Smart Code Scanner Reducing False Positives via User Feedback
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Solution Overview
Problem
Current code scanning and analysis services often produce false positives, leading to increased computational load and user frustration, as well as higher maintenance costs due to the need to sift through non-essential issues.
Innovation Solution
A system that allows users to designate false positives within a graphical user interface, enabling the scanner service to ignore similar results and reduce processing power, focusing on significant issues by applying user-defined rules based on location or scope.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If code scanning service performs comprehensive analysis to identify all potential issues, then measurement precision of code issues is improved, but quantity of false positives increases
Solution Approach 1:
The system implements feedback loops where scan results are analyzed and used to refine future scanning operations. False positives identified by users are fed back into the system to adjust scanning rules and patterns, progressively improving detection accuracy while reducing false alarm quantities through iterative learning and adaptation.
Solution Approach 2:
The system dynamically adjusts scanning parameters such as sensitivity thresholds, rule activation levels, and analysis depth based on code context, project history, and user feedback. By changing these parameters adaptively, the system maintains high detection precision while minimizing false positive outputs through context-aware configuration optimization.
2Manufacturing precision
If code scanning service analyzes all code issues in detail, then manufacturing precision of code analysis is improved, but loss of time increases
Solution Approach 1:
The codebase is divided into segments or modules that are scanned independently based on their risk profiles, complexity levels, and historical issue patterns. High-risk segments receive detailed analysis while low-risk segments undergo lighter scanning, maintaining overall analysis precision while reducing total scanning time through selective depth adjustment across different code portions.
Solution Approach 2:
The system applies partial analysis actions by focusing detailed scanning only on critical code paths, recently modified files, or high-risk areas identified through static analysis heuristics. Rather than uniformly analyzing all code with maximum depth, the system performs excessive (detailed) analysis only where necessary, achieving high precision for critical issues while minimizing time loss on lower-priority code regions.
3Reliability
If code scanning service reports all identified issues to users, then reliability of issue reporting is improved, but device complexity increases
Solution Approach 1:
The system extracts and separates false positives from genuine issues through multiple filtering layers including pattern recognition, context analysis, and user feedback mechanisms. By taking out false positives before final reporting, the system maintains reliable issue reporting while reducing the complexity burden of managing and presenting large volumes of mixed-quality findings to users.
Solution Approach 2:
The system introduces intermediary components such as rule engines, priority classifiers, and feedback processors that mediate between raw scan results and final user reports. These intermediaries filter, prioritize, and contextualize issues before presentation, maintaining reporting reliability while managing system complexity through modular architecture that separates analysis functions from reporting functions.
4Productivity
If code scanning service processes comprehensive scan results, then productivity of code review is improved, but use of energy increases
Solution Approach 1:
The system performs preliminary actions by pre-computing code metrics, establishing baseline profiles, and preparing analysis rules before actual scanning operations. Frequently accessed code patterns and historical issue data are pre-processed and cached, enabling faster subsequent scans that improve code review productivity while reducing real-time computational energy consumption through advance preparation.
Solution Approach 2:
The system implements skipping mechanisms that allow it to rapidly traverse and skip over low-risk code sections, previously analyzed stable modules, or patterns that have been predetermined as safe. By rushing through these低风险 areas without detailed analysis, the system maintains high code review productivity while significantly reducing energy consumption on portions of code that contribute minimally to overall risk assessment.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for smart code scanning, analysis and reporting. The systems, methods, and computer-readable media described herein may reduce the computational load on the scanner service and improve user experiences. In an example method described herein, a scanning service may receive code for analysis, the code being associated with a first project identifier. The scanner service may scan the code to identify one or more potential issues associated with the code. In turn, the scanner service may provide a list comprising, at least, one or more potential issues for display at a user device. The scanner service may then receive data indicative of a first potential issue being a false positive based on, at least in part, user input. The scanner service may determine, based on the first potential issue being a false positive, a first set of one or more rules for filtering the one or more potential issues, wherein the first set of one or more rules comprises a first rule. The scanner service may store and automatically apply the first set of rules.