Testing is a crucial part of the software creation process. It ensures that your code is working correctly and that all bugs are found before the software lands in the hands of consumers. But it can be difficult to explain these tests and their results to investors...
Technology has transformed remarkably in the past several years. This has caused organizations to rethink their software development process. Consumers expect smarter features that enable them to make every aspect of their life much faster and simpler than ever before.
With AI, companies have been able to deliver these features at a rapid pace to stay relevant in the market. AI has not only influenced consumer products but also seeped into the software development process.
Let’s explore some of the various advancements in AI that are impacting our software development lifecycles.
AI design assistants (AIDA)
One of the most time-consuming aspects of software development is narrowing down the requirements and design of a feature. The process usually goes like this: Stakeholders come up with requirements, requirements are converted into design mockups, and the developers start building the feature based on these mockups.
AI can help speed up this entire process through intelligent design assistants.
These AI design assistants (AIDA) take in several inputs from the user—the kind of application that needs to be designed, different taste preferences, category of business, type of customers and other relevant information. Based on these inputs, the AIDA automatically produces an initial version of the design. Then, the users work off the generated design to perfect it.
A great example of this is AIDA being used in building websites. The AI takes in different parameters from the user and automatically creates an initial version of the site in under two minutes. Working from this preliminary version, the user exercises drag-and-drop operations to come up with the final design that suits the business needs.
Automatic code refactoring
Having clean code is a necessity for long-term maintenance of application code and easier collaboration between development teams. Code refactoring is a practice that helps to achieve cleaner code, but it can be tedious.
AI can help by applying repetitive changes quickly, suggesting design patterns that could make the code more readable, identifying areas of the system that need improvement, and providing recommendations to take corrective actions. The code review process becomes a lot quicker, and the user gets notified proactively about different ways to make the code much simpler and easier to read.
Low-code development platforms
Integrated development environments (IDEs) have continually provided programmers with built-in features to ease the software development process. AI-enabled IDEs can optimize the development process and save time by detecting and learning different coding styles and making suggestions accordingly. Because the AI-enabled IDE provides much of the coding, humans can develop the next cutting-edge features at a much faster rate to meet growing customer demands.
These low-code platforms help nontechnical folks get involved in the development process by providing easy-to-use drag-and-drop functionalities, automated code generation, and straightforward integration with different code repositories and CI/CD pipelines.
Automatic analysis and error handling
AI models can make predictions on current behaviour based on what they have “learned” from past examples. Using reinforced learning, the AI can proactively identify common errors from system logs and generated code. Developers then get notified of the various vulnerabilities present in the application code and can take corrective actions. This proactive approach to fixing errors enables large enterprises to release features at a rapid pace.
In the future, the skills needed to build software are going to change drastically. The development process will focus more on data and less on the manual effort needed to develop features and deliver software. Instead of spending effort on writing, deploying and maintaining code, developers will be spending more time collecting, cleaning, labelling and analyzing data to be fed into neural networks.
AI is not going to replace software developers; rather, it will aid in gaining new insights into the development lifecycle, streamline processes and ultimately help us to write better code. It will also help in faster investigation of problems by proactively identifying them based on history. Some issues will automatically be detected and fixed in the background without the need for developers to touch the code at all.
AI is just another tool we can use to keep costs down and increase efficiency. The process of creating software will look different, but developers will still be needed to do what they do best.
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