Techopedia. Pros & cons of AI coding assistants: speed vs quality
AI coding assistants are paving a new way of programming. A chatbot can manage the entire coding process, including prompting, code generation, testing, bug-fixing, and deployment.
One high-profile example is Devin AI.
While these AI tools offer speed and efficiency, questions linger regarding their impact on code quality. As the software engineering landscape evolves, it becomes crucial to assess the pros and cons of AI coding assistants in the short term.
Recently, Cognition, the team behind Devin AI, showcased videos of its early-access master coder seamlessly writing code from prompts, fixing bugs in real-time, and even completing paid tasks on platforms like Upwork for a return on investment. These demonstrations have sparked fervent discussions suggesting this development heralds the end of traditional coding practices.
Techopedia recently explored this topic with Vaclav Vincalek, virtual CTO and founder of 555vCTO.
The use case for software coding assistants
Vincalek says it’s important to remember that AI can’t replace developers.
For example, when Google introduced typeahead within Google Docs, it didn’t replace the skilled writer, it — in some instances — sped up the text production with its suggestions.
However, unlike text typeahead, the software code has to be 100 percent accurate, Vincalek says.
Modern code editors already have built-in syntax checkers and can provide code samples for developers to copy and paste and validate. Coding assistants can — in real time — provide additional code checking for potential security or performance issues and suggest better ways to achieve the desired functionality.
One of the main motivations for adopting AI coding assistants is to speed up software development and cut off related mundane tasks for developers, Vincalek, founder at 555vCTO told Techopedia.
Vulnerabilities of AI coding assistants
While there are benefits to this new technology, a study by Cornell University researchers reveals the potential for security issues.
Developers using AI tools wrote less secure code and were more inclined to trust its security, despite it often containing more vulnerabilities.
Conversely, users who trusted AI tools less and customized prompts more cautiously introduced fewer security issues.
This underscores the need for caution and scrutiny in relying on AI coding assistants amidst ongoing concerns about generative AI security.
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