From 84ee930c906b918b53245d2b42eb8ec4d4f6bf7b Mon Sep 17 00:00:00 2001 From: pmorvalho Date: Fri, 21 Jul 2023 18:57:06 +0100 Subject: [PATCH] ECAI 23 - code and data. --- .gitignore | 5 + README.md | 41 + config_gnn.sh | 23 + eval-dataset.zip | Bin 0 -> 11222201 bytes eval.py | 110 + gen_eval_dataset.py | 65 + gen_gnn_variable_mappings.sh | 74 + gen_progs_repr.py | 709 + get_baseline_repairs.sh | 58 + get_eval_dataset.sh | 64 + get_gnn_based_repairs.sh | 69 + get_ground_truth_repairs.sh | 59 + gnn.py | 180 + gnn_models.zip | Bin 0 -> 1542828 bytes helper.py | 517 + motivating_example/motivating_example.c | 8 + .../motivating_example_incorrect.c | 13 + .../motivating_example_tests/t_0.in | 1 + .../motivating_example_tests/t_0.out | 1 + .../motivating_example_tests/t_1.in | 1 + .../motivating_example_tests/t_1.out | 2 + .../motivating_example_tests/t_2.in | 1 + .../motivating_example_tests/t_2.out | 3 + .../motivating_example_tests/t_3.in | 1 + .../motivating_example_tests/t_3.out | 4 + 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utils/fake_libc_include/tar.h | 2 + utils/fake_libc_include/termios.h | 2 + utils/fake_libc_include/tgmath.h | 2 + utils/fake_libc_include/time.h | 2 + utils/fake_libc_include/trace.h | 2 + utils/fake_libc_include/ulimit.h | 2 + utils/fake_libc_include/unctrl.h | 2 + utils/fake_libc_include/unistd.h | 2 + utils/fake_libc_include/utime.h | 2 + utils/fake_libc_include/utmp.h | 2 + utils/fake_libc_include/utmpx.h | 2 + utils/fake_libc_include/wchar.h | 2 + utils/fake_libc_include/wctype.h | 2 + utils/fake_libc_include/wordexp.h | 2 + utils/fake_libc_include/xcb/xcb.h | 2 + utils/fake_libc_include/zlib.h | 33 + utils/internal/constptr.c | 9 + utils/internal/cppify.bat | 3 + utils/internal/example_c_file.c | 25 + utils/internal/fake_includes.py | 13 + utils/internal/make_fake_typedefs.py | 21 + utils/internal/memprofiling.py | 121 + utils/internal/zc.c | 107 + utils/internal/zz-ctoc.py | 27 + utils/internal/zz_parse.py | 21 + variable_mappings.zip | Bin 0 -> 8441997 bytes 174 files changed, 32336 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100755 config_gnn.sh create mode 100644 eval-dataset.zip create mode 100644 eval.py create mode 100755 gen_eval_dataset.py create mode 100755 gen_gnn_variable_mappings.sh create mode 100755 gen_progs_repr.py create mode 100755 get_baseline_repairs.sh create mode 100755 get_eval_dataset.sh create mode 100755 get_gnn_based_repairs.sh create mode 100755 get_ground_truth_repairs.sh create mode 100644 gnn.py create mode 100644 gnn_models.zip create mode 100755 helper.py create mode 100644 motivating_example/motivating_example.c create mode 100644 motivating_example/motivating_example_incorrect.c create mode 100644 motivating_example/motivating_example_tests/t_0.in create mode 100644 motivating_example/motivating_example_tests/t_0.out create mode 100644 motivating_example/motivating_example_tests/t_1.in create mode 100644 motivating_example/motivating_example_tests/t_1.out create mode 100644 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100644 utils/internal/zz-ctoc.py create mode 100644 utils/internal/zz_parse.py create mode 100644 variable_mappings.zip diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..4256e47 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +*~ +*# +#* +.#* +.DS_Store* diff --git a/README.md b/README.md new file mode 100644 index 0000000..6b64c3a --- /dev/null +++ b/README.md @@ -0,0 +1,41 @@ +# ECAI 2023 - Graph Neural Networks For Mapping Variables Between Programs + +This repository contains the code and data used for the paper "*Graph Neural Networks For Mapping Variables Between Programs*", accepted at ECAI 2023. + +We present a novel graph program representation that is agnostic to the names of the variables and for each variable in the program contains a representative variable node that is connected to all the variable's occurrences. Furthermore, we use GNNs for mapping variables between programs based on our program representation, ignoring the variables' identifiers; +We represent each program as a graph using the script gen_progs_repr.py, as explained in [1, 2]. + +To understand the entire pipeline, from the dataset generation to the repair process, the interested reader is refered to our main script 'run_all.sh'. + +The generation of the datasets (training, validation and evaluation) and the training of our GNN has been commented since it takes a few hours to compute. Only the mapping computations and the program repair tasks were left uncommented. In this repo only the evaluation dataset is available. + +- How to execute: + +``` +chmod +x run_all.sh +bash run_all.sh +``` + +## Installation Requirements + +The following script creates a new conda environment named 'gnn_env' and installs all the required dependencies in it. + +``` +chmod +x config_gnn.sh +bash confin_gnn.sh +``` + +## Introductory Programming Assignments (IPAs) Dataset + + +To generate our evaluation set of C programs we used [MultIPAs](https://github.com/pmorvalho/MultIPAs) [3], which is a program transformation framework, to augment our dataset of IPAs, [C-Pack-IPAs](https://github.com/pmorvalho/C-Pack-IPAs) [4]. + +## References + +[1] Pedro Orvalho, Jelle Piepenbrock, Mikoláš Janota, and Vasco Manquinho. Graph Neural Networks For Mapping Variables Between Programs. ECAI 2023. [PDF](). *[Accepted for Publication]* + +[2] Pedro Orvalho, Jelle Piepenbrock, Mikoláš Janota, and Vasco Manquinho. Project Proposal: Learning Variable Mappings to Repair Programs. AITP 2022. [PDF](http://aitp-conference.org/2022/abstract/AITP_2022_paper_15.pdf). + +[3] Pedro Orvalho, Mikoláš Janota, and Vasco Manquinho. MultIPAs: Applying Program Transformations to Introductory Programming Assignments for Data Augmentation. In 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2022. [PDF](https://dl.acm.org/doi/10.1145/3540250.3558931). + +[4] Pedro Orvalho, Mikoláš Janota, and Vasco Manquinho. C-Pack of IPAs: A C90 Program Benchmark of Introductory Programming Assignments. 2022. [PDF](https://arxiv.org/pdf/2206.08768.pdf). \ No newline at end of file diff --git a/config_gnn.sh b/config_gnn.sh new file mode 100755 index 0000000..ea00ff8 --- /dev/null +++ b/config_gnn.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash +#Title : config_gnn.sh +#Usage : bash config_gnn.sh +#Author : pmorvalho +#Date : July 29, 2022 +#Description : Jelle's commands to config the GNN's environment, plus the python packages needed to run MultIPAs +#Notes : +# (C) Copyright 2022 Pedro Orvalho. +#============================================================================== + +#!/usr/bin/env bash +source ~/anaconda3/etc/profile.d/conda.sh +conda create -n gnn_env python=3.9 +conda activate gnn_env +#conda install pytorch torchvision torchaudio cpuonly -c pytorch +#pip3 install torch torchvision torchaudio +pip install torch==1.11.0+cpu torchvision==0.12.0+cpu torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cpu +# check with nvidia-smi if the 'cu102' should be replaced with another cuda version +pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.11.0+cpu.html +pip install pycparser==2.21 +conda activate gnn_env + + diff --git a/eval-dataset.zip b/eval-dataset.zip new file mode 100644 index 0000000000000000000000000000000000000000..20e7d3390e9f451f3ac276e1fcdf08fe486e05a7 GIT binary patch literal 11222201 zcmd3O1z1#D+cs$+DM)unNhsZ*bk`7rfP%yf3>^Xzf(p`&5`r)^Lk~zBlr+OoL#cEO zNC*NV|K@o5yyyMC|GNI`Kj&O7=Iq&ft@Z3@uQltw@8?-tUkjgr2#!6VRv6x^hZCb;{QWG_2hr-2l9X2kEiclH)~s0n47h$o2S4}M-Y2!J7-TjYfmpP zn5Te;ovX(^I}hu7cDBx*0`{&R);Fwu9U;zkKTL$&_#aG6{F8~k|36Li^>B4@_~rI~ z7|i***!n>E&%LEGjPO=Hx8s;Q!O~IlG0s8moVvJ}gle zE->MKw81+)t0#I^0|)U8Vnn4u^B6k-)lejGS0(Ueq`0@OLP+60*TNGK$`hK)8b#C{ z8<~SQa9)1@+uT8I;F__jcDu56yS8IHCHK00T2lRh9B6&m+JWEa+mWl7MN<|X6CFcV zFR8uLc9)M?k5BXxj~&DilgU#}Wy%3fursboDwR?6gB4zhC+bUTzLH3Uh%mm_Q6@mp zF^BsFlZr8qN~AU+R!=?xQI{cm42Ys*nozN8a5ND?(}lZ29di9mea#FBcBR7e!)Xt8WU_R 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