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Supporting Artifact for "Predictable Verification using Intrinsic Definitions"

by Adithya Murali, Cody Rivera, and P. Madhusudan, 2024. Version 4.0 of Artifact.

This is the supporting artifact for "Predictable Verification using Intrinsic Definitions" in PLDI 2024. It contains Boogie and Dafny implementations of a benchmark suite of 42 data structure manipulating methods over 10 data structures, written in the Fix-What- You-Break (FWYB) verification paradigm proposed in the paper. It also contains scripts to verify the benchmarks and record verification times.

This artifact reflects the camera-ready version of the paper: a version of the artifact (v3.0) submitted for artifact evaluation that corresponds to an earlier version of the paper may be seen here.

Artifact Outline

We give an outline of the artifact's structure:

  • boogie/: Boogie implementation of the benchmark suite.

  • boogie-original/: Boogie implementation of the benchmark suite, with parametrized map updates implemented using a transplant script operating on the generated SMT query, rather than using Boogie's native support.

  • dafny/: Dafny implementation of the benchmark suite.

  • utils/: Scripts to generate table data/plots in the paper.

  • dep-locations.sh: A script file where the user points the script to its dependencies.

  • Dockerfile: Describes how to build a Docker image for this artifact.

  • LICENSE.txt: The MIT License, under which this artifact is licensed.

  • README.md: This file.

  • ids-docker.zip: Pre-built Docker image for the artifact, available as a separate file to download.

Artifact Setup

There are three ways to set up this artifact. The first way is to use a prebuilt Docker image, the second way is to build a new Docker image, and the third way is manual setup.

Use an Existing Docker Image

The provided image - ids-docker.zip - (available here for those accessing the repository elsewhere) supplies an Ubuntu 22.04 environment as well as the recommended versions of the dependencies. Below are the instructions to use this image:

  1. Install Docker Engine. See here for instructions. Another requirement is unzip.
  2. Run unzip ids-docker.zip to extract the image, ids-artifact.tar.
  3. Run docker load ids-artifact.tar to load the extracted image into Docker.
  4. To obtain an interactive shell for the container ids-artifact, run docker run -it --mount type=bind,src="$(pwd)",target=/outpwd ids-artifact /bin/bash. (the --mount option allows you to copy files to the host machine).

Build a New Docker Image

Below are instructions for building a new Docker image.

  1. Install Docker Engine. See here for instructions.
  2. In the root directory of this artifact, run docker build -t ids-artifact . to build the container.
  3. To obtain an interactive shell for the container ids-artifact, run docker run -it --mount type=bind,src="$(pwd)",target=/outpwd ids-artifact /bin/bash. (the --mount option allows you to copy files to the host machine).

Note that dep-locations.sh should not have to be modified in this case: the Dockerfile should have installed all dependencies and placed them in the user's PATH.

Manual Setup

The artifact requires a Bash shell as well as the following dependencies:

  • Python 3 (3.10.12 recommended)
    • sexpdata (1.0.1 recommended)
    • matplotlib (3.8.4 recommended)
  • Boogie 3.0.1 or later (3.1.2 recommended)
  • Z3 (4.13.0 recommended)
  • Dafny 4.1.0 or later (4.4.0 recommended)
  • GNU time and bc

The artifact has been tested on the recommended versions of the above dependencies. Below are instructions for installing these dependencies.

  1. Make sure you have Python 3 installed. To install sexpdata, an S-expression parser, use pip install sexpdata.
  2. Install Z3. A quick way, using a Python installation, is pip install z3-solver; otherwise, see the Z3 GitHub.
  3. Install Boogie and Dafny. Check out the Boogie GitHub and the Dafny GitHub for more information on how to install them.
  4. Modify dep-locations.sh to point to the dependencies. Set the PYTHON_3, BOOGIE_3, PROVER, and DAFNY_4 variables to the locations of Python, Boogie, Z3, and Dafny respectively.

Artifact Support for Paper Claims

The paper makes the following major claims:

  1. We can express a wide range of data structures using intrinsic data structure (IDS) definitions, and a wide range of methods mainpulating them using fix-what-you-break (FWYB) methodology in Boogie (answer to RQ1).
  2. We can effectively verify our Boogie methods using decidable verification condition generation and SMT solving (answer to RQ2).
  3. Generating decidable verification conditions from programs using FWYB methodology results in better performance than using other automatic verification technologies such as Dafny (answer to RQ3).

Support for Claim 1

Boogie versions of the data structures and methods implemented in Table 2 can be seen in the boogie/ directory of the artifact. Each data structure is contained in its own directory, e.g., single-linked-list/. The data structure definition, local conditions, and FWYB manipulation macros are contained in a file with the same name as the directory: e.g., single-linked-list.bpl. The impact set proofs for each data structure are contained in a file called impact-sets.bpl.

Note that there are actually 44 methods defined across the 10 data structures: but two of them (bst-scaffolding::fix-depth, scheduler-queue::fix-depth) are excluded from Table 2 in Section 5 since they contain only ghost code.

We elaborate on the condition that programs be well-behaved (Section 3.4), which is required to use FWYB soundly, for one benchmark: single-linked-list::insert. This is done by extra comments in the files single-linked-list.bpl, impact-sets.bpl, and insert.bpl (in the directory boogie/single-linked-list).

Support for Claim 2 (~5min)

We have written a script that will verify each of the methods in the boogie/ directory using Boogie. The script will cross-check that the generated VCs are decidable, and it will also report verification times.

Here is a procedure for running the benchmarks. It assumes you are in the top-level directory of this artifact.

  1. Run cd boogie.
  2. Run ./boogie-all.sh.

You can also verify methods individually using ./boogie-method.sh DATASTRUCTURE METHOD.

These experiments in particular support the Verif. Time (s) column on Table 2 in Section 5 of the paper. The time values on the table for a given method are the sum of the time reported by this script for that method, and the time reported by this script for the impact set verification of a particular data structure.

Generating data for Table 2

A script under the utils/ directory, gen-tab2.py, generates the final time values for the Verif. Time (s) column in Table 2 by performing the above calculation. To generate these values, complete the following two steps (assuming you are in the top-level directory):

  1. Run cd utils.
  2. Run python3 ./gen-tab2.py RESULTS, where RESULTS is the output from running ./boogie-all.sh.

A sample RESULTS is utils/artifact-boogie-results.txt.

Support for Claim 3 (~8hr full, ~2.75hr partial)

We implement our suite of methods using Dafny, a widely used high-level verification language which does not generate decidable VCs. The Dafny code implemented for Claim 3 is seen in the dafny/ directory, and is structured analogously to how the Boogie code is structured.

We have additionally implemented a script that will verify each of the methods in dafny/ using Dafny, as well as provide timings. Here are instructions for running this script, assuming you are at the top-level directory.

  1. Run cd dafny.
  2. Run ./dafny-all.sh.

Again, you can verify methods individually using ./dafny-method.sh DATASTRUCTURE METHOD.

./dafny-all.sh excludes the two worst-performing methods: red-black-tree::insert and scheduler-queue::bst-remove-root, which take over 2 hours to verify on the experimental machine used in this paper. To include these benchmarks, either verify them individually, or run the ./dafny-all-plus-lt.sh script.

This experiment, in conjunction with the experiments to support Claim 2, supports the scatter plot seen near RQ3 in Section 5.3 of the paper, plotting verification times for Boogie vs. Dafny.

Generating the scatter plot for RQ3

A script under the utils/ directory, gen-scrq3.py, generates the scatter plot comparing Dafny and Boogie for RQ3. To generate this plot, complete the following two steps (assuming you are in the top-level directory):

  1. Run cd utils.
  2. Run python3 ./gen-scrq3.py DAFNY-RESULTS BOOGIE-RESULTS, where DAFNY-RESULTS is the output from running ./dafny-all.sh, BOOGIE-RESULTS is the output from running ./boogie-all.sh, and DAFNY-RESULTS is the output from running ./dafny-all.sh.
  3. Run cp scatter.png /outpwd to copy the generated scatter plot to the host machine.

A sample DAFNY-RESULTS is in utils/artifact-dafny-results.txt, while a sample BOOGIE-RESULTS is in utils/artifact-boogie-results.txt. Note that the script will work even with omissions in the Dafny results (e.g., omitting red-black-tree::insert and scheduler-queue::bst-remove-root as is done by default).

Boogie Benchmarks using a Transplant Script to implement Parametrized Updates

In our original benchmark suite, we implemented parametrized map updates using a custom script that modifies the SMT queries Boogie generates. However, a reviewer pointed out that Boogie has native support for such updates, and the present version of the paper and artifact takes advantage of this.

We include the original implementation of the benchmark suite in boogie-original/. Like the other benchmark suites above, one can verify all methods with ./boogie-all.sh and ./boogie-method.sh DATASTRUCTURE METHOD. Verifying all benchmarks takes ~5min, similarly to boogie/.

License

Copyright (c) 2024 Cody Rivera.

This artifact is licensed under the MIT licence. Please see LICENSE.txt for more details.