# Active Learning of Points-To Specifications

arXiv: https://arxiv.org/abs/1711.03239 · HTML: https://mlsanity.com/paper/1711.03239/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [CodeQueries: A Dataset of Semantic Queries over Code](https://mlsanity.com/paper/2209.08372/) (2022)
- [Learning Formal Specifications from Membership and Preference Queries](https://mlsanity.com/paper/2307.10434/) (2023)

## Abstract (arXiv)

When analyzing programs, large libraries pose significant challenges to static points-to analysis. A popular solution is to have a human analyst provide points-to specifications that summarize relevant behaviors of library code, which can substantially improve precision and handle missing code such as native code. We propose ATLAS, a tool that automatically infers points-to specifications. ATLAS synthesizes unit tests that exercise the library code, and then infers points-to specifications based on observations from these executions. ATLAS automatically infers specifications for the Java standard library, and produces better results for a client static information flow analysis on a benchmark of 46 Android apps compared to using existing handwritten specifications.