Artificial Intelligence (AI) has transformed every aspect of how businesses function, impacting all industries beyond measure, Software development processes stand at the forefront of this impact.

Not only do machine learning tools accelerate the traditional development lifecycle, but they also present an entirely new paradigm for technological innovation.

As of today, Machine Learning and AI have only been utilized through development-specific tools. However, experts believe that in the coming years, ML and AI will be part of the software as a service package (SaaS) as well.

Every aspect of the software development lifecycle will benefit from ML and AI technologies. Let’s take a look at some of them:

Precise Delivery Estimation

Experienced developers who have worked on several projects of different complexities and lengths can give near-perfect estimations of software delivery times. However, factors like changing business requirements and coding errors can lead to unforeseen delays.

Artificial Intelligence can predict precise delivery times through Machine Learning and AI-based analytics, which provide data base on similar projects. AI utilizes all kinds of software project data, including an estimated timeline for each user story, employee track records, and profiles, and project timelines for similar projects, etc.

Compared to estimation by manpower, the precision-driven predictions offered by AI take every detailed aspect of the project into consideration.

AI-based programming assistants

These assistants are quickly becoming popular among advanced developers. A great example of an AI-based programming assistant is Kite for Python programmers, which provides timely suggestions and support when programmers are carrying out debugging exercises.

The tool helps developers find related code examples, in addition to recommendations of relevant documentation. This reduces the workload for developers and helps them focus on core aspects of the software, such as achieving a flawless user experience.

Bug fixes

Bug fixing has been taken to new levels with AI. As chances of human error increase with the exponential increase in the volume of data generated, the number of overlooked bugs also increases simultaneously.

Bug fixing tools powered by Machine Learning algorithms and AI help testers autocorrect code without the need for manual intervention.

AI in Testing

AI plays a significant role in testing in the software development lifecycle. AI in testing typically refers to the following:

• AI-powered tools used in software testing
• Testing AI-based deliverables and products

AI has enabled software development teams to transform the software testing process into a robust self-driving procedure with minimal manual intervention. A lot of leading software testing tools are now incorporating AI capabilities in their recent releases.

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