4 Ways Artificial Intelligence Improves Software Development

To state that AI in software development is changing different aspects of a developer’s life will be an understatement, in fact, according to a recent Deloitte report AI-enhanced software development processes could help the average developer be 10X more productive than they would be on their own.

Today’s reality is that artificial intelligence (AI) technology is being actively applied in different industries and is improving business efficiencies across the globe.

In other words, AI technology has already, for numerous industries, brought a revolution in how things are done, and the software development industry is no exception.

In fact, the software development industry may be one of the industries where AI technology may have already started a complete revolution. As more and more innovative applications are explored, it is expected that AI technology will completely change the way developers write and maintain code.

Some experts, like Pete Warden, an author, a scientist, and a Google research engineer, believe that thanks to machine learning and AI technologies, the software jobs of the future will not even involve programming.

While this notion is a matter of debate, it provides a realistic insight into the magnitude of AI technology’s impact in software development.

Here are four pointers explaining the role of AI in software development in improving software development processes:

How AI Will Change Software Development and Applications and The Impact it Currently Has

Shorter Software Development Lifecycles

A software development process consists of several steps. Unfortunately, many of these steps are quite time-consuming, making the entire process of developing market-ready software slow.

Now, this process may not necessarily be slow, but the market demands often require software development teams to work with almost unrealistic deadlines. This is where machine learning, deep learning, and AI in software become important.

While there is still some time before AI is integrated into the entire software development process, many teams across the globe have already started using deep learning and machine learning to speed up one of the most important yet most time-consuming steps of the software development process: testing.

Traditionally, software tests aimed at finding bugs before the deployment stage had to be run manually. This was not just time-consuming but also involved an enormous scope of human error. As a result, many users would find bugs in the software after being rolled out to the market.

Nowadays, software testing is completely different. Machine learning and deep learning algorithms are deployed that conduct automated and continuous tests, freeing up numerous valuable hours in the typical developer’s day. It is also worth noting that using machine learning and deep learning algorithms allows software development teams to test more scenarios and find more bugs, minimizing the chances of a paying user finding a bug.

In other words, AI-enabled software testing allows development teams to cut down development time and still reduce their failure rate and produce a bug-free product.

The Advent Of Programming Assistants

AI-powered virtual assistants are finding applications in a variety of industries, including development. Unfortunately, modern software developers have to read and seek references from lengthy documentation and spend an unrealistic amount of time debugging code.

Programming assistants can help reduce the time taken to complete such processes. Modern programming assistants, such as Codota for Java and Kite for Python, offer superb time-saving features such as just-in-time recommendations and help, autocomplete features for code, and even code examples. Another AI coding assistant is Tabnine, an AI-powered code completion tool that supports over 30 programming languages. Tabnine is perhaps one of the most popular AI-powered development assistants, with over one million active users.

Programs like Tabnine and Codota are even capable of ‘learning’ from past experiences. With this ability, these tools can identify possible errors and highlight them in real time when the development process is underway. Experts believe soon AI technology can be used to automatically identify errors and make the necessary changes without human intervention.

Also read our related post on Software Development Outsourcing

Produce Precise Estimates

It’s no secret that AI technologies possess the ability to ‘learn’ from data that is fed to them. This ability is being actively used to produce budget and timeline estimates for software development projects. Not surprisingly, the estimates produced are incredibly precise.

In order to produce a realistic prediction, an in-depth understanding of context and subject matter expertise. Both of these things can be ‘taught’ to an AI. As a result, AI technologies can accurately predict roadblocks that may emerge during the span of the software development process. It can also precisely predict the amount of time, effort, and money required to complete a specific software project.

This ability to produce accurate estimates has serious implications. For instance, this can help development agencies plan the number of software projects they can take and adjust their pricing accordingly. In addition, this may result in a better experience for the clients of development agencies as they will be able to start projects with realistic expectations that are highly likely to be met in the future.

AI in Software Development helping in Code Refactoring

In order to efficiently migrate to better technologies, maintaining a clean code is critical. However, it is a challenging task in today’s development landscape, when multiple contributors are collaborating on the same software projects. As a result, most software projects pass through multiple hands, even in the maintenance stage.

As a result, code-refactoring has become necessary for organizations that want to move to newer and better technologies. This time-consuming and difficult task is being made easier and more efficient with the help of Machine Learning technology. With the application of Machine Learning, code-refactoring can be automated to a large extent. This will efficiently optimize code for performance and make it easy to interpret.

Also read: Software Development Ideas For SaaS Businesses

Challenges and Limitations of AI in Software Development

While AI is revolutionizing software development, it also comes with its share of challenges and limitations that must be addressed to ensure responsible and effective integration.

AI models often inherit biases from training data, leading to flawed predictions and unfair outcomes, impacting code quality and reliability. While AI aids code generation, human oversight remains crucial for adherence to standards, security, and ethical practices. Also, high computational demands and integration challenges increase costs and complicate workflows, especially for smaller organizations. Experts also suggest that overreliance on AI risks may reduce developer skills and create security vulnerabilities.

By addressing these challenges through thoughtful implementation, collaboration, and ethical considerations, developers and organizations can fully leverage the potential of AI in software development while mitigating its risks.

AI Tools and Platforms in Software Development

The adoption of AI in software development is largely driven by the availability of advanced tools and platforms that streamline processes, enhance productivity, and enable innovative solutions. From assisting with code generation to automating testing, these tools are transforming how developers approach software creation.

Key AI Tools and Platforms Supporting Developers

1. GitHub Copilot

GitHub Copilot, powered by OpenAI’s Codex, acts as an AI-powered coding assistant. It provides context-aware code suggestions, auto-completes functions, and even generates boilerplate code, saving developers significant time during the coding process.

2. Tabnine

A widely popular AI coding assistant, Tabnine supports over 30 programming languages. By learning from user-specific projects, it delivers precise code predictions and helps reduce repetitive coding tasks.

3. IBM Watsonx Code Assistant

IBM Watsonx Code Assistant brings enterprise-grade AI support to developers. It focuses on generating code snippets, automating documentation, and improving team collaboration by offering reliable and scalable solutions.

4. TensorFlow and PyTorch

TensorFlow and PyTorch are leading frameworks in the realm of machine learning and deep learning. These tools provide robust libraries for building and training neural networks, enabling the development of AI-driven applications with complex functionality.

5. Generative AI for Coding

Generative AI tools leverage natural language processing (NLP) to help developers create code from plain language descriptions. These tools simplify the development process, making it more accessible for non-technical users.

6. Automated Testing Platforms

AI-powered testing platforms enable continuous testing by automatically generating and executing test cases. These platforms can simulate user scenarios, identify bugs, and optimize test coverage, reducing the time required for manual testing.

7. Cloud-Based Machine Learning Platforms

Platforms like Google Cloud AI and Amazon SageMaker provide developers with scalable AI and machine learning tools. They allow developers to train, deploy, and monitor AI models in the cloud, reducing infrastructure costs and complexities.

8. No-Code/Low-Code Platforms

AI-powered no-code and low-code platforms are democratizing software development. By offering drag-and-drop interfaces and pre-built templates, these platforms enable faster prototyping and development without requiring extensive coding expertise.

9. Interpretable Models

For developers working on critical applications, interpretable AI models help ensure transparency. These models explain their decision-making processes, improving trust and accountability in software systems.

How AI Tools Are Shaping the Future of Development

Developers can focus on creative and strategic aspects of software development while automating repetitive and mundane tasks by leveraging these AI tools and platforms. This not only accelerates the development process but also improves the quality and scalability of the final product.

From GitHub Copilot’s intelligent suggestions to TensorFlow’s powerful machine-learning capabilities, AI tools are becoming indispensable in modern software development. Their integration ensures developers can meet market demands with speed, precision, and innovation.

Conclusion

While AI technology is expected to revolutionize the way code is written completely; it will not mean that coders and professionals working as software developers will become obsolete. It simply means that the core responsibilities of developers will transform to aid the application of the emerging outsource AI technology. In the future, software developers that can work in collaboration with AI technology are expected to be in high demand. Complete implementation of AI technology will enable businesses to access high-quality custom software development services at lower costs. Such collaborations will involve much less hassle than the current situation.

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