According to the market research firm Tractica, revenue from the application of artificial intelligence (AI) tools worldwide is expected to reach $119 billion by 2025.
There is huge interest in the application of AI in software development, with the demand for AI tools, apps and platforms growing alongside cloud adoption. A survey by Forrester showed that development and delivery teams are confident that AI in software will improve development, test automation, and the end product.
Here are some ways that AI will boost software development.
Planning and designing a project from scratch requires UX and Development leads to apply their training and experience to come up with the best solutions. This can be a daunting challenge and whilst iterative development goes some way to mitigating risk, there are efficiencies to be gained by getting it right first time.
By training an AI to understand user requirements, it is then able to select from a vast number of UX and development patterns to create an appropriate solutions.
AI tools can be used to create test information, explore information authenticity and testing scope, and help with test management. Trained right, AI can ensure the testing is error free. Human testers freed from repetitive manual testing will have time to create new automated software tests with better features.
Also, if software testing is repeated each time source code is changed, re-testing can be costly and time-consuming. AI can be used to automate the testing, and ultimately be used to improve software quality. Some AI can automatically generate fixes for specific bugs and propose fixes to developers for approval and deployment to production.
User Interface (UI) testing requires the tester to perform huge tasks that include manually developing test cases, identifying the conditions to check during test execution, determining when to check these conditions, and finally evaluate whether the UI software is adequately tested.
Currently UI testing requires the tester to perform huge tasks that include manually developing test cases, identifying the conditions to check during test execution, determining when to check these conditions, and finally evaluate whether the UI software is adequately tested. If the UI is modified after testing, the tester must change the test suite and re-test. As a result, UI testing today is resource intensive and it is difficult to determine if the testing is adequate.
AI can automatically test whether visual code is functioning properly or not. It enables users to test their visual code just as thoroughly as their functional UI code to ensure that the visual look of the application is as you expect it to be. AI can allow users to test everything from multi-screen layouts to functional behaviour of their application and its visual look.
Coding a huge project from scratch is often labour intensive and time consuming. An AI programming assistant can radically reduce the workload. To combat the issues of time and money constraints, researchers have tried to build systems that can write code however they can get stuck with ambiguity.
This is where AI can be a huge help. It can be used to extract knowledge from online source code repositories such as GitHub. The idea is that the AI trains an artificial neural network to recognise high-level patterns in hundreds of thousands of programs. It does this by creating a sketch for each program and associates this sketch with the program’s goal. The aim here is for AI to make programming easier and less error prone.
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