How To Achieve Continuous Integration Through Machine Learning And AI?

December 12, 2019

Whether it is to shop online or streaming music, smartphones and their apps are the most necessary part of our life these days. There are more than 3.2 billion smartphone users today, which is almost half of the total population on earth.

But, where do these apps come from?

These apps are built by developers and app development companies for business, enterprises and many times just for fun! While this might look easy to write and read through, developing an app and maintaining it through the application lifecycle takes much effort.

So, how can we minimize the effort to manage the application lifecycle?

Technologies have evolved and with the techniques of Artificial Intelligence and Machine Learning capabilities, Application Lifecycle Management(ALM) has become more efficient and easy by Continuous Integration(CI).

Let us explore the AI and ML usage in the CI paradigm. But, before that, we need to understand the CI and its related functions.

What Is Continuous Integration? 

Continuous integration is a practice used by developers and app development teams to reuse the common logic code and continuously update it with newer codes automatically. 

In simple terms, instead of writing codes for every new version, a repository is set up and developers write codes and save in these repositories, Now, an automated system is developed that continuously deploys the code as per the necessity and tests the version too.

But, it is the automatic part that is the biggest concern here. As far as human intervention is concerned, continuous integration through the human intervention takes much time and effort and the same can be reduced through automated systems backed up by advanced technologies like AI and machine learning.

What Is AI And ML?

AI is definitely Artificial Intelligence and it is not a singular technology, but a concierge of many different technologies including ML-Machine Learning. So, they are not quite different from each other. Machine Learning is the technology that has revolutionalized automation in almost every business today and the same has been with the app development paradigm.

How To Use ML For CI In Application Lifecycle Management?

Machine Learning(ML) uses algorithms to learn and execute data from the specified and sometimes unspecified resources. These algorithms use Natural Programming Langauge to interact with systems and learn through training or inferencing.

In simpler terms, ML models and algorithms are trained to do a particular job and in some methods, ML models learn themself, by self-learning modules. Now, as we know that the CI is achieved through the automatic deployment of codes stored in repositories. ML models need to learn or to be programmed to interpret the new data and include the same into the common logic to execute a newer version of the application.

So let us see the step by step implementation of ML models for CI based on different forms of learning:

  • Supervised CI

Supervised CI comes from the supervised learning method of Machine Learning. In supervised learning, the algorithm or the method is provided predefined metrics or parameters to learn from the data. It can be considered as programmable learning.

Take an example of an app developed by a mobile app development company to provide a ride-booking facility online and for this, the automated integration algorithm is provided with metrics like surge prices, luggage prices, etc. So, whenever there is a surge price updated by the drivers or riding partners, it is automatically integrated into the pricing estimation provided to the users.

  • Unsupervised CI

It is quite the opposite of supervised CI, as the algorithm is not mentored or programmed to learn from a specified data set. But, rather than that, it is allowed to learn from the flow of enormous data through trial and error. Here, the algorithm truly portrays an intelligent part as there is no human intervention and integration of the newer data is completely unsupervised and automated.

As we saw in the earlier example, there are no specified metrics for a price estimation in this algorithm and the ML model can itself learn from the driver’s data and different ride data to calculate an estimated price itself and provide to the customer without any interruption from the developer. Such a model can help ride-hailing companies provide real-time price estimation to riders through an app’s API designed using an uber clone. This method though can be a little tricky as there is very low to none supervision over the execution of data by ML models.

  • Reinforced CI

This method is almost similar to supervised CI, the difference being, in supervised CI, the algorithm knows what it should learn and what is the answer to it? But, in the case of Reinforced CI, it reacts to the data based on situations and real-time data. It provides real-time responsive apps to developers through the integration of the data instantly based on the necessity derived through its own intelligence.

So, now your ride-hailing app will itself know when to charge a surge price and when to charge the 1x, 2x or 5x pricing based on the peak hours, depending on the time, fuel charges and other real-time data.

  • Mixed CI

The best way is to use a mixture of these CI models based on machine learning algorithms. As it will reduce the possibilities of errors and provide smoother integrations of new iterations of your application. Mixing these models can render integration through specified metrics with the incorporation of different scenarios and yet apply the learning of a wide array of data into continuous integration.

Wrapping It Up

Continuous Integration is the most vital part of automation in application development and lifecycle management. Another aspect of this automation in testing automation. As the CI moves towards continuous development through ML models, the same technology can be used for automation of the testing of applications.

Now, that you know how to use AI and ML in Continuous Integration, Testing automation and Continuous Development, planning and managing the application development and achieving effective Application Lifecycle Management(ALM) will not be a tedious affair anymore. Yet, understanding your business capabilities and functionality to integrated these technologies into your organization’s infrastructure is vital to your plans.

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Andi Perullo de Ledesma

I am Andi Perullo de Ledesma, a Chinese Medicine Doctor and Travel Photojournalist in Charlotte, NC. I am also wife to Lucas and mother to Joaquín. Follow us as we explore life and the world one beautiful adventure at a time.

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