Conquering the Extensional Scalability Problem for Value-Flow Analysis Frameworks
This addresses scalability issues for developers and researchers using static analysis to check multiple properties in software, though it is incremental as it builds on existing frameworks with a novel optimization strategy.
The paper tackled the scalability problem in static program analysis for checking multiple value-flow properties by introducing an inter-property-aware approach that reuses analysis results to optimize performance, achieving over 8x speedup and 1/7 memory usage compared to existing methods while finding 39 bugs, including four with CVE IDs.
With an increasing number of value-flow properties to check, existing static program analysis still tends to have scalability issues when high precision is required. We observe that the key design flaw behind the scalability problem is that the core static analysis engine is oblivious of the mutual synergies among different properties being checked and, thus, inevitably loses many optimization opportunities. Our approach is inter-property-aware and able to capture possible overlaps and inconsistencies among different properties. Thus, before analyzing a program, we can make optimization plans which decide how to reuse the specific analysis results of a property to speed up checking other properties. Such a synergistic interaction among the properties significantly improves the analysis performance. We have evaluated our approach by checking twenty value-flow properties in standard benchmark programs and ten real-world software systems. The results demonstrate that our approach is more than 8x faster than existing ones but consumes only 1/7 memory. Such a substantial improvement in analysis efficiency is not achieved by sacrificing the effectiveness: at the time of writing, 39 bugs found by our approach have been fixed by developers and four of them have been assigned CVE IDs due to their security impact.