years.
Businesses looking to develop their own software product should also ask for these services so as to optimize their development process in considerably less time. OpenXcell is one such company which often employs AI techniques for its performance testing services. This has helped them deliver quality solutions in much less time and price compared to it.
