{"id":13586,"date":"2019-02-21T18:04:25","date_gmt":"2019-02-21T12:34:25","guid":{"rendered":"https:\/\/cigniti.com\/blog\/?p=13586"},"modified":"2022-01-13T19:12:01","modified_gmt":"2022-01-13T13:42:01","slug":"need-of-ai-based-testing-platforms-for-applications","status":"publish","type":"post","link":"https:\/\/www.cigniti.com\/blog\/need-of-ai-based-testing-platforms-for-applications\/","title":{"rendered":"Making AI-based Testing platforms relevant for super-busy user applications. What’s the value?"},"content":{"rendered":"

An article in Forbes by Bernard Marr, an internationally best-selling author mentions, \u2018The integration of\u00a0artificial intelligence\u00a0and the financial industry<\/a> has always been a match made in heaven\u2014high volumes, the quantitative aspect of finances, need for expediency and accuracy are ideal for the unique skill-set of AI.\u2019 These and many such related benefits of AI, makes its adoption indispensable across diverse industries and domains. Within the Software Testing domain, AI-based testing platforms are being adopted for ensuring accuracy, bringing speed, and enhancing the performance of the QA process. How can we estimate its benefits as far as high-speed and super speed applications are concerned?<\/p>\n

The article on integrating AI and the financial industry goes on to speak about the inherent intellect of AI platforms, \u2018Artificially intelligent machines analyze inordinate amounts of data at extraordinary speeds that is impossible for humans. They learn from the information they analyze to improve their trading acumen. This information includes market prices to corporate financial reports and accounting documents to social media, news trends, and macroeconomic data. Once the information is analyzed by thousands of machines, the machines then \u201cvote\u201d on what action to take and the best trades to make.\u2019\u2019<\/p>\n

Self-learning capabilities, in-built intellect, predictive capabilities, analytical decision-making, and downright accuracy are some compelling reasons for considering AI-driven software testing platforms. That being said, a lot still remains to be explored in this sphere of testing methodologies. Knowing the challenges that application development involves, AI can definitely bring a lot of value for applications that need constant data support and intense predictive capabilities.<\/p>\n

What are these super-busy applications?<\/h2>\n

Any industry that needs constant engagement with the end customers, needs data analysis, predictive strengths to deliver an immediate forecast, and many such smart capabilities are required to build an intelligent application. Some of the prominent examples from the industry are Trading applications, gaming applications, and sports applications. These applications need to be tested not just for their functional and performance efficiency, but also for their predictive and analytical capabilities.<\/p>\n

A recent report on best Cryptocurrency Trading applications<\/a> mentions some key areas that the application must cover, \u2018While choosing a platform for trading, it is always important to consider the availability of a mobile version, as it will allow you to get rid of an asset or, on the contrary, buy it at once. The functionality of mobile apps, as a rule, is not too different from the web version. The cryptocurrency trading program is also provided with a quotation schedule, the ability to deposit\/withdraw funds from the balance, make an order to buy or sell. In order to start\u00a0trading cryptocurrency\u00a0from your device, usually it is only needed to download an app from the App Store or Google Play, install it on your smartphone, and then log in to your account. It is also worth mentioning the two-factor authentication system, which is likely to be required to verify identity.\u2019<\/p>\n

Digital Assurance and Testing<\/a> with a mix of smart automation is needed to enhance and keep upgrading the quality of the application. How can AI as an automation and testing platform further augment the Quality Assurance efforts?<\/p>\n

What can AI-driven testing platforms do for highly-engaging applications?<\/h2>\n

Author Bernard Marr in another article talks further about application of AI<\/a>. He mentions, \u2018Research into applied, specialised AI is already providing breakthroughs in fields of study from quantum physics where it is used to model and predict the behaviour of systems comprised of billions of subatomic particles, to medicine where it being used to diagnose patients based on genomic data.\u2019<\/p>\n

Reference to the industry \u2018it (AI) is employed in the financial world for uses ranging from fraud detection to improving customer service by predicting what services customers will need. In manufacturing it is used to manage workforces and production processes as well as for predicting faults before they occur, therefore enabling predictive maintenance. In the consumer world more and more of the technology we are adopting into our everyday lives is becoming powered by AI \u2013 from smartphone assistants like Apple\u2019s Siri and Google\u2019s Google Assistant, to self-driving and autonomous cars which many are predicting will outnumber manually driven cars within our lifetimes.\u2019<\/p>\n

These are some of the key reasons why AI has been adopted<\/a> even in the application testing cycle. It is business critical for testers to ensure that the application delivers the expected functionality, performance, and secure interface to the users. This requires adoption of flawless automation platforms to not just bring velocity, but also precision within the testing cycle.<\/p>\n

Whether it be the health, finance, or entertainment sector \u2013 every industry is trying to innovate and use AI-based apps that help automate tasks. This makes testing the apps for automation a business-critical activity. However, there are multiple testing related challenges that organizations may face while leveraging AI for testing apps for quality<\/a>, such as:<\/p>\n