cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11652366
title: Towards Human-interpretable Explanation in Code Clone Detection using LLM-based Post Hoc Explainer

description: Overview

This artifact accompanies the APSEC 2025 Research Track submission on Towards Human-interpretable Explanation in Code Clone Detection using LLM-based Post Hoc Explainer. The research focuses on using Large Language Models (LLMs) with local neighborhood sampling with in-context learning to provide explanations for GraphCodeBERT's code clone detection predictions.

 

Artifact Contents

1. Core Implementation





 GraphCodeBERT_CloneDetection.ipynb: Complete Jupyter notebook containing:



GraphCodeBERT model setup and training

Code clone detection experiments on BigCloneBench and Google Code Jam datasets

In-Context Learning prompt generation for explanation

LLM-based explanation pipeline





2. In-Context Learning Prompts (ICL_Prompts)

The ICL_Prompts directory contains structured prompts for 10 different Google Code Jam pairs (data_GoogleCodeJam_Pair1 through data_GoogleCodeJam_Pair10) used in the manual validation step, each with varying context lengths:



layer_size4.txt: prompts with 4 examples

layer_size8.txt: prompts with 8 examples

layer_size16.txt: prompts with 16 examples

layer_size32.txt: prompts with 32 examples

layer_size48.txt: prompts with 48 examples

layer_size64.txt: prompts with 64 examples


Each prompt file is based on the prompt template structure:



Context: Description of GraphCodeBERT model

Dataset: Examples with code pairs, confidence scores, and predictions

Question: A query asking for explanation of a specific code pair

Instructions for generating explanations


3. Validation Results





Manual_Validation_Results.xlsx: Manual evaluation results of generated explanations including quality assessments and human annotations.



Usage Instructions

Prerequisites

pip install torch transformers tree_sitter scikit-learn

 

Running the Code



Open `GraphCodeBERT_CloneDetection.ipynb` in Jupyter/Colab

Follow the notebook cells to:



Set up the GraphCodeBERT model

Load and preprocess datasets

Run clone detection experiments

Generate prompts for explanation




File Organization

├── GraphCodeBERT_CloneDetection.ipynb # Main implementation

├── Manual_Validation_Results.xlsx # Human evaluation results

├── ICL_Prompts/ # Generated prompts

│ ├── data_GoogleCodeJam_Pair1/ # Pair 1 prompts

│ │ ├── layer_size4.txt # 4-example prompts

│ │ ├── layer_size8.txt # 8-example prompts

│ │ └── ... # Other sizes

│ ├── data_GoogleCodeJam_Pair2/ # Pair 2 prompts

│ └── ... # Additional pairs

└── README.md # This file

Citation

Please cite this artefact using the DOI provided by Zenodo.

Contact

For questions about this artifact or research, please contact the authors [to be updated due to double-blind policy].
DOI: 10.5281/zenodo.16264743
title: Towards Human-interpretable Explanation in Code Clone Detection using LLM-based Post Hoc Explainer

description: Overview

This artifact accompanies the APSEC 2025 Research Track submission on Towards Human-interpretable Explanation in Code Clone Detection using LLM-based Post Hoc Explainer. The research focuses on using Large Language Models (LLMs) with local neighborhood sampling with in-context learning to provide explanations for GraphCodeBERT's code clone detection predictions.

 

Artifact Contents

1. Core Implementation





 GraphCodeBERT_CloneDetection.ipynb: Complete Jupyter notebook containing:



GraphCodeBERT model setup and training

Code clone detection experiments on BigCloneBench and Google Code Jam datasets

In-Context Learning prompt generation for explanation

LLM-based explanation pipeline





2. In-Context Learning Prompts (ICL_Prompts)

The ICL_Prompts directory contains structured prompts for 10 different Google Code Jam pairs (data_GoogleCodeJam_Pair1 through data_GoogleCodeJam_Pair10) used in the manual validation step, each with varying context lengths:



layer_size4.txt: prompts with 4 examples

layer_size8.txt: prompts with 8 examples

layer_size16.txt: prompts with 16 examples

layer_size32.txt: prompts with 32 examples

layer_size48.txt: prompts with 48 examples

layer_size64.txt: prompts with 64 examples


Each prompt file is based on the prompt template structure:



Context: Description of GraphCodeBERT model

Dataset: Examples with code pairs, confidence scores, and predictions

Question: A query asking for explanation of a specific code pair

Instructions for generating explanations


3. Validation Results





Manual_Validation_Results.xlsx: Manual evaluation results of generated explanations including quality assessments and human annotations.



Usage Instructions

Prerequisites

pip install torch transformers tree_sitter scikit-learn

 

Running the Code



Open `GraphCodeBERT_CloneDetection.ipynb` in Jupyter/Colab

Follow the notebook cells to:



Set up the GraphCodeBERT model

Load and preprocess datasets

Run clone detection experiments

Generate prompts for explanation




File Organization

├── GraphCodeBERT_CloneDetection.ipynb # Main implementation

├── Manual_Validation_Results.xlsx # Human evaluation results

├── ICL_Prompts/ # Generated prompts

│ ├── data_GoogleCodeJam_Pair1/ # Pair 1 prompts

│ │ ├── layer_size4.txt # 4-example prompts

│ │ ├── layer_size8.txt # 8-example prompts

│ │ └── ... # Other sizes

│ ├── data_GoogleCodeJam_Pair2/ # Pair 2 prompts

│ └── ... # Additional pairs

└── README.md # This file

Citation

Please cite this artefact using the DOI provided by Zenodo.

Contact

For questions about this artifact or research, please contact the authors [to be updated due to double-blind policy].
DOI: 10.15468/dl.ytrdtr
title: Occurrence Download

description: A dataset containing 141 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Glycine max",
 "HasCoordinate is true",
 "Year is one of (1898, 2024)"
 ]
}

 The dataset includes 141 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021366-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ff7h3t
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hexastylis virginica, Asarum virginianum, Homotropa macranthum, Asarum memmingeri, Asarum maculosum, Homotropa macranthum, Asarum macanthum, Asarum virginicum, Asarum grandiflorum, Hexastylis memmingeri, Heterotropa virginica)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 42 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021367-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qgcveb
title: Occurrence Download

description: A dataset containing 835 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Chinese Taipei)",
 "TaxonKey is Uncaria rhynchophylla"
 ]
}

 The dataset includes 835 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021368-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12532856
title: Full TMDB Movies Dataset 2024 (1M Movies)
DOI: 10.17031/68766a94f3497
title: 2020 DAERA Sediment infauna data

description: Marine Benthic Invertebrate Analysis.
description: Data archived into DASSH from Marine Recorder Online.
DOI: 10.15468/dl.5tn79y
title: Occurrence Download

description: A dataset containing 2662 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tridax procumbens",
 "Country is India",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2662 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021369-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.duxgjw
title: Occurrence Download

description: A dataset containing 350 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tridax procumbens",
 "Country is Indonesia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 350 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021370-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17045/sthlmuni.26164570
title: X-ray microCT lizard brain data and labels. Trained network using Biomedisa with different number of training datasets.

description: <b>28_pbo24_100kV9WLE2Obj0p4xBin2t1sP801_recon.zip: </b>X-ray tomography data of a lizard head (<i>Podarcis bocagei</i>) using Zeiss Xradia Versa 520 at Stockholm University Brain Imaging Center (SUBIC).<b>SegmFromDLalgorithm.zip</b>: X-ray microCT (<i>microcomputed tomography</i>) data of lizard heads, cropped down to brain part, and the corresponding labels of brain regions segmented by deep learning algorithm.<b>SegmForDLmanual.zip</b>: X-ray microCT data of lizard heads, cropped down to brain part, and the corresponding labels of brain regions segmented semi-manually used as training dataset for deep learning.<b>06_recon.zip</b>: an example of original X-ray microCT data of a lizard head before cropping.The trained networks below can be used in Biomedisa https://biomedisa.info :<b>NetworkTrainedWith5data.tar.h5: </b>trained network using 5 datasets<b>NetworkTrainedWith7data.tar.h5</b><b>: </b>trained network using 7 datasets<b>NetworkTrainedWith9data.tar.h5</b><b>: </b>trained network using 9 datasets<b>NetworkTrainedWith11data.tar.h5</b><b>: </b>trained network using 11 datasets
DOI: 10.17045/sthlmuni.26164570.v2
title: X-ray microCT lizard brain data and labels. Trained network using Biomedisa with different number of training datasets.

description: <b>SegmFromDLalgorithm.zip</b>: X-ray microCT (<i>microcomputed tomography</i>) data of lizard heads, cropped down to brain part, and the corresponding labels of brain regions segmented by deep learning algorithm.<b>SegmForDLmanual.zip</b>: X-ray microCT data of lizard heads, cropped down to brain part, and the corresponding labels of brain regions segmented semi-manually used as training dataset for deep learning.<b>06_recon.zip</b>: an example of original X-ray microCT data of a lizard head before cropping.The trained networks below can be used in Biomedisa https://biomedisa.info :<b>NetworkTrainedWith5data.tar.h5: </b>trained network using 5 datasets<b>NetworkTrainedWith7data.tar.h5</b><b>: </b>trained network using 7 datasets<b>NetworkTrainedWith9data.tar.h5</b><b>: </b>trained network using 9 datasets<b>NetworkTrainedWith11data.tar.h5</b><b>: </b>trained network using 11 datasets
DOI: 10.17045/sthlmuni.26164570.v3
title: X-ray microCT lizard brain data and labels. Trained network using Biomedisa with different number of training datasets.

description: <b>28_pbo24_100kV9WLE2Obj0p4xBin2t1sP801_recon.zip: </b>X-ray tomography data of a lizard head (<i>Podarcis bocagei</i>) using Zeiss Xradia Versa 520 at Stockholm University Brain Imaging Center (SUBIC).<b>SegmFromDLalgorithm.zip</b>: X-ray microCT (<i>microcomputed tomography</i>) data of lizard heads, cropped down to brain part, and the corresponding labels of brain regions segmented by deep learning algorithm.<b>SegmForDLmanual.zip</b>: X-ray microCT data of lizard heads, cropped down to brain part, and the corresponding labels of brain regions segmented semi-manually used as training dataset for deep learning.<b>06_recon.zip</b>: an example of original X-ray microCT data of a lizard head before cropping.The trained networks below can be used in Biomedisa https://biomedisa.info :<b>NetworkTrainedWith5data.tar.h5: </b>trained network using 5 datasets<b>NetworkTrainedWith7data.tar.h5</b><b>: </b>trained network using 7 datasets<b>NetworkTrainedWith9data.tar.h5</b><b>: </b>trained network using 9 datasets<b>NetworkTrainedWith11data.tar.h5</b><b>: </b>trained network using 11 datasets
DOI: 10.5281/zenodo.16261310
title: Global soil and soil-groundwater PFAS concentrations database

description: We established a global soil database on PFAS, as well as a separate database on groundwater from studies with paired soil and groundwater observations.This database contains 106 papers and almost 2000 soil points from 6 continents spanning 19 countries.It also includes the country, article title, publication year, journal title, site type, sampling time, longitude and latitude, soil type, various types of PFAS and soil properties.

When using the data, please cite repositories .

For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).
DOI: 10.5281/zenodo.16261311
title: Global soil and soil-groundwater PFAS concentrations database

description: We established a global soil database on PFAS, as well as a separate database on groundwater from studies with paired soil and groundwater observations.This database contains 106 papers and almost 2000 soil points from 6 continents spanning 19 countries.It also includes the country, article title, publication year, journal title, site type, sampling time, longitude and latitude, soil type, various types of PFAS and soil properties.

When using the data, please cite repositories .

For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).
DOI: 10.5281/zenodo.16264998
title: Mapping Artificial Intelligence and Metaverse Integration in Nursing Education: A Bibliometric Analysis
DOI: 10.5281/zenodo.16264999
title: Mapping Artificial Intelligence and Metaverse Integration in Nursing Education: A Bibliometric Analysis
DOI: 10.15468/dl.jyy4sq
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tridax procumbens",
 "Country is Kiribati",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021371-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.2908/mar_pa_qm_lv
title: Nach/aus Haupthäfen beförderte Passagiere (ohne Kreuzfahrtpassagiere) - Lettland - Vierteljährliche Daten
title: Passagers (à l'exclusion des passagers de croisières) transportés de/vers les principaux ports - Lettonie - données trimestrielles
title: Passengers (excluding cruise passengers) transported from/to the main ports - Latvia - quarterly data
DOI: 10.15468/xzjtp5
title: Fauna of flesh flies (Diptera: Sarcophagidae) in the Republic of Mordovia (European Russia)

description: The reliable locations of 58 species of Sarcophagidae from 10 genus in the Republic of Mordovia (the center of European Russia) are indicated. The fauna of the Sarcophagidae of two large Protected Areas has been studied. We reliably identified a total of 2,162 Sarcophagidae males.
DOI: 10.15468/dl.jtzs2q
title: Occurrence Download

description: A dataset containing 954 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anemone amurensis"
 ]
}

 The dataset includes 954 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021373-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mjn8rd
title: Occurrence Download

description: A dataset containing 35602 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lysichiton americanus)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 35602 records from 134 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021372-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3dr6av
title: Occurrence Download

description: A dataset containing 1547 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Rupornis magnirostris)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 1547 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021374-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5zq62r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Rupornis magnirostris)",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021375-250717081556266/datasets/export for details.
DOI: 10.15468/dl.ddfkp7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Robinia pseudoacacia",
 "Country is Myanmar",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021377-250717081556266/datasets/export for details.
DOI: 10.15468/dl.cm25vd
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rattus rattus",
 "Country is Japan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 39 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021379-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p9tcka
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Robinia pseudoacacia",
 "Country is Norway",
 "HasCoordinate is true"
 ]
}

 The dataset includes 67 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021378-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23708/tlryum
title: Taxonomy of the native angiosperm flora of New Caledonia in 1911, 1948, 2001, 2012 and 2024

description: The table lists all angiosperm species from New Caledonia cited in five successive checklists published in 1911, 1948, 2001, 2012 and 2024. The table track the fate of each species in the five checklissts and in particular the changes in genus and family.
DOI: 10.15468/dl.uhzbcv
title: Occurrence Download

description: A dataset containing 361 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Colección de plantas vasculares y no vasculares del Archipiélago de Juan Fernández e islas Desventuradas"
 ]
}

 The dataset includes 361 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021381-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29608901.v1
title: <b>Simulation part of the code: </b><b>Allocation of responsibility for carbon emission reduction, carbon sequestration pathways and high-quality development of agriculture from the perspective of the</b><b>Agricultural Industrialization Coalition</b>

description: Green agriculture is necessary for high-quality agricultural development, emphasizing ecological protection, resource conservation, and product quality for sustainable growth. This study adopts a dynamic deductive approach to investigate the responsibility and pathways for reducing carbon emissions in agriculture. The study assessed the changes in total factor productivity of green agriculture in Chinese provinces using the SBM-GML index model. The study also assessed agricultural modernization, high-quality development, and sectoral coordination through the entropy model and coupled coordination model. Spatial autocorrelation and Markov chain methods were applied to analyze trends. The results of the study elucidated the carbon emission reduction model of “Government &gt; Agricultural Industrialization Entrepreneurs &gt; Farmers”, in which government policies, green technology promotion, and low-carbon practices are key. Pilot policy innovations by agricultural industrialization coalitions have significantly contributed to agricultural development, demonstrating positive effects in terms of coupling coordination, green productivity growth, and modernization. In most regions, technological innovation has mainly contributed to green productivity growth, but the phenomenon of “the strong getting stronger and the weak getting weaker ”persists. The geographical distribution of high-quality agricultural development remains stable, interregional mobility is low, and the spatial lag effect is evident in the stage of regional development. The main objective of this study is to provide policy recommendations for promoting the development of green agriculture and enhancing high-quality development in China.
DOI: 10.5281/zenodo.15862207
title: From simulations to observations. Data release of mock TNG50 galaxies at 0.3 < z < 0.7 for WEAVE-StePS

description: We release the dataset linked to the publication Ikhsanova et al. 2025, consisting of multi-wavelength imaging and spectroscopic data of ∼ 750 galaxies at redshift z = 0.3, 0.5, and 0.7. In particular, we created this mock datasets from the TNG50 cosmological simulation, performed radiative transfer calculations using SKIRT v9.0 and extracted WEAVE-like fiber spectra (central 1.3 arcsec). 

Complementary, we built a mock imaging dataset obtained by convolving SKIRT flux densities with the response curve for multiple filters. We created SDSS ($ugriz$) images with spatial resolution of 0.396~arcsec~px$^{-1}$, ACS/WFC3 (F435W, F606W, F814W, F105W, F125W, F160W) images with spatial resolution of 0.03 and 0.06 arcsec~px$^{-1}$,  and NIRCam (F090W, F115W, F150W, F200W, F277W, F356W, F444W) images with native spatial resolution of 0.031 and 0.063 arcsec~px$^{-1}$ for the short and long channel, respectively.

This work provides a fundamental bench-test for the forthcoming WEAVE observations, providing the community with realistic mock spectra of galaxies that can be used to test currently available tools for deriving first-order stellar populations parameters (i.e., ages and metallicities) as well as more complex diagnostics such as mass and star formation histories.