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Artificial Intelligence (AI) is rapidly transforming all business functions from different industry verticals, and software development is no brainer. Not only can Machine Learning techniques give a significant boost to the traditional software development lifecycle (SDLC), but they also offer a completely novel model for inventing technology.
Traditional Software Gets a Significant Boost from Machine Learning Techniques
There is no need to fret about custom software development, since it is not going anywhere, however training a Machine Learning model would be helpful in productizing AI technology. A popular Google paper states that only a fraction of real-world ML systems bear Machine Learning code. Critical components such as Data Security, Data Management, and front-end product interfaces will still be managed by regular software. However, technologies developed using the traditional software development lifecycle (SDLC) can still benefit from ML approaches in the given ways below:
- Rapid Prototyping
Generally, it takes months, if not years for turning business requirements into technology products, however Machine Learning is reducing this lengthy and laborious process by allowing less technical domain experts to develop technologies using either visual interfaces or natural language.
- Intelligent Programming Assistants
You have probably noticed that developers spend a good amount of time in reading documentation and debugging code. With the help of intelligent programming assistants, developers can reduce this time, since these intelligent programming assistants will offer just-in-time support and recommendations, such as best practices, relevant document, and code examples. Examples of such assistants include Codota for Java, and Kite for Python.
- Automatic Analytics & Error Handling
Intelligent programming assistants can also learn from the past experiences to find out common errors and mark them automatically during the development stage. Once a technology has been deployed, you can use Machine Learning to analyze system logs and flag errors. In the future, we can also see that it would become possible to allow software to change proactively in response to errors without any human intervention.
- Automatic Code Refactoring
Sharp and clean code is a must-have for team collaboration and long-term maintenance. As business organizations upgrade their technologies, large-scale refactoring becomes almost inevitable and often painful necessities. In this scenario, Machine Learning can be used to analyze code and optimize it for performance and interpretability.
- Precise Estimates
Generally, it is seen that software development goes over timelines and over budget. Reliable estimates require deep understanding of context, deep expertise, and understanding with the deployment team. With the help of Machine Learning, one can train on data from the previous projects, such as estimates, user stories, and feature definitions- to predict budget and timelines more precisely.