The advent of ChatGPT, powered by the impressive GPT-3.5 language model, has sparked significant interest among software engineering leaders, particularly in the realm of code generation. The question of whether software engineering teams should embrace ChatGPT to generate application code has become a pressing issue. However, the decision requires careful consideration, taking into account an organization's existing use of generative AI models for code generation. In this article, we delve into the details to help shed light on this topic.
GPT-3.5, the core foundation model behind ChatGPT, has garnered immense enthusiasm since its launch. It incorporates extensive training data used to build Codex, a code generation model specifically tailored for programming languages. Developers who have utilized ChatGPT for code generation have expressed positive feedback, leading to a surge in interest and the development of unofficial tools to integrate ChatGPT into developer environments.
Despite the promise ChatGPT holds for code generation, several policies and limitations need to be taken into account, prompting software engineering teams to rethink its usage
To utilize ChatGPT effectively, developers may be required to provide prompts that contain intellectual property, potentially exposing sensitive information. It's crucial to note that prompts and generated code may be incorporated into future OpenAI updates, raising concerns about data privacy compliance and potential breaches
Enterprises currently have no option to opt out of OpenAI's data retention policies. This means any personally identifiable information entered during code generation will be included in the data used to train the model. OpenAI strongly advises against inputting sensitive information, including enterprise intellectual property.
For most enterprise software engineering teams seeking the benefits of generative AI-based code creation tools, utilizing the Codex model or the Copilot service, built upon Codex, is recommended. These alternatives provide more control, as enterprises can customize the Codex model using one-shot or few-shot learning techniques and small datasets. Importantly, these services allow enterprises to opt out of data retention for training the Codex model, mitigating privacy and intellectual property concerns.
There are specific scenarios where software engineering teams can consider using ChatGPT.
For initial exploration of generative AI-based code generation models, ChatGPT can be utilized in scenarios such as stub generation, prototyping, and evaluating English prompt tools versus IDE plugins. However, it is vital to adhere to strict guidelines
The decision of whether software engineering teams should adopt ChatGPT for code generation requires careful evaluation of the associated risks and benefits. While ChatGPT showcases impressive capabilities, enterprises should exercise caution due to intellectual property concerns, data privacy limitations, and the lack of opt-out options. Exploring alternatives like Codex and Copilot, which offer more control and privacy safeguards, is advisable for most enterprises. Nonetheless, ChatGPT can serve as an initial exploration tool in specific scenarios, as long as stringent guidelines and thorough code review processes are implemented. By striking a balance between innovation and risk mitigation, software engineering teams can make informed decisions on integrating ChatGPT into their development workflows.