Generative artificial intelligence (GenAI) is now being utilized by developers to code more efficiently and quickly. However, caution and attention must still be exercised during its use.
GenAI, which has been in existence since at least 2019, offers advancements in generating natural language, images, videos, and even code, according to Diego Lo Giudice, Forrester’s vice president and principal analyst. It provides access to expert peer programmers and specialists who can provide quick information, suggest solutions, and test cases interactively.
Developers can leverage GenAI throughout the software development lifecycle with dedicated “TuringBots” at each stage to enhance tech stacks and platforms. These AI-powered tools can assist in building, testing, and deploying code, as well as looking up technical documentation and auto-completing code.
Generative models have the ability to write code in various languages, allowing developers to input prompts for generating, refactoring, or debugging lines of code. While GenAI can significantly increase productivity, it is important for developers to use it as a starting point and thoroughly test code before production.
Organizations can face challenges related to the governance of large-scale implementation and pricing based on the number of end users. Despite the benefits of GenAI in filling talent gaps and enabling junior professionals, an expert eye review is still necessary for tasks such as security remediation.
Security requirements must be considered when using GenAI, especially in regulated or data-sensitive environments. While coding assistants can enhance productivity, developers must ensure that code is adequately tested and meets quality requirements throughout the development process. Ultimately, organizations must assess how GenAI can enhance coding practices while also mitigating security risks associated with new attack vectors and vulnerabilities. There is great potential for generating more code with low-code platforms, but this also comes with an increase in potential risks. It is important for organizations to understand the AI models being used, especially when it comes to large language models (LLMs) like OpenAI’s ChatGPT. While GenAI Turingbots can be beneficial for software development, businesses should ensure that the models align with their corporate policies.
It is crucial for developers to be cautious when using GenAI tools, especially when it comes to proprietary data and intellectual property. Sharing private IP such as code and financial information can lead to training GenAI models with another organization’s IP, which can have serious consequences. It is recommended to thoroughly test open-source LLMs before putting them into production.
The impact of AI-powered coding assistants on roles like software developers is still uncertain. While these tools can change how skills are valued, the biggest potential lies in their ability to summarize information and provide developers with a better understanding of the business. This knowledge can then be translated into specific instructions for systems to execute tasks and build products that meet customer needs.
