How AI Powered Tools Are Bringing Revolution to Software Development? 

• Updated on: 2019-05-06
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Artificial Intelligence (AI) has impacted all industries and digital manoeuvres beyond measure. Software development stands at the forefront of receiving this huge impact, is no exception. As industry experts pointed out, every aspect of the software development lifecycle is likely to be benefited by AI and Machine Learning technologies. Right from predicting project timeline to software delivery estimation to bug fixing and coding assistance and testing, AI and Machine Learning can help to programme in multiple ways. 

AI and Machine Learning as of now have only been utilised through some tools built for specific development purposes. In the coming years, AI and ML can be part of Software as a Service (SaaS) package as well. Already AI-powered algorithms are taken up by cloud services, and now it is time for AI tools to get packaged through subscription-based services.

Before explaining the actual benefits of AI for software development process let us have a look at some real-world examples from the recent past about integrating AI into the software development cycle. 

Now let us explain the key ways AI can play a significant role in software programming. 

Any experienced mobile app development company that worked on too many projects of different length and complexities can easily give a near-perfect estimation of the delivery of the software. However, some factors like a lot of coding errors and accommodating client requests during the projects can lead to unwanted delay beyond the delivery schedule. Artificial Intelligence (AI) in this respect can help development companies give the most precise delivery time as AI based analytics, and Machine Learning can analyse a lot of data from similar types of projects. 

AI can ensure predicting more precise project timelines and deadlines by utilising all types of software project data including features, estimated timeline for each feature, employee profiles and track record, project timeline for similar projects, etc. AI can make a more precision-driven prediction compared to estimation by manpower that is more unlikely to take into consideration every detailed aspect. 

AI-based programming assistants are already gaining popularity among advanced developers. For example, Kite for Python programmers can provide timely support and suggestions just when they are trying to understand the documentation or carrying out debugging exercise. The tool can continuously help developers with relevant code examples and recommendations of relevant documents that can be helpful for the purpose and context. Such tools can help developers to reduce the unnecessary workload so that they focus more on essential aspects such as delivering better user experience.

Bug fixing is one of the most important aspects of software development that have got a complete makeover by AI. As the volume of data requiring to be tested increases exponentially, the chances of human errors and overlooked bugs also increases simultaneously. The bug fixing tools powered by AI and Machine Learning algorithms can just play a vital role here. The bug fixing tools powered with AI algorithms helps auto-correcting the codebase without requiring manual intervention. 

In software development, the role of compilers is to convert the code written with a high-level programming language into machine language or specific instructions for the hardware to follow. An intelligent compiler in doing this doesn’t need the original source code, and this makes the process faster. With AI, the code compilers can complete a task of regenerating code for the hardware in a few hours instead of months. 

The biggest role of AI in the life cycle of software development is played in testing. AI in software testing basically refers to the following things. 

In either way, AI plays a massive role in enabling development companies to transform software testing into a robust self-driving exercise with least manual intervention. As of now, most leading software testing tools started incorporating AI capabilities partially in their package. For example, both Eggplant and TestComplete tools in their recent releases included a few AI based features.

In spite of all the buzz and resounding efforts for AI to make our applications smarter, still, we are not in a position to give full autonomy to an AI tool for building a complete solution for our purpose. As of now, AI-based tools are just there to augment our efforts to develop and deliver a better product. So, while the role of app developers will continue to stay as important as ever, how better we can utilise these AI-powered tools will matter most.