As the world is pumped up in exhilaration with the advent of AI, performance engineering is also zealous towards its application.
Now before we are headed towards the application, let us take a quick look at the basics.
So, “What does data mean?”
Data is bits of factual information, mostly numeric, collected through multiple observations. And it can be the basis of AI build-upon machines.
Therefore, if the question of the application of AI to enhance performance engineering arises, then data has a lot to do with it as well.
Performance Engineering mainly focuses on:
- delivering the best user experience to one’s customers, despite load or weather conditions,
- implement performance throughout every stage of Software Development Life Cycle (SDLC).
- testing of speed, scalability, responsiveness, response time under varying workloads.
Henceforth, keeping a meticulous eye on the data and its fixing can be a cumbersome procedure. On top of that, the cost of fixing can turn out to be 30 times more than its building up.
Recent studies suggest that 41.02% of the users are likely to leave a website if its load time tends to be more than three minutes. So there is great pressure on performance engineering to place everything right.
In this case, AI is a boon to performance engineering. This would enable just a ‘go’ button on the software to make ready all the testing and its results ready. Here are the two most important steps where AI proves itself better than human testings:
1.Track users better.
User action i.e. what the user would do next has been the guesswork of the testers until recent times. What action does the user take or how often or how much time is required; all are part of a long process!
Instead, all the data should enter into an ML tool aided by AI for processing, which would enable the testers to access information faster.
2.Enhance performance in production.
A huge amount of data is required in production. Also, systematically arranging data to extract the required information is a must.
Here too, ML could be your knight in shining armor. ML can utilize data to predict trends. ML charts can predict performance problems earlier to production. Hence, it could be possible to test the fix much earlier in the production process, which would minimize the cost of fixing.
ML with the help of AI can work beyond the scope of search engine with data from various sources. Increasing the credibility of your performance engineering has become faster than ever before. Predicting trends, fixing bugs and more now possible at a swift without interfering customer’s experience. Thanks to AI for being yours and your customer’s best pal.