Sustainable Solving: Reducing the Memory Footprint of IFDS-Based Data Flow Analyses Using Intelligent Garbage Collection

Abstract

Static data flow analysis is an integral building block for many applications, ranging from compile-time code optimization to security and privacy analysis. When assessing whether a mobile app is trustworthy, for example, analysts need to identify which of the user s personal data is sent to external parties such as the app developer or cloud providers. Since accessing and sending data is usually done via API calls, tracking the data flow between source and sink API is often the method of choice. Precise algorithms such as IFDS help reduce the number of false positives, but also introduce significant performance penalties. With its fixpoint iteration over the program s entire exploded supergraph, IFDS is particularly memory-intensive, consuming hundreds of megabytes or even several gigabytes for medium-sized apps. In this paper, we present a technique called CleanDroid for reducing the memory footprint of a precise IFDS-based data flow analysis and demonstrate its effectiveness in the popular FlowDroid open-source data flow solver. CleanDroid efficiently removes edges from the path edge table used for the IFDS fixpoint iteration without affecting termination. As we show on 600 realworld Android apps from the Google Play Store, CleanDroid reduces the average per-app memory consumption by around 63% to 78%. At the same time, CleanDroid speeds up the analysis by up to 66%.