How Does Generative AI Impact Software Engineering?
A recent poll conducted by Gartner, Inc. among over 2,500 executive leaders revealed that 70% of them are currently in the phase of investigating and exploring generative AI, while 19% have advanced to the pilot or production stage.
With that being said, 2023 witnesses the emergence of Generative AI with innovative solutions like GitHub Copilot, TabNine, ChatGPT and others. 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 underscores the importance of every developer to stay abreast of latest developments. Doing so 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 field of software development.
How do generative AI coding tools operate?
An AI code builder involves training AI models on enormous code samples from various programming languages using deep learning. Deep learning enables computers to process data by identifying patterns, establishing connections and drawing inferences, mimicking human learning processes.
To replicate the human pattern recognition process, these AI models employ extensive networks of interconnected nodes, allowing them to function similarly to neurons. Once they finish training on vast amounts of data and become proficient in generating useful code, these models are incorporated into tools like coding editors and Integrated Development Environments (IDEs). In these environments, they respond to natural language prompts and existing code, offering suggestions for new code, phrases and functions.
How can developers use generative AI coding tools to enhance performance?
As the realm of generative AI continues to evolve, developers are engaged in a multitude of experiments and explorations to unlock the technology’s full potential.
While the precise scope of generative AI in software development are still being actively explored, developers today can harness these coding tools to achieve the following:
Tackling intricate code translation tasks
Some code creators AI have provided code translation capabilities that allow developers to convert source code into different programming languages. For example, translating from Python code to Ruby. While the suggestions may not always be flawless, developers have found it more convenient to review and rectify any errors rather than manually translating the source code from scratch. They have also observed that this process of reviewing and correcting resembles their typical workflow when collaboration with colleagues on code.
Enhance coding efficiency
The modern AI code builder can generate comprehensive multiple lines of code that are highly relevant to your context. Although generative AI coding tools are still in their infancy stage, the benefits we gain have been remarkable.
In a Github’s study, developers using GitHub Copiloy experienced a remarkable 55% increase in coding speed compared to those who didn’t use the tool. THe benefits of improved productivity extended beyond speed alone. An overwhelming 74% of engineers reported feeling less frustrated during coding sessions and were able to concentrate on more satisfying work.
Improve test coverage
Some generative AI coding tools show exceptional proficiency in pattern recognition and completion. Developers can deploy these tools to create comprehensive unit, functional and even security tests through natural language prompts. Additionally, some tools provide security vulnerability filtering, notifying developers when they unintentionally introduce a vulnerability in their code.
Current limitations of Generative AI coding tools
The profound impact of generative AI on the software development process is indisputable. Nevertheless, this transformative technology also brings forth a set of challenges that require careful consideration and resolution
Well-prepare the workforce for Generative AI coding tools
The success of technologies often hinges on their ease of adoption. Despite the considerable buzz surrounding AI code builder tools like GitHub Copilot and similar offerings, their usage has largely been limited to proof-of-concepts and studies, reaching only a small user base. Therefore, the critical task that lies ahead for software organizations is to train their workforce in harnessing the potential of generative AI platforms, ensuring widespread adoption and maximizing their benefits.
Lack of certainty in the accuracy of generated/suggested output
Generative AI algorithms undergo training within specific domains and with a predetermined dataset, which can restrict their capability to generate code beyond those domains. Developers need to recognize the limitations of this technology and devise strategies to incorporate manual intervention at each output stage to ensure appropriate usage.
The bottom line
Generative AI offers more than just alleviation of tedious tasks. It ignites developers’ creativity, empowering them to confront complex problems and envision innovative solutions previously unexplored. With its ability to enhance productivity, provide alternative perspectives and facilitate skill expansion, there is much to be enthusiastic about in the upcoming wave of software development.
KMS Solutions boasts a team of tech experts specializing in generative AI. With a strong emphasis on continuous learning and development, we generate a comprehensive set of rigorous training courses to help our development team fully equipped with the latest technical knowledge, enabling them to push boundaries and deliver cutting-edge solutions.
We also offers consulting sessions to clients to guide and support them in effectively integrating new technologies, such as generative AI, into their processes and drive business growth. Contact us today to get ahead of the curve and gain competitive edge!