Dynamically Generating Callback Summaries for Enhancing Static Analysis

Interprocedural static analyses require a complete and precise callgraph. Since third-party libraries are responsible for large portions of the code of an app, a substantial fraction of the effort in callgraph generation is therefore spent on the library code for each app. For analyses that are oblivious to the inner workings of a library and only require the user code to be processed, the library can be replaced with a summary that allows to reconstruct the callbacks from library code back to user code. To improve performance, we propose the automatic generation and use of precise pre-computed callgraph summaries for commonly used libraries. Reflective method calls within libraries and callback-driven APIs pose further challenges for generating precise callgraphs using static analysis. Pre-computed summaries can also help analyses avoid these challenges. We present CGMiner, an approach for automatically generating callgraph models for library code. It dynamically observes sample apps that use one or more particular target libraries. As we show, CGMiner yields more than 94% of correct edges, whereas existing work only achieves around 33% correct edges. CGMiner avoids the high false positive rate of existing tools. We show that CGMiner integrated into FlowDroid uncovers 40% more data flows than our baseline without callback summaries.