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ShaPE, a Shape Predicate Extractor.

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ShaPE: Shape Predicate Extractor
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ShaPE, a Shape Predicate Extractor.

SummarySetupUsageExamplesBenchmarkPublications

Summary

The prototype tool ShaPE automatically learns recursive shape predicate definitions from memory graphs, so as to formally describe the pointer-based data structures contained in a program. These predicates are encoded as special Prolog predicates and can be translated to separation logic, e.g., to construct efficient secure wrappers that check if a memory graph is an instance of a learned shape predicate. ShaPE first decomposes memory graph(s) into sub-graphs, each of which exhibits a single data structure, and generates candidate shape predicates of increasing complexity, which are expressed as rule sets in Prolog. Under separation logic semantics, a meta-interpreter then performs a systematic search for a subset of rules that form a shape predicate that non-trivially and concisely captures the data structure. This repository contains an implementation of the tool in Python3 and a benchmark consisting of real-world, textbook, and synthetic examples, as well as examples taken from the research literature.

Setup

In order to run the prototype tool on a local machine, please make sure that ...

Alternatively, the Dockerfile can be used to build an image where dependencies are pre-installed. The Makefile provides three convenient targets:

  • docker-build builds the jboockmann.shape image
  • docker-test spawns a new container and automatically runs all test cases
  • docker-bash spawns a new container and provides a bash

Usage

The functionality of ShaPE is accessible via an auto-generated CLI interface (see python-fire). Invoking python -m jboockmann.shape provides an overview of all top-level commands (learn, match, etc.). Invoking python -m jboockmann.shape COMMAND --help provides further information on the parameters expected from the particular COMMAND.

The three key commands are:

  • match: Matches the memory graph against predefined shape predicates (see jboockmann/shape/rules-templates).
  • learn: Learns a shape predicate for a simple, i.e., homogeneously typed, memory graph.
  • composition: Constructs a shape predicate for a complex, i.e., non-homogeneously typed, memory graph. By default, learning a predicate is only attempted if there does not exist a predefined predicate that matches the sub-graph(s) outright.

These commands can be supplied with a single or multiple memory graphs as input. After a successful execution, the files code.c and code.pl contain the verifast proof witness and the bundled Prolog facts, respectively. The file logfile.log contains debug information.

Examples

  • Learn a shape predicate that characterizes the input memory graphs bt-parent.pl.

    python -m jboockmann.shape learn \
    examples-prolog/bt-parent.pl
  • Learn a single shape predicate that characterizes the three input memory graphs bt-null-1.pl, bt-null-2.pl, and bt-null-3.pl.

    python -m jboockmann.shape learn \
    examples-prolog/series-bt/bt-null-1.pl \
    examples-prolog/series-bt/bt-null-2.pl \
    examples-prolog/series-bt/bt-null-3.pl
  • Match the memory graph cdll.pl against all predefined shape predicates.

    python -m jboockmann.shape match \
    examples-prolog/cdll.pl
  • Match the memory graph cdll.pl only against the shape predicate defined in cdll.pl .

    python -m jboockmann.shape match \
    examples-prolog/cdll.pl \
    --template_path=jboockmann/shape/rules-templates/cdll.pl
  • Decompose and match (learn if no predefined shape predicate matches):

    python -m jboockmann.shape composition \
    examples-prolog/nesting-bt-sll.pl

Benchmark

The memory graphs used in the benchmark are located in the folders examples-prolog and examples-dsi. Each folder contains a README.md file that provides further information on each example. Note that while memory graphs in the former folder are stored in .dot format and can be viewed with a standard dot file viewer (see xdot), the memory graphs in the latter folder are stored in .pl format, which enables a fast manual modification.

ShaPE also provides means to visualize a memory graph in .pl format by internally translating it to .dot format. The following command visualizes the example SHN-sll-sll.pl:

python -m jboockmann.shape visualize examples-prolog/SHN-sll-sll.pl

Furthermore, most benchmark examples are covered in the provided integration test suite (see pytest) for which the Makefile provides convenient targets, e.g., integration-match, integration-learn, integration-composition, etc.

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