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3,739 results for “RePAIR”
Slimfield: Escherichia coli DNA repair proteins (RecA-mGFP and RecB-sfGFP)
<p>Imaging modality / instrument: <em>Brightfield</em> + <em>Slimfield</em></p> <p>Image format:<em> OME TIFF (16 bit) + MicroManager metadata files</em></p> <p>Microscope settings:</p> <p><em>488 nm triggered excitation; split detection, cropped to GFP or RFP/GFP (left/right) channels; 3 ms/frame laser exposure; Photometrics Prime95b CMOS</em></p> <p>Samples and acquisitions:</p> <p>Fluorescent fusions in live E.coli cells. MMC = mitomycin C</p> <table> <tbody> <tr> <td> <p>No. fields of view</p> </td> <td> <p>MMC-</p> </td> <td> <p>MMC+ (0.5 ug/ml 3h)</p> </td> </tr> <tr> <td> <p>RecA-mGFP</p> </td> <td> <p>7</p> </td> <td> <p>15</p> </td> </tr> <tr> <td> <p>RecB-sfGFP</p> </td> <td> <p>17</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p>MG1655 control</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> </tr> </tbody> </table> <p>Approx. size before/after compression: 21 GB / 7 GB</p>
Supplementary Data for the manuscript 'The Rad9-Rad1-Hus1 DNA repair clamp is found in Microsporidia'
<p>This dataset contains the supplementary data files for the manuscript titled 'The Rad9-Rad1-Hus1 DNA repair clamp is found in Microsporidia'.</p>
Phase I trial of CX-5461, a first-in-class G-quadruplex stabilizer in patients with advanced solid tumors enriched for DNA-repair deficiencies (CCTG IND.231) - Variant Calls
<p>Variant Calls from Phase I trial of CX-5461, a first-in-class G-quadruplex stabilizer in patients with advanced solid tumors enriched for DNA-repair deficiencies (CCTG IND.231)</p> <p>See publication for methodology.</p>
Vul4J+: A Dataset of Vulnerabilities for Automated Vulnerability Repair
<div> <div><strong>Vul4J+</strong> is a dataset of vulnerability fixes for automated vulnerability repair (AVR) in Java. Each entry of the dataset represents a <strong>vulnerability</strong> affecting an open-source Java project, having reference to the commit (revision) containing the code affected by the vulnerability and its version fixed by a human developer (the "left" and "right" parts of the commit). Each vulnerability is equipped with at least one <strong>"oracle"</strong> that shows the presence of the vulnerability, and that can be used to validate the correctness of patches generated by AVR tools. This *"oracle"* might have the form of a:</div> <div>- <strong>Vulnerability-witnessing test</strong>, i.e., a JUnit test case that fails on the vulnerable version of the code but passes on the patched version.</div> <div>- <strong>Warning/report</strong> raised by a vulnerability static analyzer, i.e., SpotBugs, that is presented in the vulnerable version of the code but not in the patched version.</div> <br> <div>In essence, Vul4J+ is a cleaned up and extended version of Vul4J containing:</div> <div>- 106 known vulnerabilities with executable vulnerability-witnessing test cases in Docker containers and warnings (reports) from SpotBugs static analyzer (if found);</div> <div>- 79 come from the original Vul4J;</div> <div>- 27 result from the replication of the same protocol used in the original Vul4J;</div> <div>- 50 vulnerabilities stored in Docker containers with the warnings (reports) from SpotBugs static analyzer ;</div> <div>- 35 known vulnerabilities matched with vulnerability-witnessing test cases retrieved from projects in the wild.</div> <br> <div>In total, Vul4J+ points to <strong>191 vulnerabilities</strong>, each with at least one vulnerability oracle.</div> </div>
Replication package for "Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation"
<p>This repository contains the replication package for the paper "Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation" by Fernando Vallecillos Ruiz, Anastasiia Grishina, Max Hort and Leon Moonen, accepted for publication in ACM Transactions on Software Engineering and Methodology on 2025-10-09.</p> <p>A preprint is deposited on arXiv with DOI: <a href="https://doi.org/10.48550/arXiv.2401.07994">10.48550/arXiv.2401.07994</a>.</p> <p>The replication package is archived on Zenodo with DOI: <a href="https://doi.org/10.5281/zenodo.10500593">10.5281/zenodo.10500593</a>. It is maintained on GitHub at <a href="https://github.com/secureIT-project/RTT_for_APR">https://github.com/secureIT-project/RTT_for_APR</a>.</p> <p>This project builds on code from the <a href="https://github.com/lin-tan/clm/">clm</a> project, which is (c) 2023, The ASSET research group led by Lin Tan, Purdue University, licensed under the BSD 3-Clause License (see jasper/LICENSE.BSD). All modifications and new contributions are (c) 2025 by the authors of this replication package and distributed under the MIT License (see LICENSE.MIT). The data, models and preprint are distributed under the CC BY 4.0 license.</p> <h2>Citation<code> </code></h2> <p>If you build on this data or code, please cite this work by referring to the paper:</p> <div> <pre><code>@article{ruiz2025:rtt, title = {Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation}, author = {Vallecillos Ruiz, Fernando and Anastasiia Grishina and Max Hort and Leon Moonen}, journal = {ACM Transactions on Software Engineering and Methodology (TOSEM)}, year = {2025}, publisher = {{ACM}} }</code></pre> </div> <h2>Organization</h2> <p>The replication package is organized as follows:</p> <ul> <li>clm-apr <ul> <li>plbart: code to generate patches with PLBART models.</li> <li>codet5: code to generate patches with CodeT5 models.</li> <li>transcoder: code to generate patches with the TransCoder model.</li> <li>incoder: code to generate patches with InCoder models.</li> <li>santacoder: code to generate patches with the SantaCoder model.</li> <li>starcoder: code to generate patches with the StarCoderBase model.</li> <li>quixbugs: code to validate patches generated for the QuixBugs benchmark.</li> <li>defects4j: code to validate patches generated for any of the Defects4J benchmarks.</li> <li>humaneval: code to validate patches generated for the HumanEval-Java benchmark.</li> </ul> </li> <li>humaneval-java: the HumanEval-Java benchmark proposed by Jiang et al. 2023</li> <li>jasper: a Java tool to parse Java programs needed to preprocess input.</li> <li>model: folder to download the language models.</li> <li>analysis_wandb: data from WandB and Jupyter notebook to create graphs.</li> <li>tmp_benchmarks: folder for temporary files used in patch validation. The folder may contain pairs of `paralell’ folders src and src_org for each benchmark, used to replace buggy code with candidate patches.</li> </ul> <h2>Replication</h2> <h3>Prerequisites</h3> <ul> <li>Python version: 3.8—3.10.</li> <li><a href="https://git-lfs.com/">Git LFS</a> is required for model downloading.</li> </ul> <h4>Weight and Biases (WandB)</h4> <ol> <li>Create an account on <a href="https://wandb.ai/">Weights and Biases</a></li> <li>Install the <a href="https://docs.wandb.ai/ref/python">Weights and Biases</a> library</li> <li>Run <code>wandb login</code> and follow the instructions</li> </ol> <h4>Set up OpenAI access</h4> <p>OpenAI account is needed with access to <code>gpt-3.5-turbo</code> and <code>gpt-4</code> . The <code>OPENAI_API_KEY</code> environment variable should be set to your OpenAI API access token.</p> <h3>Dependencies</h3> <ul> <li><a href="https://github.com/rjust/defects4j">Defects4J</a> - To generate inputs for the Defects4J datasets or to validate them, you need to have installed <a href="https://github.com/rjust/defects4j">their tool</a>.</li> <li>Java 8</li> <li>Apache Maven</li> </ul> <h3>Setup</h3> <p>We recommend the use of the setup script:</p> <pre><code>setup.sh </code></pre> <p>which performs the following:</p> <ol> <li>Creates a virtual environment for Python and activate it.</li> <li>Install the packages in <code>requirements.txt</code>.</li> <li>Compiles Jasper.</li> <li>Downloads parsers.</li> <li>Check if the Defects4J installation is correct.</li> </ol> <h3>Download models</h3> <p>The following bash script contains the code to download all of the models used:</p> <pre><code>models/download_models.sh </code></pre> <p>We recommend downloading only the models you are going to use due to their size</p> <pre><code>cd models chmod +x download_models.sh ./download_models.sh </code></pre> <p>To run one specific model, for example, PLBART (C#), use the following commands:</p> <pre><code>cd models git lfs install git clone https://huggingface.co/uclanlp/plbart-java-cs git clone https://huggingface.co/uclanlp/plbart-cs-java cd ../.. </code></pre> <h3>Step 1: Preprocessing and Prompting:</h3> <p>Each script in each <code>clm-apr/[model]</code> folder connects one or more models with<br>one dataset. These scripts follow the template: [benchmark]_[model]_[technique].py.<br>The scripts first create an <code>[model]_input.json</code> file with the preprocessed<br>input. Then generate outputs based on that file with one or more models.<br>For example:</p> <pre><code>cd clm-apr/plbart python quixbugs_plbart_round.py # Generates input for QuixBugs and generate patches using Java<->C# RTT. python quixbugs_plbart_round_nl.py # Generates input for QuixBugs and generate patches using Java<->NL RTT. </code></pre> <p>Optionally, use argument <code>--device_map cpu</code> if you wish to run the script on<br>CPU, for example:</p> <pre><code>python quixbugs_plbart_round.py --device_map cpu </code></pre> <p>Otherwise, the script will be run on all available CUDA GPU’s.</p> <p>We have commented the generation of inputs in the scripts. Users are free to<br>uncomment this method and try for themselves. It is easily recognizable by<br>their name template <code>[model]_[benchmark]_input()</code>. In the previous case:</p> <pre><code>quixbugs_plbart_input() </code></pre> <h3>Step 2 and 3: Round Trip Translation and Postprocessing</h3> <p>These steps are also included in the [benchmark]_[model]_[technique].py<br>script mentioned above. They are modularized in the method recognizable by<br>their name template [model]_[benchmark]_output().<br>For example:</p> <pre><code>quixbugs_incoder_output() </code></pre> <p>This method:</p> <ol> <li>Reads the input json file.</li> <li>Generates outputs through the LLM.</li> <li>Postprocess the output (extract the patch, clean up extra token, etc.).</li> <li>Creates [model]_output_[technique]_[extra].json.</li> </ol> <p>The last 3 steps are repeated according to the number of runs set to performed<br>(10 in our experiments). Each run will produce a different file with the seed<br>used in its generation. For example, <code>quixbugs\_plbart\_round.py</code> and<br><code>quixbugs\_plbart\_round_nl.py</code> scripts create:</p> <pre><code>clm-apr/quixbugs/plbart_results/run_0/plbart_java_cs_java_output_round_csharp_batch.json clm-apr/quixbugs/plbart_results/run_0/plbart_java_nl_java_output_round_nl_batch.json </code></pre> <h3>Step 4: Evaluation of RTT Results:</h3> <p>The last step evaluates the generated outputs against the test-suites of each<br>benchmark. This script reads the previous outputs files and generates a new one<br>with the results of the test for one model. Furthermore, it connects with the<br><em>WandB</em> tool to calculate metrics and send them to analyze.</p> <p>Following the previous examples, to validate the results previously obtained,<br>we execute the following:</p> <pre><code>cd clm-apr/quixbugs python validate_quixbugs_parallel.py </code></pre> <p>Given the included JSON, this script would create:</p> <pre><code>clm-apr/quixbugs/plbart_results/run_0/plbart_java_cs_java_validate_round_csharp_batch.json </code></pre> <p>We have disabled <em>WandB</em> in the script to allow users to try the script first.<br>However, it can be easily activated by changing the parameter <code>mode="disabled"</code><br>to <code>mode="online"</code>.<br>We have set the variable <code>total_runs = 1</code>, as well as <code>input_file</code> and <code>output_file</code><br>to the results included. They should be modified accordingly to validate more runs<br>or to validate other files/models.</p> <h3>Included Results</h3> <p>We include two CSV files obtained through WandB.</p> <pre><code>'data_cleaned_grouped.csv': Aggregated metrics of the 25 outputs for all runs. 'full_data_all_runs.csv': All metrics for all outputs on all runs. </code></pre> <h2>Changelog</h2> <ul> <li>v1.0 - updates corresponding to the accepted version of the manuscript in TOSEM</li> <li>v0.1 - initial replication package corresponding to v1 of arXiv deposit: includes raw data, code, and example outputs.</li> </ul> <h2>References</h2> <p>Jiang, N.; Liu, K.; Lutellier, T.; and Tan, L. 2023. Impact of Code Language<br>Models on Automated Program Repair. In 45th International Conference on<br>Software Engineering (ICSE), 1430–1442. IEEE. ISBN 978-1-66545-701-9.</p> <div> </div>
Importance rating of various operational aspects related to repair service processes
<h3>Contextual information</h3><p>The survey considers the scenario that repairs are conducted at a repair service centre but not as a repair service at home as, e.g., for washing machines. This allowed to split the sample into four groups, each of it referring to a different portable product to be repaired (bicycle, smartphone, vacuum cleaner and a generic product) in order to examine the generalisability of the results.</p><p>The survey is based on items related to repair operations and provides data concerning the importance of the different items. It considers the scenario that repairs are conducted at a repair service centre but not as a repair service at home as, e.g., for washing machines. The operational aspects concern three different stages of the repair process that can be influenced by the repair company: the prepurchase, service encounter, and post-encounter stage. Furthermore, socio-demographic data is included.</p><h3>Description of the data and file structure</h3><p>The dataset contains 107 aspects identified for potential improvement of repair operations. The importance of operational aspects was rated by survey participants on a 7-point Likert scale (extremely important to not at all important). Three additional questions for quality control (e.g., 'Please tick 1 = extremely important') tested the attention of the participants. </p><p>File 'Translation_of_items.ods' contains the items in German (original version) and English (translated). File 'Description_of_Data.ods' contains a description of the variables.</p><ul><li>We removed all answers with completion time of less than five minutes, as the pre-test showed that this was the minimum time for a focused completion of the survey.</li><li>All data rows with wrongly answered control questions were removed.</li></ul>
Fig. 1 in Evidence of Stress Recovery in Free-Living Ciliate Colpoda cucullus: The Repair Capability of Resting Cysts to Damage Caused by Gamma Irradiation
Fig. 1. Excystment assay of Colpoda wet cysts (A) and dry cysts (B). 'Non-irradiated' indicates non-irradiated cysts; 'irradiated' indicates cysts irradiated at 4000 Gy, and 'irradiated-incubated' indicates cysts irradiated at 4000 Gy and incubated for 12 hours before the induction of excystment. Time indicates the number of hours after the induction of excystment. Columns and attached bars correspond to the means and standard errors, respectively, of six measurements. Asterisks and double asterisks represent significant differences at p <0.05 and p <0.01 (Mann-Whitney U test), respectively.
Data used to evaluate ORBITS: Optimal Repair-Based Inconsistency-Tolerant Semantics
<p>This dataset provides the input files that were used in the evaluation of the ORBITS system (Optimal Repair-Based Inconsistency-Tolerant Semantics, <a href="https://github.com/bourgaux/orbits">https://github.com/bourgaux/orbits</a>). A detailed description is available in a technical report on arXiv (<a href="https://arxiv.org/abs/2202.07980">https://arxiv.org/abs/2202.07980</a>).</p> <p><strong>Content:</strong></p> <p>Folders <em>cqapri_benchmark</em>, <em>food_inspection_benchmark</em>, and <em>physicians_benchmark</em> contain JSON files of conflict graphs and candidate queries and their causes.<br> These files are named using the following pattern: files of candidate answers and their causes are named <database>_<query>_answers_causes.json, and conflict graphs are named <database>_conflictGraph_<priority relation>.json where <priority relation> says whether the priority relation is score-structured (prio_score) or not (prio_non_score) and the probability (p<proba>) or number of scores (n<number>) used to build the priority relation.</p> <p>Folder <em>original_datasets_and_queries</em> contains the Food Inspection and Physicians datasets used to generate files from <em>food_inspection_benchmark</em> and <em>physicians_benchmark</em>.<br> Files from <em>cqapri_benchmark</em> have been generated from the CQAPri benchmark available at <a href="https://lahdak.lri.fr/CQAPri/CQAPri.php">https://lahdak.lri.fr/CQAPri/CQAPri.php</a>.<br> In all cases, we use ProvSQL (<a href="https://github.com/PierreSenellart/provsql">https://github.com/PierreSenellart/provsql</a>) to build conflict graphs and causes from the datasets.</p>
High resolution 3D reconstruction of regenerating nerve within a chitosan conduit 7 days after injury and repair
<p><strong>Video S1:</strong> high resolution 3D reconstruction of 7 consecutive 50 µm thick sections labelled with Reca1 (red, endothelial cell marker) and S100β (green, Schwann cell marker).</p>
Figure 2-Immunofluorescence staining of regenerating nerves 7, 14, 21, and 28 days after the injury and repair.
<p>Figure 2- Immunofluorescence staining of regenerating nerves 7, 14, 21, and 28 days after the injury and repair. One section every millimeter labeled with Reca1 (red, endothelial cell marker), S100β (green, Schwann cell marker), and Neurofilament/NF (white, axon marker) to follow the nerve regeneration progression. The single labeling is shown in Figures S1 (NF), S2 (S100β), and S3 (Reca1) in the published manuscript. The dotted line delimits the region containing cell nuclei identified with DAPI (as in Figure 1B). Scale bar: 400 µm. It is possible to zoom in on this high-resolution version of this figure to appreciate the interactions between the different structures.</p> <p> </p>
An End-to-End Framework for Detecting and Repairing Potential Vulnerabilities
<p>Nowadays, program development is getting easier and easier as the various IDE tools provide advice on what to write in the program. But it is not enough to implement a solution to a problem; it is also important that the non-functional properties, like the quality or security of the code, are appropriate in all aspects. One of the most widely used techniques to ensure quality is testing. If the tests fail, one can fix the code immediately. However, security issues are unexpected cases when implementing the program, which is why we do not write tests for them in advance. In many cases, security-relevant bugs can not only cause financial loss but also put human lives at risk, so detecting and fixing them is an important step for the reliability and quality of the program. The tool presented in this paper aims to generate automatic code repairs to potential vulnerabilities in the program. By integrating the recommended fixes, one can easily harden the security of their program early in the development process. A case study on six open-source Java subject systems showed that we were able to generate viable repair patches for 57 out of the 81 detected security issues (70%). For certain types (e.g., revealing private references of mutable objects), our tool reached close to perfect performance.</p>
Code and Dataset for "Examining Zero-Shot Vulnerability Repair with Large Language Models"
<p><strong>Code and Dataset for "Examining Zero-Shot Vulnerability Repair with Large Language Models"</strong></p> <p>The following Zenodo contains the resources associated with the S&P accepted paper ‘Examining Zero Shot Vulnerability Repair with Large Language Models’, https://arxiv.org/abs/2112.02125</p> <p>In this resource, you can find the following.</p> <p> - 'important_results' directory:<br> This directory is for containing the final raw results as generated by the framework, including a global CSV of all generations and an HTML file containing all of the diffs generated for the 'high-confidence' real-world scenarios.<br> - final_results.csv<br> - This contains the final results of all generated software patches.<br> - Note the nomenclature differences with the manuscript tables. These are explained in the README in the framework.<br> - Original vs LLM-Generated Vulnerability Fixes.html<br> - This contains all of the diffs for the real-world patches versus the canonical developer-provided patches.</p> <p> - 'framework' directory:<br> This directory contains the complete archive of the code framework and all results at the time of the paper’s submission. It contains every language model prompt, suggestions, assembled repair patch and analysis data. It contains every script used for generation and analysis. It is a large archive, and within it contains an included README describing how to understand and use it.<br> - For convenience, we include a copy of the README external to the zipped archive.</p> <p> - 'resources' directory: <br> This directory contains the resources used by our large associated tools, including:<br> - 'gpt2-csrc' subdirectory:<br> Everything to do with the gpt2-csrc model, including the trained files, training scripts, and training data.</p> <p> - 'ExtractFix' subdirectory:<br> A docker image containing all ExtractFix scenarios, even those we did not use. Provided for interest (not required for usage).</p>
Figure 33. Repaired Argonauta argo shell from Monterey, California. Repaired A in Recognising variability in the shells of argonauts (Cephalopoda: Argonautidae): the key to resolving the taxonomy of the family
Figure 33. Repaired Argonauta argo shell from Monterey, California. Repaired A. argo shell from Monterey, California (81.9 mm shell length, USNM 61374): a, left lateral view; b, oblique left lateral view; c, oblique anterior aperture view. Note change in direction of lateral ribs along repair line. Scale bar = 1 cm.
Figure 12. Repaired shell displaying components consistent with Argonauta nouryi and A in Recognising variability in the shells of argonauts (Cephalopoda: Argonautidae): the key to resolving the taxonomy of the family
Figure 12. Repaired shell displaying components consistent with Argonauta nouryi and A. cornutus: a–d, four perspectives of a single shell (52.3 mm shell length, SBMNH 357476) displaying an initial component consistent with A. nouryi Lorois, 1854 ("nouryi") followed by a subsequent component consistent with A. cornutus Conrad, 1854 ("cornutus"); a, right lateral view; b, oblique right lateral view; c, anterior aperture view; d, oblique ventral keel view. Dashed line represents repair line separating two visually different components. Scale bar = 1 cm.
Tool Artifact for "Mutation-based Lifted Repair of Software Product Lines"
<p>In this work, we describe the installation, usage, and evaluation results of the tool SPLAllRepair introduced by the paper<br>``Mutation-based Lifted Repair of Software Product Lines''. We provide step-by-step instructions on how to download, run, and compare the tool's outputs to outputs described in the paper. The tool implements a novel lifted repair algorithm for program families (Software Product Lines - SPLs) based on code mutations. The inputs of our algorithm are an erroneous SPL and a specification given in the form of assertions. We use variability encoding to transform the given SPL into a single program, called family simulator, which is translated into a set of SMT formulas whose conjunction is satisfiable iff the simulator (i.e. the input SPL) violates an assertion. We use a predefined set of mutations applied to feature and program expressions of the given SPL.<br>The algorithm repeatedly mutates the erroneous family simulator and checks if it becomes (bounded) correct. The outputs are all minimal repairs in the form of minimal number of (feature and program) expression replacements such that the repaired SPL is (bounded) correct with respect to a given set of assertions. We present the experimental results showing that our approach is able to successfully repair various interesting #ifdef-based C SPLs.</p>
Photomorphogenesis for Robot Self-assembly: Adaptivity, Collective Decision-making, and Self-repair
<p>Self-assembly in biological systems is an inspiration for engineered large-scale multi-modular systems with desirable characteristics, such as robustness, scalability, and adaptivity. Previous works have shown that simple mobile robots can be used to emulate and study self-assembly behaviors. However, many of these studies were restricted to rather static and inflexible aggregations in predefined shapes, and were limited in adaptivity compared to that observed in nature. We propose a photomorphogenesis approach for robots using our vascular morphogenesis model---a light-stimuli directed method for multi-robot self-assembly inspired by the tissue growth of trees. Robots in the role of `leaves' collect a virtual resource that is proportional to a real, sensed environmental feature. This resource is then shared throughout the whole robot aggregate and determines where it grows or shrinks as a reaction to the dynamic environment. In our approach the robots use supplemental bioinspired models to collectively select a seed robot to decide who starts to self-assemble (and where), or to assemble static aggregations. The robots then use our vascular morphogenesis model to aggregate in a directed way preferring bright areas, hence resembling natural phototropism (growth towards light). In this assembly, they are adaptive and able to react to a dynamic environment by collectively and autonomously rearranging the aggregate, discarding outdated parts and growing new ones. In representative experiments, the self-assembling robots collectively make rational decisions on where to grow. Cutting off parts of the aggregate triggers a self-organizing repair process in the robots, and the parts regrow. All these capabilities of adaptivity, collective decision-making, and self-repair in our robot self-assembly originate directly from self-organized behavior of the vascular morphogenesis model. Our approach opens up opportunities for self-assembly with reconfiguration on short time-scales with high adaptivity of dynamic forms and structures.</p>
Method to Improve Workflow Net Decomposition for Process Model Repair - experiments
<p>This repository contains data that was used to carry out experiments as well as the results of these experiments.</p> <p>File names are presented in the following format: LM2-[repair method]-[b/f]-[number of experiment], where:<br> - repair method may take values "Greedy" or "Smart". "Greedy" means that the repair method of the model was greedy algorithm working with maximal decomposition. "Smart" means that the repair method was greedy algorithm as well, with the difference of decomposition method being the developed one.<br> - "b" (broken) means that the model has not undergone repair. "f" (fixed) means that the file presents a repaired model (the one which fits the initial log perfectly).<br> - number of experiment ranging from 1 to 10.</p> <p>This repository also contains the following files:<br> - LM2-CL.xes - the initial LM2 model log;<br> - LM2-CM.pnml - the initial LM2 model;<br> - LM2-Greedy-data.txt - auto-generated measurements of greedy approach performance;<br> - LM2-Smart-data.txt - auto-generated measurements of smart (developed) approach perfomance.</p>
APRSuite: A Suite of Components and Use Cases Based on Categorical Decomposition of Automatic Program Repair Techniques and Tools
<p><strong>During the last decade, we are witnessing the advent of a proliferation of techniques and associated tools for automatic program repair (APR). The current techniques and tools provide rich sources of knowledge that should be taken into consideration for future research. An overview of the current APR techniques and tools can serve the research community as a knowledge accumulator. However, APR techniques and tools differ in many aspects making knowledge accumulation challenging. To overcome this challenge, in this paper, we propose to leverage common components that constitute the APR techniques and tools. To achieve this objective, we surveyed current APR techniques and tools to identify the APR Suite of common constituent components, namely as APRSuite. Repair source and defect class are examples of identified components. We grouped these components into several categories such as patch evaluation and target defects. We have also identified some of the possible use cases per component as well as different lessons learned in studies for each component and for each use case. In addition, we developed a principled way for application of the components. The <em>APRSuite</em> and the <em>principled way</em> to apply it comprise a <em>framework</em> for knowledge accumulation, evaluation, and comparison of APR techniques and tools. The novelty of our work lies in its original viewpoint to the process of literature review in the APR research field. To demonstrate the applicability of the framework, we mapped out several concrete APR techniques, as a first instantiation of the framework. We observed that the framework brings discipline into the evaluation and/or comparison of APR techniques and tools. The framework offers these benefits objectively and systematically. We concluded that knowledge accumulation and characterization through literature reviews can be therefore facilitated through the identified suite of components while at the same time the existing component suite can be modified, augmented, or improved.</strong></p>
Fig. 2 in Repaired injuries and shell form in some Palaeozoic pleurotomarioid gastropods
Fig. 2. Schematic drawing of Fig. 1 as a guide to emphasize the location of repaired injuries, here shown in thicker lines. For explanation see Fig. 1 captions and the text. A–F. Turbiniform shells. G–J. Trochiform shells. K, L. Planispiral shells.
Data from: Repairing a deleterious domestication variant in a floral regulator of tomato by base editing
<p>This repository contains the data necessary to run the analysis described in the publication "Repairing a deleterious domestication variant in a floral regulator of tomato by base editing" by Glaus et al., 2024. A preprint is available on bioRxiv (doi: <a href="https://doi.org/10.1101/2024.01.29.577624" rel="nofollow">https://doi.org/10.1101/2024.01.29.577624</a>)</p> <p> </p> <p>82_acc_Spim0.1_filtered.vcf.gz -- variant call results for 82 genomes with LA1589 as reference</p> <p>82_acc_Spim0.1_filtered_SIFT_out.tar.gz -- sift4g prediction results for 82 genomes with LA1589 as reference (SIFTannotations.xls and SIFTpredictions.list)</p> <p>sift_lib_LA1589.tar.gz -- sift4g library for the LA1589 genome</p> <p>SolpimLA1589_liftoff.tar.gz -- liftoff annotation of LA1589 genome</p> <p> </p> <p>In case of any questions, please contact Sebastian Soyk (sebastian.soyk@unil.ch)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.