Data-Driven Precondition Inference with Learned Features

ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2016), Santa Barbara, CA, June 13-17, 2016.
Winner of the Invariant Synthesis track in both the 2017 and 2018 Syntax Guided Synthesis Competition

Saswat Padhi, Rahul Sharma, Todd Millstein

We extend the data-driven approach to inferring preconditions for code from a set of test executions. Prior work requires a fixed set of features, atomic predicates that define the search space of possible preconditions, to be specified in advance. In contrast, we introduce a technique for on-demand feature learning, which automatically expands the search space of candidate preconditions in a targeted manner as necessary. We have instantiated our approach in a tool called PIE. In addition to making precondition inference more expressive, we show how to apply our feature-learning technique to the setting of data-driven loop invariant inference. We evaluate our approach by using PIE to infer rich preconditions for black-box OCaml library functions and using our loop-invariant inference algorithm as part of an automatic program verifier for C++ programs.

[PDF | Implementation]