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How Artificial Intelligence Affects Software Development

Posted December 15, 2019

Ever since its inception, artificial intelligence has been disrupting different fields and industries.

Software development is no exception, as there are numerous parts of this complex process that can greatly benefit from introducing this emerging and powerful technology. Supported by big data and cloud computing, AI impacts every part of the software development lifecycle, from conceptualization all the way to deployment and maintenance.

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The potential of AI in this field can be best illustrated by the fact that experts in Google managed to teach machine-learning software to create machine-learning software.

Simply put, artificial intelligence and its subsets can be expected to speed up software development, as well as to make it easier and more reliable.

Collecting Data and Requirements

This is one of the stages of the software development lifecycle in which human intervention is still a must.

At the moment, the initial stage of SDLC operates in the following manner: a software business analyst and a business executive discuss the software and come up with all the requirements and features. After that, a programmer writes the code that corresponds to these requirements.

However, with the help of natural language processing (NLP) and machine learning, the task of developers will be to collect and prepare domain-specific data and feed it to machine-learning algorithms.

This new approach will not only automate and facilitate the process but also result in models that are constantly trained and improved. Such a machine-learning model is capable of identifying software features that are important to users.

Although agile development significantly accelerated the traditional software development lifecycle, all components, including features, functionalities, and integrations have to be manually managed and updated. Needless to say, this usually leads to numerous bugs and inconsistencies due to the complexity of the system.

With machine-learning models, everything will be automated which means that human error will be eliminated.

That’s why this shift from agile to AI development is much more dramatic than that from waterfall to agile.

Some of the benefits of this new approach are:

  • Significantly easier to manage
  • Easily implemented into hardware
  • No downtime
  • New developers can learn it faster
  • More effective and efficient than any human coder.

Recent surveys corroborate all these points as 54% of executives say that AI solutions they implemented in their businesses helped them increase productivity.

Modernizing the Traditional Approach

Still, the advanced technology mentioned above won’t completely remove the traditional approach from the equation. As a matter of fact, certain essential components of this process, including data management and front-end interfaces will have to be handled by traditional software.

However, even traditional SDLC technologies will receive a boost through machine learning.

Quick Prototyping

When it comes to building software, speed plays an important role. The faster your product hits the market, the sooner it will start bringing you conversions and revenue.

That’s why it’s important that every step of the process is optimized. Given that it takes years to put a business idea and requirements into a final product, the fact that machine learning can cut down on the time spent prototyping to months or even days is a tremendous improvement.

For example, this advanced technology will allow companies to build high-tech prototypes from low-res sketches – something that Airbnb has been using for some time.

Using Intelligent Programming Assistants

These AI-powered algorithms can help developers when it comes to writing code.

Namely, as this is a time-consuming process, it’s quite useful to have something like smart code autocomplete suggestions.

Thanks to Kite, an AI-powered code autocompletion tool, developers can rely on something to pick up the slack. This tool’s recent feature is also capable of generating an equivalent of the entire sentence composed of “tokens” in Python. Also known as Intelligent Snippets, this feature doesn’t require any manual defining of the sentence in advance – it will pick up every particular developer’s coding style and adapt to it.

But, what for those who are trying to develop Java backend, for example? Codota completes lines of code by processing and analyzing millions of Java programs, thus allowing developers to code much faster and with fewer errors.

Leveraging Automatic Analytics and Error Handling

Similarly, looking for bugs and errors is something that programming assistants perform during the development phase, and these aberrations are automatically flagged.

However, after deployment, machine learning can be used to analyze system logs and allow for a quick and efficient error flagging.

But that’s not everything. It’s predicted that in the future, the software itself will be enabled to change dynamically when an error occurs – without any human intervention.

Automating Code Refactoring

Scalability and regular maintenance are of critical importance for the success and longevity of any software, and these require clean code.

The thing is that many companies upgrade their technologies which means that refactoring has to be conducted. And it would be an understatement to say that this procedure is challenging.

Again, machine learning can save the day by analyzing and optimizing the code thus improving its interpretability and performance.

Allowing for Accurate Estimates

One of the most important factors when it comes to software development is planning a budget and deadlines.

Unfortunately, as the best-laid schemes often fall through, artificial intelligence can be used to offer more precise estimates and predictions. It takes a lot of expertise and understanding the nitty-gritty of every individual project, as well as being familiar with the implementation team for these estimates to be reliable.

Machine learning can use the data from previous projects, including customer feedback, feature definitions, estimates, and final results to calculate how long it will take to build a new product and what its cost will be.

Implement Strategic Decision-Making

It can be challenging to figure out which products and features to zero in on and which ones aren’t worth investing time and energy into.

With artificial intelligence and big data, it’s possible to take business factors and past developments into consideration and assess the performance of the existing products.

Such an intelligent solution can help both entrepreneurs and development teams figure out how to minimize risks and maximize their profits with the right efforts.

Artificial intelligence has a profound impact on software development. No matter whether you opt for an approach entirely based on machine-learning models or stick to the traditional SDLC agile approach with a machine-learning facelift, you can expect to boost your productivity, cut costs, speed up the entire development process, and create a more successful, easily-scalable product.

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