StatiCode AI+ adds support for defect assessment and remediation guidance
StatiCode AI+ feature has been launched, offering support for defect assessment and remediation guidance by integrating DeepSeek among other general large models. In the defect audit phase, it provides authenticity-assisted judgment, repair suggestions, and code differences. Developers can further confirm issues and repair plans by combining contextual code with test results.
StatiCode AI+ adds support for defect assessment and remediation guidance. Static analysis can identify potential defects without running the program, but the scan results still require manual judgment to determine if they meet triggering conditions and which fixes to apply based on project code, compilation configurations, and call relationships. AI+ integrates this auxiliary analysis into the existing audit interface.
Two bottlenecks in traditional code audits
- Confirm whether the alert is genuine. Analysts need to review the code context, data flow, and trigger path to avoid incorrectly attributing issues that cannot be triggered as defects.
- Forming a verifiable repair solution. The fix must eliminate the defect while avoiding changes to the original business logic, and it should be confirmed through retesting that the issue no longer exists.
How AI+ fits into defect auditing
After analysts invoke AI+, the system generates an auxiliary judgment based on rules, relevant code snippets, and context. This output helps narrow down the investigation scope but cannot replace engineers' confirmation of real-execution conditions and business impacts.
Retain verification evidence before and after remediation
For confirmed issues requiring a fix, AI+ can display repair suggestions along with code differences. Developers must still verify whether the suggestions comply with project constraints, complete compilation, unit testing, or regression testing, and then associate the final adopted modifications with the original alert.
By consolidating auxiliary analysis, manual judgment, and retesting results in one place, the team can quickly understand alerts and retain the handling process. The reliability of AI outputs depends on the completeness of context and model capabilities; however, critical issues should still be confirmed by personnel with project knowledge.