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22 results for “code search”
On the Effectiveness of Transfer Learning for Code Search - Replication Package
<p>This repository represents the replication package for the paper <em>On the Effectiveness of Transfer Learning for Code Search</em>.</p> <p>The paper is published in the journal <em>IEEE Transactions on Software Engineering (TSE)</em>.</p> <p>In this replication package, we provide all the data and scripts we used in our study.</p>
Code and data associated with: Searching the web builds fuller picture of arachnid trade
<p>Data and code used in the paper: Searching the web builds fuller picture of arachnid trade. Throughout the methods we have indicated the stage of analysis each data component was used and the code script connected. We have numbered to code and data supplements to reflect as closely as possible the order in which data generation and summary was undertaken. The following provide additional details linked to each of the data files.</p> <p>Data S1 - Website data: lang = language of the search engine used, ad hoc websites had language described after discovery; engine = the search engine used; page = the page on which the website appeared from the search engine; searchdate = search date in YYYY-mm-dd HH:MM:SS; link = link to the webpage, redacted to protect website identity; reviewdate = date revewied for arachnids being sold and search strategy; sells = whether the website sells arachnids (1 == sells); allow = whether the site explcicilt forbids automated searching (1 == allows, NA when search method was not fully automated, e.g., single page); type = the type of the website (e.g., trade, classified ads); order = whether arachnids where organised in a particular ways; target = a refined target URL to start search; method = the search method chosen, see methods for details; refine = any refinement or filter than could constrain the scope of the website to be searched; spages = the number of pages required to cycle through to cover the entire stock (also separated by ; if multiple cycles where needed or multiple single pages could be easily collected); prelimCheck = whether the website passed initial checks for arachnid selling; notes = any details that might need special attention during searches; webID = code used for subsequent data summary.</p> <p>Data S2 - Raw keyword searches outputs: species keywords. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (only applies to Data S3); webID = the website ID.</p> <p>Data S3 – Raw keyword searches outputs: genus keywords. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (multiple detections separated by ;); webID = the website ID.</p> <p>Data S4 - Raw keyword search outputs: temporal sample. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (multiple detections separated by ;); webID = the website ID; timestamp.parse = the timestamp extracted from the archived web page; year = a simplified timestamp including only the year.</p> <p>Data S5 - LEMIS data used. An arachnid filtered version of <sup>74,75</sup>.</p> <p>Data S6 - CITES trade database data used <sup>76</sup>.</p> <p>Data S7 - CITES appendices data used <sup>77</sup>.</p> <p>Data S8 - IUCN Redlist data used <sup>78</sup>.</p> <p>Data S9 - Compiled final dataset, with data deriving from WSC, Scorpion files, ITIS, WAM and the data collection process. speciesId = a numeric code, one per species; clade = the clade the species belongs to; family = the family the species belongs to; genus = the genus of the species; species = the species epithet; author = the species authority name; year = the species authority year; parentheses = whether parentheses are needed with the authority; distribution = WSC original distribution descriptions; invalid = whether the species is considered valid; source = the species source, either World Spider Catalogue, Scorpion files, ITIS or WAM; accName = the species binomial being used as our accepted name; allNames = the accepted species binomial and all synonyms; allGenera = the accepted genus, and all other genera the species has belonged to at one point; onlineTradeSnap = whether the species was detected via a match to the accName in the snapshot data; onlineTradeSnap_Any = whether the species was detected via any synonym in the snapshot data; onlineTradeSnap_genus = whether the genus was detected via a match to the genus in the snapshot data; onlineTradeSnap_genusAny = whether the genus was detected via any synonym in the snapshot data; onlineTradeTemp = whether the species was detected via a match to the accName in the temporal data; onlineTradeTemp_Any = whether the species was detected via any synonym in the temporal data; onlineTradeTemp_genus = whether the genus was detected via a match to the genus in the temporal data; onlineTradeTemp_genusAny = whether the genus was detected via any synonym in the temporal data; onlineTradeEither = whether the species was detected via a match to the accName in the temporal data or snapshot data; onlineTradeEither_Any = whether the species was detected via any synonym in the temporal data or snapshot data; LEMIStrade = whether the species was detected via a match to the accName in the LEMIS data; LEMIStrade_Any = whether the species was detected via any synonym in the LEMIS data; LEMIStrade_genus = whether the genus was detected via any synonym in the LEMIS data; LEMIStrade_genusAny = whether the genus was detected via any synonym in the LEMIS data; CITEStrade = whether the species was detected via a match to the accName in the CITES trade database data; CITEStrade_Any = whether the species was detected via any synonym in the CITES trade database data; CITEStrade_genus = whether the genus was detected via any synonym in the CITES trade database data; CITEStrade_genusAny = whether the genus was detected via any synonym in the CITES trade database data; CITESapp = the CITES appendix the species is listed under using an exact match to the accName; CITESapp_Any = the CITES appendix the species is listed under using any match to any of the species’ synonyms; redlist = the IUCN Redlist category the species is listed under using an exact match to the accName; redlist_Any = the IUCN Redlist category the species is listed under using any match to any of the species’ synonyms; extactMatchTraded = the species is detected in any of the trade sources via a match to the accName; anyMatchTraded = the species is detected in any of the trade sources via a match to any species’ synonym.</p> <p>Data S10 - Forum listings of “What species are you currently keeping” from an online fora posted between 9th September 2021 and 9th October 2021, to provide an idea of online discussions. Each user with a separate list is provided in a separate tab. Morph_collector is the same as poster1, but the potential cryptic species or morphs are noted separately to make them clearer.</p> <p>Data S11 – Distribution information for spiders. Only two columns used in summaries: accName = the accepted name used throughout summaries; NAME = the country name the spider occurs in.</p> <p>Data S12 - Distribution information for scorpions. species = the accepted name used throughout summaries; NAME = the country name the scorpions occurs in.</p> <p>Code S1 - Search URL Extract.R</p> <p>Code S2 - Retrieve web data.R</p> <p>Code S3 - Temporal Classified Ads.R</p> <p>Code S4 - Keyword Generation.R</p> <p>Code S5 - Keyword Search.R</p> <p>Code S6 - LEMIS filter and summary.R</p> <p>Code S7 - Compiling results.R</p> <p>Code S8 - Summary Figures.R</p> <p>Code S9 - Temporal Figures.R</p> <p>Code S10 - New description figure.R</p> <p>Code S11 - Term exploration.R</p> <p>Code S12 - LEMIS summary and mapping.R</p>
CRISPS coding scheme for complex casual leisure search requests — Auxiliary materials
<p>This repository contains auxiliary materials related to the CRISPS coding scheme for complex casual leisure search requests. The CRISPS coding scheme is described in the article "Understanding Complex Casual Leisure Information Needs: An Analysis of Search Requests for Books, Games, Movies and Music" by Toine Bogers (= corresponding author), Maria Gäde, Marijn Koolen, Vivien Petras, and Mette Skov. The article provides a comprehensive analysis of complex search requests in the casual leisure domains of books, games, movies and music using the CRISPS coding scheme.</p> <p>This repository contains the following auxiliary materials:</p> <ul> <li>The CRISPS codebooks for all four domains along with examples for each code and a comparison between the four domains (<strong>codebook.pdf</strong>)</li> <li>The CRISPS coding scheme figures from the article in a single PDF file (<strong>coding-scheme.pdf</strong>)</li> <li>The CRISPS coding scheme from the article in a machine-readable TSV-format (<strong>coding-scheme.tsv</strong>)</li> <li>All 2000 posts along with their assigned CRISPS codes in TSV-format (<strong>all-posts.tsv</strong>)</li> <li>The appendix belonging to the article</li> </ul>
Data sets and R codes for "It's about her: male within-season movements are related to mate searching in a songbird"
<p><strong>Abstract</strong></p><p>In species with resource-defense mating systems (such as most temperate-breeding songbirds), male dispersal is often considered to be limited in both frequency and spatial extent. When dispersal occurs within a breeding season, the favored explanation is ecological resource tracking. In contrast, movements of male birds associated with temporary emigration, such as polyterritoriality (i.e., defense of an additional location after attracting a female in the initial territory), are usually attributed to mate searching. We suggest that male dispersal and polyterritoriality are functionally related, and that mate searching may be a unifying hypothesis for predicting the within-season movements of male songbirds. Here, we test three key predictions derived from this hypothesis in Wood Warblers <i>Phylloscopus sibilatrix</i>. We collected data on the spatial behavior of 107 males between 2017 and 2019, and related male movements to a new territory (both in a dispersal and polyterritorial context) to mating potential in the current territory. Most males dispersed from their territories within days or weeks after failing to attract a female, despite occupying territories in apparently suitable habitat. Probability of polyterritoriality by paired males increased after the peak fertile period of their mate. Males never dispersed following nest predation if the female remained to renest. Thus, our data are consistent with the hypothesis that both movement types are functionally related to mate searching.</p>
Code and data for: Emergence of spatially structured populations by area-concentrated search
<p>The idea that populations are spatially structured has become a very powerful concept in ecology, raising interest in many research areas. However, despite dispersal being a core component of the concept, it typically does not consider the movement behavior underlying any dispersal. Using individual-based simulations in continuous space, we investigate the emergence of a spatially structured population in landscapes with spatially heterogeneous resource distribution and with organisms following simple area-concentrated search (ACS); individuals do not, however, perceive or respond to any habitat attributes per se but only to their foraging success. We investigated effects of different resource clustering patterns in landscapes (single large cluster vs. many small clusters) and different resource densities on spatial structure of populations and movement between resource clusters of individuals. As the results, we found that foraging success increased with increasing resource density and decreasing number of resource clusters. In a wide parameter space, the system exhibited attributes of a spatially structured population with individuals concentrated in areas of high resource density, searching within areas of resources, and 'dispersing' in a straight line between resource patches. 'Emigration' was more likely from patches that were small or of low quality (low resource density), but we observed an interaction effect between these two parameters. With the ACS implemented, individuals tended to move deeper into a resource cluster in scenarios with moderate resource density than in scenarios with high resource density. 'Looping' from patches was more likely if patches were large and of high quality. Our simulations demonstrate that spatial structure in populations may emerge if critical resources are heterogeneously distributed and if individuals follow simple movement rules (such as ACS). Neither the perception of habitat nor an explicit decision to emigrate from a patch on the side of acting individuals is necessary for the emergence of spatial structure.</p>
Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform"
<p>Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform".</p> <p>The R code containsboth code for simulation and code for estimation.</p>
Code and data for: Emergence of spatially structured populations by area-concentrated search
Open the record for dataset details and reuse information.
Data and code for: A delayed response in the area-concentrated search can improve foraging success
Open the record for dataset details and reuse information.
Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation
<p>Data Package for "Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation"</p> <p>This package contains data generated as part of our experiments on EvoSuite and Defects4J.</p> <p>This paper is currently under submission.</p> <p>This package contains experimental data and the algorithms that generated them in folder "framework\test\". Specifically, in the folder "framework\test\Experiments", the data folders contain the suites generated by each technique for each project and fault ID. </p> <p>If you have questions, please contact Afonso Fontes at afonsohfontes@gmail.com.</p>
R code and dataset to "Monetizing Spillover Effects in the Creative Industries: the Impact of Live Music Performances on Youtube Searches"
<p>Content:</p> <ol> <li>The script<strong> main_script.R</strong> includes code to run a regression discontinuity (RD) design and validation and falsification of estimated results</li> <li>The folder <strong>data</strong> contains two files: <ol> <li>bands_2016_2019.csv: a dataset of performers with additional information for each one.</li> <li>festivals_2016_2019.csv: a dataset of video search activity (as retrieved from Google Trends) for performers in file bands_2016_2019.csv</li> </ol> </li> <li>The folder <strong>source</strong> contains two additional R scripts: <ol> <li>data_preparation.R: generates the long dataset used to estimate RD effects</li> <li>status_simulation.R: randomly assigns treattment status to performers and estimates RD effects. Note this may take a long time to run. Parallel code is used: the number of cores has been set to 4. </li> </ol> </li> <li>The folder simulation_results contains simulated data after running the script status_simulation.R.</li> </ol>
ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search – Replication Package
<p>This is the replication package associated with the paper "<em>ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search</em>" accepted at the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023) – Technical Track. Cite this paper using the following:</p> <p><em>@inproceedings{pan2023atm,<br> title={ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search},<br> author={Pan, Rongqi and Ghaleb, Taher A. and Briand, Lionel},<br> booktitle={Proceedings of the 45th IEEE/ACM International Conference on Software Engineering},<br> year={2023},<br> pages={1--12}<br> }</em></p> <p><strong>Replication Package Contents:</strong><br> The replication package contains all the necessary data and code required to reproduce the results reported in the paper. We also provide the results for other minimization budgets, and detailed <em>FDR,</em> execution time, and statistical test results. In addition, we provide the data and code required to reproduce the results of baselines techniques: FAST-R and random minimization.</p> <p><strong>Data:</strong><br> We provide in the <em><strong>Data</strong></em> directory the data used in our experiments, which is based on 16 projects from <a href="https://github.com/rjust/defects4j">Defects4J</a>, whose characteristics can be found in <em><strong>Data/subject_projects.csv</strong></em><em>.</em></p> <p><strong>Code:</strong><br> We provide in the <em><strong>Code</strong></em> directory the code and scripts (Java, Python, and Bash) required to run the experiments and reproduce the results.</p> <p><strong>Results:</strong><br> We provide in the <em><strong>Results</strong></em> directory the results for each technique independently, and also a summary of all results together for comparison purposes. The source code for this step is in the <em><strong>Code/ATM/CodeToAST</strong></em> directory. The source code for this step is in the <em><strong>Code/ATM/Similarity</strong></em> directory.</p> <p><strong>_________________________________</strong></p> <p><strong>ATM - Code to AST transformation:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory)</p> <p><strong>Input:</strong><br> All zipped data files should be unzipped before running each step.<br> * Data/test_suites/all_test_cases.zip → Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases.zip → Data/test_suites/changed_test_cases<br> * Data/test_suites/relevant_test_cases.zip → Data/test_suites/relevant_test_cases</p> <p><strong>Output:</strong><br> * Data/ATM/ASTs/all_test_cases<br> * Data/ATM/ASTs/changed_test_cases</p> <p><strong>Running the experiment:</strong><br> To generate ASTS for all test cases in the project test suites, the <em><strong>Code/ATM/CodeToAST/src/CodeToAST.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/CodeToAST/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/CodeToAST bash transform_code_to_ast.sh</code></pre> <p>Each test file in the <em><strong>Data/test_suites/all_test_cases</strong></em> and <em><strong>Data/test_suites/changed_test_cases</strong></em> directories is parsed to generate a corresponding AST for each test case method (saved in an XML format in <strong>Data/ATM/ASTs/all_test_cases</strong> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> for each project version)<br> <strong>_________________________________</strong></p> <p><strong>ATM - Similarity Measurement:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em><strong> </strong>files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory)<br> <br> <strong>Input:</strong><br> * Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases<br> <br> <strong>Output:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Running the experiment:</strong><br> To measure the similarity between each pair of test cases, the <em><strong>Code/ATM/Similarity/src/SimilarityMeasurement.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/Similarity/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Similarity bash measure_similarity.sh</code></pre> <p>ASTs of each project in the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directories are parsed to create pairs of ASTs containing one test case from the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> directory with another test case from the <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directory (redundant pairs are discarded). Then, all similarity measurements are saved in the <em><strong>Data/ATM/similarity_measurements.zip</strong></em> file.<br> __________________________________________</p> <p><strong>Search-based Minimization Algorithms:</strong><br> The source code for this step is in the <em><strong>Code/ATM/Search</strong></em> directory.<br> <br> <strong>Requirements:</strong><br> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <strong>Code/AMT/Search/requirements.txt</strong> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Search pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Output:</strong><br> * Results/ATM/minimization_results<br> <br> <strong>Running the experiment:</strong><br> To minimize the test suites in our dataset, the following bash script should be executed:</p> <pre><code class="language-bash">bash minimize.sh</code></pre> <p>All similarity measurements are parsed for each version of the projects, independently. Each version is run 10 times using three minimization budgets (25%, 50%, and 75%). Genetic Algorithm (GA) is run using four similarity measures, namely top-down, bottom-up, combined, and tree edit distance. NSGA-II is run using two combinations of similarity measures: top-down & bottom-up and combined & tree edit distance. The minimization results are generated in the <em><strong>Results/ATM/minimization_results</strong></em> directory.<br> __________________</p> <p><strong>Evaluate results:</strong><br> To evaluate and summarize the minimization results, run the following:</p> <pre><code class="language-bash">cd Code/ATM/Evaluation bash evaluate.sh</code></pre> <p>This will generate summarized <em>FDR</em> and execution time results (per-project and per-version) for each minimization budget, which can all be found in <strong>Results/ATM</strong>. In this replication package, we provide the final, merged <em>FDR</em> with execution time results.</p> <p><strong>_________________________________</strong></p> <p><strong>Running FAST-R experiments</strong><br> ATM was compared to <a href="https://github.com/ICSE19-FAST-R/FAST-R">FAST-R</a>, a state-of-the-art baseline, which is a set of test case minimization techniques called: <em>FAST++, FAST-CS, FAST-pw, and FAST-all</em>, which we adapted to our data and experimental setup.</p> <p><strong>Requirements:</strong><br> To run this step, Python 3.7 is required. Also, the libraries in the <em><strong>Code/FAST-R/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/FAST-R pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/FAST-R/test_methods<br> * Data/FAST-R/test_classes</p> <p><strong>Output:</strong><br> * Results/FAST-R/test_methods/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> * Results/FAST-R/test_classes/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run FAST-R experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash fast_r.sh test_methods #method level bash fast_r.sh test_classes #class level</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong> in the <em><strong>Results/FAST-R/test_methods</strong></em> and <em><strong>Results/FAST-R/test_classes</strong></em> directories.</p> <p><strong>_________________________________</strong></p> <p><strong>Running the random minimization experiments</strong><br> ATM was also compared to random minimization as a standard baseline.</p> <p><strong>Requirements:</strong> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <em><strong>Code/RandomMinimization/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/RandomMinimization pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> <em>N/A</em></p> <p><strong>Output:</strong><br> * Results/RandomMinimization/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run the random selection experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash random_minimization.sh</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <em><strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong></em> in the <em><strong>Results/RandomMinimization</strong></em> directory.</p>
Python Annotated Code Search (PACS) Datasets & Pretrained Models
<p>This upload contains datasets and pre-trained models used for the paper <em>Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent. </em>The code for easily loading these datasets and models will be made available here: <a href="http://github.com/nokia/codesearch">http://github.com/nokia/codesearch</a> </p> <p><strong>Datasets</strong><br> There are three types of datasets:</p> <ul> <li>snippet collections (code snippets + natural language descriptions): so-ds-feb20, staqc-py-cleaned, conala-curated</li> <li>code search evaluation data (queries linked to relevant snippets of one of the snippet collections): so-ds-feb20-{valid|test}, staqc-py-raw-{valid|test}, conala-curated-0.5-test</li> <li>training data (datasets used to train code retrieval models): so-duplicates-pacs-train, so-python-question-titles-feb20</li> </ul> <p>The staqc-py-cleaned snippet collection, and the conala-curated datasets were derived from existing corpora:</p> <ul> <li>staqc-py-cleaned was derived from the Python StaQC snippet collection. See <a href="https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset">https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset</a>, <a href="https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset/blob/master/LICENSE.txt">LICENSE</a>. </li> <li>conala-curated was derived from the conala corpus. See <a href="https://conala-corpus.github.io/">https://conala-corpus.github.io/</a> , <a href="https://creativecommons.org/licenses/by-sa/4.0/">LICENSE</a></li> </ul> <p>The other datasets were mined directly from a recent Stack Overflow dump (https://archive.org/details/stackexchange, <a href="https://creativecommons.org/licenses/by-sa/4.0/">LICENSE</a>). <br> <br> <strong>Pre-trained models</strong><br> Each model can embed queries and (annotated) code snippets in the same space. The models are released under a BSD 3-Clause License.</p> <ul> <li>ncs-embedder-so-ds-feb20</li> <li>ncs-embedder-staqc-py</li> <li>tnbow-embedder-so-ds-feb20</li> <li>use-embedder-pacs</li> <li>ensemble-embedder-pacs</li> </ul>
Investigating Transformers as Context-aware Word Search Engines - Data and Code
<p>This is the review version, please use the published record: </p> <p> https://zenodo.org/record/6425595</p> <p> </p> <p>Data and code for the paper 'Investigating Transformers as Context-aware Word Search Engines' as submitted for review at ACL21. </p> <p> </p>
Leveraging Search-Based and Pre-Trained Code Language Models for Automated Program Repair
<p>This page serves as supplementary material for the article: <strong>Leveraging Search-Based and Pre-Trained Code Language Models for Automated Program Repair</strong>. Here, we provide the ARJACLM code utilized in the study, enabling other researchers to replicate the experiments and further develop the tool. </p>
Minimizing Search Areas for Leak Detection in Water Distribution Networks - Code
<p>This database includes the code used to analyze and produce the results for the following research article:</p> <p>Minimizing Search Areas for Leak Detection in Water Distribution Networks by B. Snider, G. Lewis, A.S. Chen, L. Vamvakeridou-Lyroudia, S. Djordjevic, D.A. Savic. Journal of Hydroinformatics. (Accepted - awaiting publication).</p> <p> </p> <p> </p>
Replication package for the paper: "Code Clone Configuration as a Multi-Objective Search Problem"
<p><strong>Replication Package Context</strong></p> <p>This is the replication package for the paper "Code Clone Configuration as a Multi-Objective Search Problem". The paper was originally published in the <em>International Symposium on Empirical Software Engineering and Measurement</em> (ESEM).</p> <div> <div><strong>Search Algorithms and Datasets for MC3 Problem</strong></div> <div>This repository contains implementations of search algorithms for solving the MC3 (Multi-objective Code Clone Configuration) problem. </div> <div>Additionally, it includes various files related to Elasticsearch configurations, I/O operations, and other supporting resources. For more information about the project files and folders, you can read the README.md for general information.</div> </div>
Research Data for Beyond Keywords: Intent-Driven Semantic Code Search in Software Ecosystems
<p>This contains the software repository for the intent-enhanced code search engine as well as the baseline code search engine used in the Master Thesis: 'Beyond Keywords: Intent-Driven Semantic Code Search in Software Ecosystems'</p>
Software Open Access Maximal-Sum Submatrix search using a hybrid Contraint Programming/Linear Programming approach: experiment code and results
<p>Experiments (made with Snakemake) code & results for the paper " Software Open Access Maximal-Sum Submatrix search using a hybrid Contraint Programming/Linear Programming approach" under review at EJOR.</p>
Data quality definitions systematic search; data and coding (2008-2024 april, version edit Dimensional-definitions worksheet)
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Code, benchmarks and experiment data for the SoCS 2022 paper "Additive Pattern Databases for Decoupled Search"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper.</p> <p>sievers-et-al-socs2022-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 7.0 for reproducing all experiments of the paper, under experiments/decoupled-abstractions. The scripts 2022-04-* contain configurations for running the experiments and the script paper-tables-*.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.)</p> <p>sievers-et-al-socs2022-ipc-benchmarks.zip contains the IPC benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2018 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks).</p> <p>sievers-et-al-socs2022-autoscale-benchmarks.zip contains the Autoscale 21.11 benchmarks (from https://github.com/AI-Planning/autoscale-benchmarks).</p> <p>sievers-et-al-socs2022-lab.tar.gz contains a copy of Lab 7.0 (https://github.com/aibasel/lab).</p> <p>sievers-et-al-socs2022-raw-data.zip and sievers-et-al-socs2022-processed-data.zip contain the experimental data. Directories without the "-eval" ending (sievers-et-al-socs2022-raw-data.zip) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" (sievers-et-al-socs2022-processed-data.zip) ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</p>
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