How to Trust AI Contributions to Your Codebase
Blog post from Sonar
The traditional Software Development Life Cycle (SDLC) relies on developers understanding and modifying any code they use. However, the introduction of generative AI solutions can break this trust as developers may blindly accept AI-generated code without fully understanding it. This can lead to security risks, IP theft, and a lack of visibility into which LLMs are in use. To build trust in AI-generated code, organizations should carefully evaluate and customize specific LLMs for their needs, integrate them directly into developer workspaces, monitor changes, track where AI was used, help developers validate AI-generated code, and constantly reevaluate the performance of different models. Tools like Sonar's AI Code Assurance can assist in this process by validating AI-generated code and reporting on accountability data.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 22 | 3,391 | 431 | 160 | -16% |
| AI Coding Assistant | 2 | 458 | 93 | 58 | -12% |
| AI Agents | 1 | 894 | 163 | 79 | +56% |
| AI Model Fine-tuning | 1 | 576 | 147 | 74 | -37% |
| Real-time | 1 | 3,716 | 885 | 233 | -20% |
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