5 Ways AI Impacts Software Development

Everyday software developers are challenged with multiple problems and tasks, from code improvements to reducing time to market. That’s why they are seeking for solutions to bring positive challenges and implement continuous integration/continuous delivery, automation tools, and DevOps. Some believe in the power of AI and prophesy the end of code.

Indeed, artificial intelligence has already influenced software development but the days when you tell a computer to create custom software are still far. What does artificial intelligence offer to software development right now? Let’s take a look at five potential use cases.

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Delivery Estimates

Working on the variety of projects, any experienced software development company get used to estimating delivery schedules within the accuracy of hours. However, such emerging factors as buggy code or change requests from customers may influence the estimate. With that said, artificial intelligence will help overcome these factors due to collecting more data from multiple sources and machine learning from the similar projects which are likely to improve the quality and precision of delivery schedules.

Project Management

Even today, AI may impact project management by focusing on narrow areas rather than embracing the whole process. For example, sprint management, budget management or team members onboarding with the consequent data collection, machine learning and actionable insights based on the analytics gotten from these data.

Over time, AI power will extend over the narrow tasks and use data points to link lines of code and changes made to it with the people and tasks related to it. As a result, this may help human project managers to improve project performance.

Declarative Programming

Developing software powered by machine learning or natural language processing will require another approach to the code building than imperative programming. The new model will require writing code in a natural language style. It is called declarative, as it declares any functional and technical requirements to the machine learning mechanism which will train models according to the rules and constraints a developer gives to them. In other words, a developer will create code by telling what to do rather than how to do this.

This will enable developers to create applications using their voice by speaking what they need. The rest will be done by the algorithm.

Code Review Automation

So far, developers have had some automated, intelligent code analysis tools at their disposal. In future AI-based software development will employ code commenting blocks and analyze the vulnerability of code (some solutions are already getting tested by AI developers like Elinext). What is more, they will fix the emerging issues and do PR review. A developer will get a report with descriptions of each detected problem with the references to the source code.

Quality assurance

As far as testing routines, the mechanism of AI involvement will be exactly the same: the algorithm will detect bugs in code and automatically fix them. Human quality assurance engineers will get the right data to make more meaningful decisions which will make them observers in the process of software testing and quality assurance. Their only task will remain teaching AI to execute the tests.

AI Software Development – Over To You

It seems like software development stands on the threshold of the new era which will transform the traditional way of coding and eliminate human mistakes. However, there’s no reason for developers to have a fear of losing their jobs, quite the opposite, they should embrace these intelligent solutions to become better specialists. In future, the role of the developer may shift to working with algorithms and ensure more effective and cost-saving outcome.

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