Our newfound ability to analyze source code in massive software repositories such as GitHub has led to an uptick in data-driven solutions to software engineering problems. Source code analysis is often realized as traversals over source code artifacts represented as graphs. Since the number of artifacts that are analyzed is huge, in millions, the efficiency of the source code analysis technique is very important. The performance of source code analysis techniques heavily depends on the order of nodes visited during the traversals: the traversal strategy. For instance, selecting the best traversal strategy and optimizing it for a software engineering task, that infers the temporal specification between pairs of API method calls, could reduce the running time on a large codebase from 64% to 96%. While, there exists several choices for traversal strategy, like depth-first, post-order, reverse post-order, etc., there exists no technique to choose the most time-efficient strategy for traversals. In this paper, we show that a single traversal strategy does not fit all source code analysis scenarios. Somewhat more surprisingly, we demonstrate that given the source code expressing the analysis task (in a declarative form) one can compute static characteristics of the task, which together with the runtime characteristics of the input, can help predict the most time-efficient traversal strategy for that (analysis task, input) pair. We also demonstrate that these strategies can be realized in a manner that is effective in accelerating ultra-large-scale source code analysis. Our evaluation shows that our technique successfully selected the most time-efficient traversal strategy for 99.99%-100% of the time and using the selected traversal strategy and optimizing it, the running times of a representative collection of source code analysis in our evaluation were considerably reduced by 1%-28% (13 minutes to 72 minutes in absolute time) when compared against the best performing traversal strategy. The case studies show that hybrid traversal reduces 80--175 minutes in running times for two software engineering tasks. The overhead imposed by collecting additional information for our approach is less than 0.2% of the total running time for a large dataset that contains 287K

Control Flow Graphs (CFGs) and less than 0.01% for an ultra-large dataset that contains 162M CFGs.