A machine-learning algorithm with disjunctive model for data-driven program analysis

We present a new machine-learning algorithm with disjunctive model for data-driven program analysis. One major challenge in static program analysis is a substantial amount of manual effort required for tuning the analysis performance. Recently, data-driven program analysis has emerged to address this challenge by automatically adjusting the analysis based on data through a learning algorithm. Although this new approach has proven promising for various program analysis tasks, its effectiveness has been limited due to simpleminded learning models and algorithms that are unable to capture sophisticated, in particular disjunctive, program properties. To overcome this shortcoming, this article presents a new disjunctive model for datadriven program analysis aswell as a learning algorithm to find the model parameters. Ourmodel uses Boolean formulas over atomic features and therefore is able to express nonlinear combinations of program properties. A key technical challenge is to efficiently determine a set of good Boolean formulas, as brute-force search would simply be impractical. We present a stepwise and greedy algorithm that efficiently learns Boolean formulas. We show the effectiveness and generality of our algorithm with two static analyzers: Contextsensitive points-to analysis for Java and flow-sensitive interval analysis for C. Experimental results show that our automated technique significantly improves the performance of the state-of-the-art techniques including ones hand-crafted by human experts.