### Data-Driven Precondition Inference with Learned Features

⟬   Winner of SyGuS-Comp (Inv Track) 2017 – 18 ⟭

Proceedings of the 37 th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2016
⟨ PLDI 2016 ⟩

###### Abstract

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.

###### BibTeX Citation
@inproceedings{pldi16/padhi/pie,
title     = {Data-Driven Precondition Inference with Learned Features},