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How Machine Learning Is Changing Software Development

Artificial intelligence is rapidly gaining popularity across all business sectors, and software engineering is no exception. Machine learning models can be used to expedite the software development life-cycle and, what is more, they offer a completely new paradigm for inventing technology.

Traditionally, application development needs advanced requirements specification of what exactly should be created and then have all of the features hand coded. Though computers were powerful enough even before the advent of artificial intelligence, some tasks were too complex to teach to them in a rule-based way.

Even tasks that seem to be pretty simple, like identifying an object in a photo, can’t be performed using the traditional software development approach. Most engineers are unlikely to list all the rules that would reliably recognize the object in the picture.

ML Will Change the Way Software Is Created Forever


Machine learning eliminates the need for giving computers the rules for how to make decisions and take actions. A developer prepares data that is later fed into learning algorithms and the system discerns important patterns from the data. Moreover, ML algorithms can even find out patterns or details that the developers haven’t even thought of.

Areas Where ML Will Play A Crucial Role

Strategic Decision-Making

Development teams usually spend considerable amount of time discussing which features should be prioritized and which ones should be ignored. Machine learning can speed up the decision-making process by analyzing the success of past development projects and, thus, help stakeholders and development teams make data-driven business decisions and minimize the risks.

Precise Estimates

Software development often goes over budget and the deadline. To provide accurate estimates the team needs profound experience and understanding of the context. Machine learning can analyze data from the past projects, such as feature descriptions, user stories, forecasts, and provide a more accurate budget estimate.

Rapid Prototyping

Usually, it takes months to turn an idea into a product, because you have to go through many stages, from brainstorming to wire-framing and creating a product prototype. When it comes to developing software, machine learning has the potential to cut down on the time spent on prototyping products from weeks and months to mere days. Besides that, with machine learning, fewer technical experts are required to create software.

Code Review

Clean code is essential for long-term maintenance and team collaboration. With companies evolving their technologies, large-scale code refactoring is unavoidable. Machine learning technologies can be used to automatically review the code and optimize it for performance.

Compilers can fix old code without requiring the original source. Compilers are programs that process and translate computer code written in a high-level programming language into a machine language that can be read and executed by a computer. They automate the task of fixing old code and make the next generation of code faster. It would take a developer about three months or even more to perform this task, while a compiler can do it in a few days.

Testing Tools


Software testing is a straightforward task. As long as we know how the system is supposed to behave, it is relatively easy to enter input and compare the results with the expectations. A match means that the test is passed. In case of a mismatch, the bug has to be fixed by starting all over again. In such a traditional scenario, a tester has to go through the checklist to make sure that all errors are fixed. However, the market is getting more competitive and customers are becoming more and more demanding, traditional testing methods are not enough to keep up with them.

Machine learning lets software testers come up with more accurate results and significantly reduces the probability of errors. In addition to that, it takes less time to run a software test and find a potential bug, while the volume of data that needs to be processed can still grow without any additional burden on the testing team.

Bug Fixing

This is one of the areas that machine learning has an impact on. Given the overlooked bugs due to human error and the massive volumes of data to be analyzed and tested, machine learning algorithms can auto-correct themselves with minimal human intervention and, thus, make it easier for developers to create software.

Smart Programming Assistants

Developers spend a great share of their time reading technical documentation and debugging code. By providing just-in-time support and guidance, such as related text, best practices, and code examples, smart programming assistants can significantly shorten this time.

Besides that, programming assistants can learn from past experience in finding errors and automatically flag them during the development process. Machine learning can even be used to analyze system logs to identify errors. It is expected that in the future, machine learning will let software adjust in response to errors without human intervention.

Bottom line

Overall, machine learning can have a significant impact on software development. Software development companies need to seriously consider the impact of machine learning and the potential benefits it may offer, not just in the way of building applications but also in the nature of software itself. Undoubtedly, machine learning will prove to be a game-changer in software development.

Planning to transform software development in your organization with the help of machine learning? There are many ways in which machine learning can change software development in your organization, however, it may be hard to deliver a complex project. Contact us for help!