Software Security Analysis in 2030 and Beyond: A Research Roadmap
This research roadmap examines the future of software security analysis toward 2030 and beyond, outlining key challenges, emerging threats, and promising research directions. It emphasizes the need for scalable, automated, and AI-driven approaches to address growing software complexity and attack surfaces.
Background
- This is a research roadmap published in the 2025 ACM Digital Library, a leading computing professional association. It lays out where software security analysis is heading, not a specific tool or breakthrough.
- The key frame is a shift from "vulnerability identification" (finding known bug patterns) to "property satisfaction" (proving that software does or does not satisfy specific security properties). This moves from reactive patching toward proactive assurance.
- The paper discusses large language models (LLMs) as a disruptor: they lower the barrier to entry for security analysis but also introduce new, hard-to-verify risks.
- Core tensions covered: automation vs. human reasoning, soundness (no false negatives) vs. scalability, and the need for "explainability" when AI tools flag issues.
- Context: software security has traditionally relied on static analysis (scanning code without running it), dynamic analysis (testing in runtime), and formal methods (mathematical proofs). All three have limits. This roadmap argues none alone will suffice by 2030.