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7 results for “Decompiler”
A Taxonomy of C Decompiler Fidelity Issues
<p>This archive contains the supplementary material and dataset for the USENIX Security paper “A Taxonomy of C Decompiler Fidelity Issues.”</p> <p> </p> <p>The dataset archive contains all coded examples and a decompiler comparison summary table.<br> </p> <p>The codebook file contains a detailed description of each code in the taxonomy as well as additional details about the coding process, including a definition of alignment and a list of benign issues.</p>
CAPYBARA: Decompiled Binary Functions and Related Summaries
<p><strong>CAPYBARA</strong></p> <p>This dataset is published as part of the paper: "Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries". It includes both the training/evaluation data as well as the raw data.</p> <ul> <li>The data_split folder contains .pickle files with the test and validation repos, the train repos are all the remaining repos. It also contains a .pickle file with a dictionary that specifies the optimization level for each repository.</li> <li>In the processed_data folder, the processed datasets can be found in .csv format. The columns of the CSV are the `summaries`, the `original documentation`, the `repo`, the `source` and `decompiled` code, the `function name` and a unique `identifier`. We also include the deduplicated samples in separate CSVs.</li> <li>The processed training files can be found in the training_data folder. `Source C`, `decompiled`, `demiStripped`, and `stripped` can each be found in their corresponding folders and are split into deduplicated and regular datasets. The data is further split into .jsonl files, for the train, test, and validation sets. These .jsonl files can be loaded into CodeT5 and CodeXGlue as is. </li> <li>The raw_data folder contains all the stripped and decompiled functions without any pre-processing applied. The columns of the CSV are `repo`, the `location`, the `original` code, the corresponding `decompiled` code, the `function name`, a unique `identifier` key, and the corresponding `documentation` for both the decompiled and stripped functions.</li> </ul> <p><strong>License</strong></p> <p>Copyright 2022 ##########</p> <p>Licensed under the Apache License, Version 2.0 (the "License");</p> <p>you may not use this file except in compliance with the License.</p> <p>You may obtain a copy of the License at:</p> <p> </p> <p>http://www.apache.org/licenses/LICENSE-2.0</p> <p>Unless required by applicable law or agreed to in writing, software</p> <p>distributed under the License is distributed on an "AS IS" BASIS,</p> <p>WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</p> <p>See the License for the specific language governing permissions and</p> <p>limitations under the License.</p>
What You Get Is What You See: A Visual Markup Decompiler arXiv preprint arXiv:1609.04938
<p>What You Get Is What You See: A Visual Markup Decompiler</p> <p>arXiv preprint arXiv:1609.04938</p> <p> </p>
DIRE: A Neural Approach to Decompiled Identifier Naming
<p>This dataset is released as a companion to the paper "DIRE: A Neural Approach to Decompiled Identifier Naming", appearing in the proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019).</p> <p>It contains information generated by decompiling 3,195,962 functions found in 164,632 unique binaries generated from C code scraped from GitHub. For practicality, the dataset is partitioned into 16 archives by the first hexadecimal digit of the SHA-256 hash of the binary used to generate it. Each of the 16 archives contains approximately 10,000 JSONL files, named according to a binary's hash. Each JSONL file consists of a single JSON object per-line corresponding to a single function in the decompiled binary.</p> <p>Archives are provided in both GZIP and BZIP2 format.</p> <p>See the README file for more information.</p>
Research Artifact for paper "Decompiling x86 Deep Neural Network Executables"
<p>Research Artifact for USENIX Security 2023 paper "Decompiling x86 Deep Neural Network Executables"</p>
Understandability in Decompilation
<h2>Code Understandability in Java Decompilation</h2><p>Data and tools of the paper "Demystifying and Assessing Code Understandability in Java Decompilation".</p><h3><strong>Data</strong></h3><p>Our data in the directory data/ includes three parts:</p><ol><li>Experimental data including original source code and corresponding code decompiled by CFR, Fernflower and Jadx respectively in directory data/code/.</li><li>Calculation results in directory data/results/.</li><li>The annotated dataset data/relative_understandability.csv denotes the relative understandability of the file decompiled by the decompiler compared to the original file, in which -1, indicating that the decompiled file is less understandable than the original file; 0, signifying equivalent; and 1, indicating more understandable.</li></ol><h3><strong>Tools</strong></h3><p>Our tools in the directory tool/ includes tools for assessing the understandability of decompiled code with perplexity, Cognitive Complexity and Cognitive Complexity for Decompilation.</p><h4><strong>Environment</strong></h4><ul><li>System: Ubuntu 20.04</li><li>Python: python 3.10<ul><li>pip install kenlm</li><li>pip install javalang</li></ul></li><li>Java: JDK >= 11</li></ul><h4><strong>Perplexity Calculator</strong></h4><p>perplexity_calculator.py calculates the perplexity of n-gram models for a Java file. 5-gram.binary is our 5-gram language model.</p><ul><li>python perplexity_calculator.py <5_gram.binary> <file></li></ul><p>Where <5_gram.binary> represents path to the n-gram model, <file> represents the Java file to be evaluated.</p><h4><strong>Cognitive Complexity Calculator and Cognitive Complexity for Decompilation Calculator</strong></h4><p>CognitiveComplexityCalculator-1.0.jar calculates the Cognitive Complexity for Java files. CognitiveComplexityforDecompilationCalculator-1.0.jar calculates the Cognitive Complexity for Decompilation for Java files.</p><ul><li>java -jar CognitiveComplexityCalculator-1.0.jar <input_directory> <output_file></li><li>java -jar CognitiveComplexityforDecompilationCalculator-1.0.jar <input_directory> <output_file></li></ul><p>Where <input_directory> represents the directory of all Java files to analyze, including all the files in the subdirectories. <output_file> represents where the output file is created.</p><p>The output file is a .csv file which contains the Cognitive Complexity or Cognitive Complexity for Decompilation value for each method. Specifically it contains:</p><ul><li>Absolute Module Path: The path of the class containing the method</li><li>Module Position: The line in the .java file where the method starts</li><li>Module declaration: The method signature and return type or pattern type</li><li>Max Nesting: The maximum level of nesting reached by the method (considering as 1 the starting level)</li><li>Cognitive Complexity or Cognitive Complexity for Decompilation</li></ul><h2><strong>Reference</strong></h2><ol><li>Cognitive Complexity Calculator: <a href="https://github.com/BruhZul/cognitive-complexity-calculator">https://github.com/BruhZul/cognitive-complexity-calculator</a></li></ol><p> </p>
Code Understandability in Java Decompilation
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