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The Impact of Artificial Intelligence on Software Development

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Artificial intelligence is transforming business functions in companies across almost every sector of today’s world. The software development industry is no exception. Over the past few years, software development technologies have gone through an enormous transformation thanks to the new advancements of AI in this field.

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Artificial Intelligence's Influence on Software Development

AI has already begun to impact the building of software and applications - and it will only continue to do so as technology advances. According to a report conducted by Teradata, approximately 80% of IT and business decision-makers are currently using artificial intelligence. Additionally, 30% have reported that they will be spending more on the tech over the next 3 years.

The best way to understand just how an artificial intelligence strategy applies to the development process by a software development company is to analyze the parts that have room for improvement or change.

  • Understanding Requirements - The requirements phase of a software development life cycle is the one that needs the most human intervention. However, artificial intelligence offers a wide range of tools, such as the Google Machine Learning Kit, to help automate processes within this step. Natural language processing (NLP) helps machines understand user requirements input in everyday sentences through the power of automated high-level software models.
  • Software Design - Software projects require designers to have specialized learning and experience when it comes to the planning and designing phase. Setting the correct design for each phase of the development process can be a bit tricky for designers. Dynamic changes to the design may be frequently required until the client feels satisfied with the solution.

Artificial intelligence tools allow designers to automate a few of the complex procedures within the design process by enabling the most capable methods to protect the designs throughout the editing process. For example, an Artificial Intelligence Design Assistant (AIDA) works as a website building platform to help examine different combinations of software designs. It then presents a customized design based on the requirements set forth by the client to save time.

  • Automated Code - Coding for a large project can be incredibly time-consuming and take quite a bit of manpower to accomplish. Many developers are choosing tools that are able to write code to help automate this process. Artificial intelligence-based assistance is able to learn from a developer and automate code generation (to some extent) while looking for bugs.
  • Testing - Artificial intelligence tools can explore the authenticity of the information, create testing information, as well as advance and examine the scope of the testing at hand. AI-powered testing can help improve overall quality with the power of machine learning as software grows in complexity and leads to an increase in complexity.

The Benefits of Incorporating Artificial Intelligence in the Software Development Life Cycle

Although traditional human-powered software development is not going anywhere in the near future, it will be getting upgraded thanks to artificial intelligence, including:

  • Intelligent Assistants - With the help of AI-powered intelligent assistants, developers can stop spending a large part of their time debugging code and reading the documentation.
  • Code Refactoring - Machine learning programs can analyze code and automatically optimize it for performance and interpretability. Clean code is an absolute necessity for successful team collaboration and maintenance in the future. AI can help make large- scale refactoring as painless as possible.
  • Quick Prototyping - Creating technology products for businesses can take months or even years of planning ahead. Machine learning is able to help expedite this process with the assistance of either natural language or visual interfaces.
  • Automated Analytics and Error Identification - Programming assistants using machine learning can learn from past projects to help identify common errors and bring them to the developers’ attention during the development phase of the software development life cycle. They can also analyze system logs to rapidly identify errors as soon as (or even before) they happen.
  • Better Estimates - After training on data from past projects, machine learning can help predict timelines and budgets more accurately than humans may be able to. This can help prevent development projects from running over timelines and keep them within a previously agreed-upon budget.

What’s Next?

The ever-present question of whether or not artificial intelligence will be able to take over for the power of humans is still a “no” in the world of software development (at least for the near future). Perhaps we’ll see artificial intelligence itself create other AI solutions and eliminate the need for human interference in the process one day.

Today, Artificial intelligence and machine learning have already had a major impact on nearly every phase of the software development life cycle and will undoubtedly continue to grow in use in automated cases and popularity. However, developers are still required to complete software development projects. So, up to this point, AI is more of a collaborator rather than a full replacement.

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