Machine Learning in Software Development - Techniques and Tools

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Machine Learning in Software Development - Techniques and Tools

We are currently living in the software era where almost everything is dependent upon software. Whether you talk about business operations listening to music or connecting with people, everything is being done through software. The world of software development has gone through a vast transformation.

The shift from mobile to the use of cloud platforms, software development covered a long journey of evolution. And currently, the most trending technological marvel being used in the world of software development is machine learning.

Machine learning is the scientific study of the statistical model and algorithm that is used by the computer system for performing specific tasks without the need for any type of instruction. The same concept is being used in the field of software development like in QuickBooks hosting. So, let us go through some of the top tools and techniques of machine learning that is being used in software development.

The top tools for machine learning

  • Kite – It is one of the most famous code completion tools that are available for most of the code editors and the best part is that it uses the amazing technique of machine learning for filling your code as you start typing in. You should know that Kite uses a working model that has been taken from GitHub. The GitHub code that is accessible for all, is used to make an abstract that acts as the basis of the Kite model.
  • Codota – It is almost like Kite since it also uses machine learning to generate automatic code completion. But you should know that it also uses a type formed from the syntax tree that is derived from all the publically available codes. Codota is built for Java language and Kotlin. It is a cloud-based solution that can generate smart auto predictions.
  • PROSE – It is a framework that has been created by Microsoft and it helps the software developers to generate code by using samples. PROSE stands for ‘Program Synthesis Using Examples’ and it can be used to create other programming tools, instead of implementing it directly as a predictive solution. The main base of this advanced framework is machine learning and the ways it can be used is a text transformation through example, data extract from a text file and file manipulation through prediction.

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The trending techniques of machine learning

  • Python – It is surely the default language for scripting the frameworks. You should know that in Python many kinds of models can be used, you can also create your model. Reinforcement Learning, Closed-loop and semi-supervised ML techniques are acting as a magic wand in various phases of SDLC. For example, the Closed-loop ML technique for SDLC can use unsupervised algorithms to test, train and validate ML models to enhance accuracy.
  • DevOps – There is no denial in the fact that DevOps is the answer to every question but instead of considering DevOps as an answer to everything, a better approach will be to always think in terms of providing a useful feedback loop. We always narrow down our focus to mechanics and ceremony without equipping DevOps in such a way that it finds value right from the metrics. But by including machine learning on the ops level, you will be able to wipe out the chances of analysis paralysis by giving developers the information that they need.

This is just a glimpse of how machine learning is being used in the software development world as its application is very broad. With the help of machine learning, software developers have been able to overcome many hassles, and therefore this marvelous technological innovation will continue to become a bigger part of the modern software development era.

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Jeff Morgan is a writer at NetworksGrid with several years of experience in the IT industry. She loves writing and exploring the world of technology while making the digital platform a more informative platform. Technology is what she eats and drink and she has a firm grasp on topics like managed IT service providers and managed NOC services.

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