A recent survey of 2,500 CEOs by Gartner, Inc. found that 70% of them are currently in the investigation and research phase of generative AI, while 19% are in the pilot or production phase.
That being said, 2023 is witnessing the emergence of Generative AI with innovative solutions like GitHub Copilot, TabNine, ChatGPT and more. These AI-assisted engineering workflows provide code suggestions and even entire functions in response to existing code and natural language prompts. The rapidly evolving nature of these tools highlights the importance of every developer staying abreast of the latest developments. It can help developers effectively adapt their approaches and seamlessly navigate the changing landscape of software development.
In this article, we’ll delve into how Generative AI is transforming the software development industry.
How do generative AI coding tools work?
The generative AI coding tool involves training AI models on massive code samples from various programming languages using deep learning. Deep learning enables computers to process data by identifying patterns, making connections and making inferences, mimicking human learning processes.
To replicate the human pattern recognition process, these AI models use extensive networks of interconnected nodes, allowing them to act like neurons. Once they’ve completed training on massive amounts of data and become proficient at generating useful code, these models are incorporated into tools such as code editors and Integrated Development Environments (IDEs). In these environments, they respond to natural language prompts and existing code, suggesting new code, expressions, and functions.
How can developers use generative AI to increase productivity?
As the field of generative AI continues to evolve, developers are engaged in a lot of experimentation and exploration to unlock the full potential of the technology.
While the exact scope of generative AI in software development is still being actively explored, developers today can use these coding tools to achieve:
Solving complex code translation tasks
Some AI-powered coding tools have provided code translation capabilities that allow developers to convert source code into different programming languages. For example, translating Python code to Ruby. While the suggestions may not always be flawless, developers have found it more convenient to review and fix errors than to manually translate the source code from scratch. They also found that this review and fix process was similar to their typical workflow when collaborating with colleagues on code.
Increase coding efficiency
Modern generative AI tools can generate comprehensive multiple lines of code that are highly relevant to your context. While generative AI models for coding are still in their infancy, the benefits we’ve seen have been remarkable.
In a Github study, developers using GitHub Copiloy experienced a remarkable 55% increase in coding speed compared to those not using the tool. The benefits of improved productivity go beyond just speed. An overwhelming 74% of engineers reported feeling less frustrated during coding sessions and were able to focus on more fulfilling work.
Improve test coverage
Some generative AI coding tools show exceptional skill at pattern recognition and completion. Developers can use these tools to create comprehensive unit, functional, and even security tests using natural language instructions. In addition, some tools provide security vulnerability filtering, notifying developers when they inadvertently introduce vulnerabilities into their code.
Current limitations of Generative AI coding tools
The profound impact of generative AI on the software development process is indisputable. However, this transformative technology also raises a number of challenges that require careful consideration and resolution
Prepare the workforce well for Generative AI tools
The success of technologies often depends on their ease of adoption. Despite the considerable hype surrounding GitHub Copilot and similar tools, their use has largely been limited to proof-of-concepts and studies, reaching only a small user base. Therefore, a critical challenge facing software organizations is to prepare their workforce to harness the potential of AI platforms, ensuring widespread adoption and maximizing their benefits.
Lack of certainty in the accuracy of the generated/proposed output
Generative AI algorithms are trained on specific domains and with a predetermined database, which can limit their ability to generate code outside of those domains. Developers must recognize the limitations of this technology and develop strategies to manually intervene at each output stage to ensure appropriate usage.
Generative AI offers more than just easing tedious tasks. It ignites the creativity of developers, enabling them to tackle complex problems and imagine innovative solutions that have not been explored before. With the ability to increase productivity, provide alternative perspectives, and facilitate skill development, there is much to be excited about in the upcoming wave of software development.