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Harnessing the Power of AI in Performance Engineering
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Harnessing the Power of AI in Performance Engineering

Performance engineering is the backbone of every software development company. All efforts of an organization will go futile if Performance Engineering is not executed robustly. When a product falls due to performance-related issues, it also takes down the brand's name with it.

Poor Performance Engineering dramatically impacts the organization's bottom line. A glitch of even a few seconds can cost millions of dollars to the company. According to Kissmetrics, 47 percent of users expect a webpage to load in two seconds or less and 40 percent of the users will leave the website if the loading time is more than three seconds!

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Benjamin Franklin once said, “An ounce of prevention is worth a pound of cure”. Ideally, it is good to identify the errors during the initial stages of the launch but it's still a win if we can detect and eliminate the issues before they impact even a single live user. It might sound too ambitious, but when done right, it is achievable.

The magic called AI

Undoubtedly, Artificial Intelligence is one of the hottest buzzwords in the tech world. It won't be an overstatement if we say AI is humankind's most transformative technology ever. It makes the impossible possible. 

AI-driven innovation and frameworks are sweeping the industries across the globe and transforming the ways businesses operate. It is empowering companies to effectively tackle human limitations and efficiently perform tasks. It eliminates the need for humans to perform tedious tasks and creates a space where employees can take up more interesting and innovative works. Artificial Intelligence opens a plethora of opportunities for businesses and gives them a competitive edge over others. 

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AI trends and Performance Engineering

‘If you don't change with time, the world will leave you behind.’

Keeping up with trends and setting new benchmarks for quality and performance is a must for any IT firm to sustain itself in the digital era. Even the slightest lapse can take a toll on the company's standing against the competitors and severely hurt the business’s reputation. 

To avert the embarrassment, organizations invest a huge amount of time and effort in organizing production logs and getting the trends right. However, the amount of data can be overwhelming, and days can go by in reading and predicting the market trends. Besides, they are highly volatile and are subject to user behavior and technological advancements. Also, a lot of guesswork goes into predicting trends and designing the future course of action which isn't that reliable.

In the light of its complexity, divergence, and size, it is difficult to gauge the market movement through manual means. Conventional methods are vulnerable to errors and can render misleading information. However, a cultural shift towards a cloud-native mindset can save the boat from sinking.

Harnessing the power of AI

To obtain accurate trend assessment, performance engineers must work with the right data. AI makes the rendering of reliable data easier and faster. Machine learning algorithms can be composed with specialized logic and rules that can capture the minutest of fluctuations and quickly identify (user and market) patterns. They can (almost) immediately curate high-quality data and filter the information as per the business requirements. 

Artificial Intelligence solutions are fast at processing humongous volumes of data. There are various APIs and engines that can acquire data from various platforms such as social media, app stores, customer support logs, etc. In the blink of an eye, AI-powered tools can run all this data through pre-set logic and deliver real-time insights into a product's performance and mitigate risks associated with poor performance. It helps businesses analyze user behavior better, evaluate strategic plans, and recognize data patterns. The insights that AI tools give are invaluable to organizations. They use the insights to ascertain users' choices and preferences and provides tailored information.

Overall, it enhances the quality of performance monitoring and testing and spares humans from tedious tasks. 

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Anomaly detection and AI

Defining metrics

It's important for performance engineers to identify anomaly, but more important is to know - 'what accounts for an anomaly'. Something that's an anomaly for one can be an intended feature for another. The definition of 'normal' and 'anomalous' activities varies from product to product and from organization to organization. Therefore, performance engineers need to set very specific metrics that complement their business needs. Metrics and thresholds are generally defined on parameters, like, CPU utilization, stability, and scalability, frequency of updates, website traffic, memory consumption, reliability on live and historic data, etc..

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Performance engineers compare the system against the threshold and check if it is meeting the requirements. If they find a threshold violation, they raise an alarm to the developers and assist in the solution development. However, the process sounds simple only on paper. In practice, the manual way is susceptible to human errors and often raises false alarms. The tech world advances at the speed of light; so, by the time the traditional way detects an anomaly, it is entirely possible that its definition and scope has changed. To cope with the issue, businesses need to move to Automation and AI as the way forward.

AI and baseline

AI-powered solutions can help predict the performance issues, fix them, and even avoid them entirely. They are smart and time efficient. They help automate business processes, unseat humans from dull tasks and give insights through data analysis. However, for insights to be valuable, it’s important to do away with a static baseline. 

A system is dependent on variable values and today’s tech world is highly dynamic. Under such a situation, it's futile to rely on a static reference. AI, on the other hand, has an incredible quality of auto-updating itself and keeping the team posted with the latest developments. AI algorithms that are being trained for supervised learning can adjust performance thresholds to match the real-time scenarios and help the performance engineering team in rightly measuring the effectiveness of the product.

Moreover, it can be used in dealing with the problem of ascertaining the cause of an operational error or a software failure. Despite running checks, multiple times, a few errors may remain a mystery to the team. Here, AI-powered solutions act as ‘messiah’ and quickly go through chunks of data to flag potential threats. 

AI has a sharp eye for recognizing patterns and directs attention towards any sort of abnormal behavior. Before pushing the application to production, it is highly recommended to run it through an AI framework. The practice can save the team from potential embarrassment and support in building AI-driven solutions. 

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Overcoming the challenges of ‘Rule of thumb’ with AI

While checking the stability of software applications, performance engineers often apply the ‘rule of thumb’ approach. The method is a by-product of personal observations and past experiences. Meaning, it highly depends on subjective judgments rather than objective sources or verifiable data.

The major drawback of the ‘rule of thumb’ approach is that it gets dated before we even realize. What worked in the past may not work again. Subsequently, it’s wiser to incorporate Artificial Intelligence with subjective assumptions for more reliable outputs. AI-driven models can provide engineers with near to accurate data and enrich manual performance check-ups. Performance engineers can further fine-tune the insights to adjust them to the business requirements and develop effective solutions.

AI: The Gamechanger

Artificial Intelligence is no longer a fantasy of science fiction. AI has revolutionized nearly every aspect of the IT industry, and Performance Engineering is no exception. It has significantly reduced human errors and discharged humans from mundane tasks. 

Inducing Artificial Intelligence in performance engineering isn't a luxury, but a necessity. It is an essential practice to stay agile, effective, and stay ahead in the competition. AI empowers an organization to take a more pre-emptive approach towards performance optimization; resulting in a system that not only provides a great user experience but exceeds their expectations.