Ongoing Learning Pipeline for Assisted Code Remediation
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Solution Overview
Problem
Existing software development processes, particularly in DevSecOps, struggle to integrate security effectively without hampering the speed and efficiency of the development pipeline, and there is a lack of efficient methods for learning and applying organization-specific flaw fixes in real-time.
Innovation Solution
A remediation agent integrated into the software development pipeline uses a combination of automatic remediation rules, a flaw database, and machine learning models to suggest and apply fixes for detected flaws, leveraging both project-specific and multi-organization training data to enhance the learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security scanning and analysis tools are integrated into the DevOps pipeline, then software security quality is improved, but the development speed and pipeline efficiency deteriorate
Solution Approach 1:
The system performs security scanning and flaw detection as preliminary actions within the CI/CD pipeline, automatically identifying vulnerabilities before deployment. By integrating security checks into the build process and using automated remediation, the system addresses security issues proactively without requiring separate manual security phases, thus maintaining development speed while improving security quality
Solution Approach 2:
The remediation agent automatically applies fixes for detected flaws without requiring manual security expert intervention. The system self-services by autonomously analyzing scan results, selecting appropriate remediations from a knowledge base, and applying fixes directly to the codebase, thereby eliminating the time-consuming manual review process while ensuring security quality
2Measurement precision
If manual security expert review is performed on code submissions, then flaw detection accuracy is improved, but the time required for code review increases
Solution Approach 1:
The remediation agent acts as an intermediary between automated security scanners and developers. It receives scan results, consults a knowledge base of known flaws and remediations, and automatically applies appropriate fixes. This intermediary system captures the expertise previously required from manual security reviewers and embeds it in an automated agent, maintaining high detection accuracy while eliminating time losses
Solution Approach 2:
The system replaces the mechanical process of manual security expert review with an automated computational system. The remediation agent uses algorithmic analysis of code patterns, vulnerability signatures, and remediation rules to detect and fix flaws, substituting human manual inspection with automated mechanical processes that are both accurate and time-efficient
3Reliability
If comprehensive security scanning is performed on all code submissions, then flaw detection coverage is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies local quality by tailoring the depth and type of security scanning to specific code contexts, vulnerability patterns, and risk levels. Rather than uniformly scanning all code with the same intensity, the remediation agent selectively applies scanning techniques based on the local characteristics of the code being analyzed, improving coverage efficiency and reducing unnecessary processing time
Solution Approach 2:
The system performs partial security scanning by focusing on the most critical vulnerability patterns and high-risk code areas in each submission. Rather than exhaustively analyzing every line of code with all possible security checks, the remediation agent applies targeted scanning that covers the most important security concerns, achieving sufficient detection coverage with reduced processing overhead
Data Source
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AI summary
With invocations of a software development pipeline, organization specific remediations/fixes for a software project can be learned from scanning results of code submissions (e.g., commits or merges) across an organization for a software project(s). Fixes of detected program code flaws can be detected and/or specified across scans and associated with flaw identifiers and used for training machine learning models to identify candidate fixes for detected flaws. This ongoing learning during development propagates fixes created or chosen by experts (e.g., software engineers working on the software project) relevant to the software project. The experts can choose from suggestions mined from the learned fixes of the organization and suggestions generated from a pipeline created with the trained machine learning models. The selections are then used for further training of the machine learning models that form the pipeline.