AI + ML may seem to be an overused buzzword, but they’re essentially ‘put your money where your mouth is’ phenomena in software development cycles. They have increasingly made production simpler, faster and better. The global ML market is expected to grow at a Compound Annual Growth Rate (CAGR) of 42.8% from 2018 to 2024 (source:

Let’s understand the role of ML in software development. 

What is Machine Learning?

ML is a method of data analysis that is created with the help of AI to make software that ‘learns’ to make something smarter and enhance performance. Wikipedia defines it as “Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data.” The development of computer systems that can learn and adapt without following explicit instructions, through in-built algorithms and without human intervention. It is a way of getting computers to act, understand, build and predict like humans. AI + ML applications are widely used in image recognition, traffic/weather prediction, spam filtering, product recommendations and marketing, social media promotion, virtual assistants, online customer service, search engine result sorting and filtering, software for personalized medicine. These are only a few examples as ML has limitless potential and can be applied across sectors, tasks and projects depending on the goals. 

How is ML used in software development?

ML can change how traditional software development works at the core. Since machine learning entails that the system can function independently, developers can use it for various tasks like code optimization, testing and deployment. Automation of certain software development phases can free up the programmer’s time for more productive action items. AI + ML can help generate code if the correct requirements and inputs are fed into the system. Let’s explore the role of ML in software development in depth. 

  1. Foundational prototyping

Once the client requirements are identified and the core concept/idea is defined clearly, ML can extract the data, extrapolate from past and present models to create a suitable prototype for the current project, thereby reducing the time and effort of the legwork. Developers have to make a successful ML prototyping practice by collecting and exploring datasets, using domain knowledge for feature engineering. 

  1. Code structuring and review

Code needs to be clean and bug-free. ML can be used to review and restructure the code to make it more readable, consistent and even more performant with natural language style. Code needs to be upgraded for long term maintenance, which ML can achieve since it uses compilers to read programming language and automatically clean and debug or modify a code. Generating new code can take weeks and months, even with successfully planned sprints. But with ML, But ML can shorten the entire process to days. Prep runs, variable predictions, and training models built with ML-enabled tools can help create greater performance during pipelining. 

  1. Writing code

Computers can be taught to code with the right combination of deep learning and code structure recognition. Developers can create the source code, while ML can create subsets to accompany the main code, fill in the gaps through self-learning code writing, self-code low-level details, and covert diagrams into code. Automated ML-powered tools can identify code errors and get rid of ineffectual code. ML models can assess risk, detect anomalies and improve the process of authentication within users so that the digital product can provide security and privacy of data and help with fraud detection. Developers have found that ML can reduce thousands of lines of code into hundreds, thereby saving time and resources. It could mean that developers can leave a good portion of manual coding to ML tools and take on the more value-based work of analyzing, testing the result and creating curated and enhanced code. 

Although ML can use the capability to learn from experience or historical data to create short programs or ancillary programs, it cannot develop extended programs or end to end software development cycle, which can only be accomplished by human intelligence. 

When combined with symbolic reasoning and deep learning, AI and ML tools can learn from public and private GitHub or other repositories and optimize code by fixing overlooked bugs. ML can help with all types of statistical analysis and enhancement without changing or modifying the source code when scaling up the app or website, thereby enabling developers with decision making and app maintenance. ML tools can also autocomplete the code after inferring from the current code. Agile developers use ML during each sprint so that continuous delivery can be accomplished at each stage. 

  1. QA and Testing

Although the contribution of AI and ML with respect to autonomy is much lower currently, it’s expected to grow. They can help with the automation unit test generation and parameterization. Software testers can use ML to produce more accurate and refined results. Smart programming assistants can read technical documentation and debug code by sifting through massive volumes of data and self-correct code anomalies with the least human intervention. Developers are also able to create tests based on feeding data in simple English. Simultaneously, the ML algorithms carry out the technical specifications and reduce the time taken in building a full-fledged test manually. Also, the accuracy of the project-cost estimations can be significantly aided, helping companies cut downtime to market. The deployment phase is also eased through code release to production very quickly. 

Consistency is crucial when it comes to using ML in software development. Plan each stage of development concisely to incorporate the correct ML tools effectively. Full-fledged automation through ML is yet to be achieved. The use of a combination of supervised and unsupervised learning for Intelligent Process Automation (IPA) is the right approach to be adopted.