The Revolutionary Role Of Machine Learning In Software Development

In a world where cutting-edge tech is a pre-requisite and not an add-on, Machine Learning has had a profound impact in the software development sector. It has been seen as a portal into the future, where Artificial Intelligence and Machine Learning will act as a game-changer in software engineering and development.

What exactly is ML?

To a layman, you can say that Machine Learning refers to a system of algorithms designed to help a computer ‘learn’ from experience, just like a person. The learning would be automatic, making the machine more customized to the user. So, essentially, machine learning in software development will contribute by making a program smarter.

A good example of this would be a function getting better with machine learning - such as facial recognition, voice recognition, driving, speech-to-text, etc. These functions get better as they are tested in different stimulations and refined to suit the target users. While in reality, the system is just using the new data which is getting stored in it every second, it actually gives the observer an illusion of ‘machine learning’.

Artificial Intelligence or AI is based on this principle of machine learning. It is difficult to have an AI setup without machine learning.

How is it helpful with software development?

A very important, and virtually irreplaceable element of software development is - software testing. It is the process that ensures that a product is functioning the way it is supposed to. Again, to put it in layman’s perspective, you can say that software tests are the ‘trial runs’ of software development. They happen a number of times, at different stages.

The main purpose of software testing is:

• Identify and neutralize any bugs before they cause a problem
• Evaluate the capacity of a product
• Test speed and performance in different situations

This is basically the maintenance of the product as it is built. For instance, if you are building a table, you will probably fix the wobbly leg before you start with varnishing it, rather than trying after the table is fully made. If a product is built without testing, that wobbly leg could result in a complete collapse. A seemingly minor issue could result in the failure of the product. This is why software testing is absolutely crucial.

Machine Learning primarily contributes to this process of testing in software development. Up until the past decade, software testing meant a lot of manual work. Observation, coding, and inputting desired behaviour was involved. Now ML is being used to automate this manual process.

Used in combination with continuous delivery methods, such as the agile methodology. This means that it is not used on the entire product after its development. Machine learning is utilized at every step so that before the product is developed further, any glitches from the previous stage is eliminated.

How is machine learning revolutionary in software testing?

It’s simply faster!

Human intervention cannot be faster than ML technology. Automated tests are faster and effortless. It is simply easier than a group of software engineers looking for a small error in a program.

It keeps going, even if you don’t

There is no worry about developing the software continuously. Quality Assessment testers are available only for a short period of time. ML-based testing will keep moving at the speed of software development, thus not putting the project at hold.

Consistency

To be able to test for a function in exactly the same environment can be difficult manually. ML algorithms, however, are built exactly for this. They will execute the same process without any discrepancies as many times as necessary.

Detection of minor anomalies

Small details that might be missed by the human eye can be picked up by an ML. Positions, colours, visual interface, can prove difficult to free from bugs. ML is far more accurate when it comes to minor details.

What is the software development sector waiting for?

Machine learning is a promising revolution in the future of software, however, the levels that full-blown automation requires are still nowhere near. ML-based software is being used but only to identify errors, and not remove them.

It is improving as more and more developments take place and saving time while doing it. The coming decade is expected to show a lot more promise in the field of ML and reshape the world of computers and software development.

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