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22 results for “Bug Reports”
Tomcat bug-report
<p>About the Data</p> <p>This dataset is one of the Datasets donated by An Ngoc Lam.</p> <p>Overview of Data</p> <p>The data is present in 2 files:</p> <p>“Tomcat.xlsx” : A spreadsheet with the bug-ids, commits, its summary, files etc.</p> <p>“Tomcat.xml” : An xml file with more detailed information than the above spreadsheet(detailed files changed).</p> <p>Attribute Information</p> <p>The spreadsheet contains a table with the “bug_id”, “summary”, “description”, “time_reported”, “commit associated”, “status of commit” and “files committed”.</p> <p>The xml contains the above information and additionally the lines associated with the commit.</p> <p>Paper Abstract</p> <p>Bug localization refers to the automated process of locating the potential buggy files for a given bug report. To help developers focus their attention to those files is crucial. Several existing automated approaches for bug localization from a bug report face a key challenge, called lexical mismatch, in which the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. This paper presents a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files and documentation if they appear frequently enough in the pairs of reports and buggy files. Our empirical evaluation on real-world projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, HyLoc, with a combination of the features built from DNN, rVSM, and project’s bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it is correct with just a single suggested file. Two out of three cases, a correct buggy file is in the list of three suggested files.</p>
Fig. 1 in First report of brown marmorated stink bug (Hemiptera: Pentatomidae) reproduction and localized establishment in Florida
Fig. 1. Location of peach orchards monitored during the 2016, 2017, and 2018 peach seasons. Years in parenthesis indicate years with detections of adult Halyomorpha halys. Asterisks (*) indicate the detection of H. halys nymphs.
Artifact for "Inside Bug Report Templates: An Empirical Study on Bug Report Templates in Open-Source Software"
<p>This is the artifact for the paper "Inside Bug Report Templates: An Empirical Study on Bug Report Templates in Open-Source Software".</p> <p><strong>What the artifact does:</strong><br>1) a questionnaire that we used for our online survey (PDF);<br>2) the valid responses of our online survey (CSV).</p> <p>3) the code of preprocessing (.py).</p> <p>4) the dataset of preprocessing and labeling (CSV).</p>
Enhancing Automated Tools: Reporting Improvements and Bugs with Bug Builder
<p><strong>Qualitative Research</strong></p> <p><strong>Part I - Manual Process</strong></p> <p>Task: Open a Issue for some automated test tool</p> <p><em>Geneva Emotion Wheel </em></p> <p>List of emotions (Anger, Interest, Amusement, Pride, Joy, Pleasure, Contentment, Love, Admiration, Relief, Compassion, Sadness, Guilt, Regret, Shame, Disappointment, Fear, Disgust, Contempt, Hate)</p> <p>What are the causes of these feelings?</p> <p><strong>Part II - Bug Builder</strong></p> <p>Task: Open a Issue for some automated test tool</p> <p><em>Geneva Emotion Wheel </em></p> <p>List of emotions (Anger, Interest, Amusement, Pride, Joy, Pleasure, Contentment, Love, Admiration, Relief, Compassion, Sadness, Guilt, Regret, Shame, Disappointment, Fear, Disgust, Contempt, Hate)</p> <p>What are the causes of these feelings?</p> <p><strong>Part III - Semi-Structured Experience Interview</strong></p> <p>1. How do you feel about opening a task to the automation team using the manual process?</p> <p>2. What situations do you face when opening a bug manually to the automation team?</p> <p>3. What were your first impressions of Bug Builder?</p> <p>4. Do you feel that Bug Builder meets your needs for reporting automation bugs? Why?</p> <p>5. Which Bug Builder features did you find most useful?</p> <p>6. Which Bug Builder features did you find least useful or unnecessary?</p> <p>7. How intuitive do you find the Bug Builder interface?</p> <p>8. Has Bug Builder helped increase the efficiency of opening bugs? How?</p> <p>9. Have you noticed any improvements in the speed of opening a bug since implementing Bug Builder?</p> <p>10. Have you encountered any technical difficulties when using Bug Builder? If so, what?</p> <p>11. What would you do differently if you had to implement Bug Builder again?</p> <p>12. Have you received sufficient training to use Bug Builder effectively?</p> <p>13. How effective was the team support provided during the Bug Builder deployment?</p> <p>14. What additional support would you like to see?</p> <p>15. Are you satisfied with the Builder? Why?</p> <p>16. Would you recommend the Bug Builder to other testing teams? Why?</p> <p>17. Are there any other tools or features that you think could complement or replace the Bug Builder?</p> <p>18. Is there anything else you would like to share about your experience with the tool?</p> <p>19. Are there any specific improvements you would like to see in the Bug Builder?</p>
Bug Reports - DBRD using LLM's
<p>This dataset contains the bug reports from JIRA, MongoDB, and Hyperledger converted into JSONL Format.</p>
FIGURE 5 in First report of the lace bug Neoplerochila paliatseasi (Rodrigues, 1981) (Hemiptera Tingidae) infesting cultivated olive trees in South Africa, and its complete mitochondrial sequence
FIGURE 5. Usage of start codons (ATG, ATA, ATT, ATC and GTG) found in the complete complement of 13 mitochondrial protein-coding genes from 15 lace bug species belonging to the family Tingidae.
FIGURE 3 in First report of the lace bug Neoplerochila paliatseasi (Rodrigues, 1981) (Hemiptera Tingidae) infesting cultivated olive trees in South Africa, and its complete mitochondrial sequence
FIGURE 3. Percentage of maximum intragroup p-distances (K2P) in 25 species of lace bugs (Hemiptera: Tingidae). The analysis was based on a 512 bp alignment of COI barcoding sequences (n = 218).
FIGURE 2 in First report of the lace bug Neoplerochila paliatseasi (Rodrigues, 1981) (Hemiptera Tingidae) infesting cultivated olive trees in South Africa, and its complete mitochondrial sequence
FIGURE 2. Neighbor-Joining (K2P) tree of lace bugs (Hemiptera: Tingidae) based on an alignment of COI sequences (512 bp). The analysis included 218 sequences belonging to 25 species in 16 genera retrieved from BOLD, except for the newly reported Neoplerochila paliatseasi (in bold). Triangles represent collapsed groups of sequences belonging to the same species. Nodal statistical support was based on 1,000 replicates (only values> 85% are shown).
FIGURE 6 in First report of the lace bug Neoplerochila paliatseasi (Rodrigues, 1981) (Hemiptera Tingidae) infesting cultivated olive trees in South Africa, and its complete mitochondrial sequence
FIGURE 6. Predicted structure of the 22 tRNAs in the complete mitochondrial genome of Neoplerochila paliatseasi (Hemiptera: Tingidae). Inferred canonical Watson-Crick bonds are represented by lines, and non-canonical bonds (U-U, A-A, G-U, G-A, G-G, C-C, A-C, and C-U) are represented by dots.
FIGURE 7. Phylogenetic relationships among 15 in First report of the lace bug Neoplerochila paliatseasi (Rodrigues, 1981) (Hemiptera Tingidae) infesting cultivated olive trees in South Africa, and its complete mitochondrial sequence
FIGURE 7. Phylogenetic relationships among 15 lace bug species (Hemiptera: Tingidae) based on 13 mitochondrial proteincoding genes, using Bayesian inference. PCG123 was constructed using DNA sequences with partitioned codon positions. PCG12 was constructed using DNA sequences, excluding the 3rd codon position. AA was constructed using amino acid sequences. Trees were rooted by the outgroups Adelphocoris fasciaticollis and Apolygus lucorum (Miridae). Nodal statistical support is given as Bayesian posterior probability.
An Approach for Traceability Recovery between Bug Reports and Test Cases
<p>Scripts and data sets used in research for <em>An Approach for Traceability Recovery between Bug Reports and Test Cases</em>.</p> <p><em><strong>(Context)</strong></em> Automatic traceability recovery between software artifacts may promote early detection of issues.<br> Information Retrieval (IR) techniques have been proposed for the task, but they differ considerably in terms of input parameters and results. It is difficult to assess results when those techniques are applied in isolation, usually in small or medium-sized software projects. Also, an overview would be more comprehensive if a Deep Learning (DL) based technique is applied, in comparison with traditional IR techniques.<br> <em><strong>(Objective)</strong></em> We propose an approach to recover traceability links between bug reports and test cases, which can be instantiated with a set of IR and DL techniques.<br> <em><strong>(Method)</strong></em> For applying and evaluating our solution, we used historical data from the Mozilla Firefox quality assurance (QA) team, on which we assessed the following IR techniques: LSI, LDA, and BM25. We also experimented with a DL architecture called Convolutional Neural Networks (CNNs) through the use of Word Embeddings.<br> <em><strong>(Results)</strong></em> In this context of traceability, we noticed poor performances from three out of the four studied techniques. Only the LSI technique was effective, even standing out over the state-of-the-art BM25 technique.<br> <em><strong>(Conclusions)</strong></em> The obtained results suggest that the semi-automatic application of the LSI technique -- with an appropriate combination of thresholds -- is feasible for real-world software projects.</p>
Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
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On Reporting Performance and Accuracy Bugs for Deep Learning Frameworks: An Exploratory Study from GitHub
<p>This repository aims to store the dataset of performance and accuracy bug reports, which belongs to <em>"On Reporting Performance and Accuracy Bugs for Deep Learning Frameworks: An Exploratory Study from GitHub"</em></p>
Information needs in bug reports for web applications (supplementary material)
<p>This repository contains supplementary material for the manuscript "Information needs in bug reports for web applications". The zip archive data.zip (34,835KB) includes the following files:</p> <ul> <li>project_metadata.csv: List of 10 analyzed projects and metadata (9KB).</li> <li>bug_reports.csv: List of bug reports studied in the paper, including meta-data (204,390KB).</li> <li>bug_reports_additional_data: Additional data/information in bug reports captured in comments of bug reports, including meta-data (5,269KB).</li> </ul>
Bug Report Analytics for Software Reliability Assessment using Hybrid Swarm-Evolutionary Algorithm
<p><span>There are in total 6 files.</span></p> <p><span><span>1.<span> </span></span></span><span>Out of these files three documents are related to datasets. Two are related to unrefined Eclipse and JDT files and third is refined data of Eclipse and JDT Project Failure Datasets which has been used for experimentation purpose.</span></p> <p><span><span>2.<span> </span></span></span><span>This package also includes code for all the models version wise for all versions of Eclipse and JDT projects.</span></p> <p><span><span>3.<span> </span></span></span><span>Sample Code has also been given for version 4.3 and 4.10. </span></p> <p><span>Steps to run </span></p> <p><span><span>a)<span> </span></span></span><span>In this code, Main ABCDE file needs to be run and different datasets could be executed on this file. This is for one type of datasets that is time domain dataset only. </span></p> <p><span><span>b)<span> </span></span></span><span>If anyone is interested in getting separate results for cumulative sum and failure intensity, separate file has been given. </span></p> <p><span><span>c)<span> </span></span></span><span>Code for ABCDE algorithm that is Swarm Evolutionary algorithm used in the paper has also been given in these files.</span></p>
Duplicate Bug Report Detection using an Attention-basedPre-trained Neural Language Model
<p>A BERT based Approach for Automatic Duplicate Bug Report Detection</p>
Improving Traceability Recovery Between Bug Reports and Manual Test Cases
<p>Vídeo backup da apresentação SBES 2020.</p> <p>Artigo: Improving Traceability Recovery Between Bug Report and Manual Test Cases.</p>
GPTs are not the Silver Bullet: Performance and Challenges of using GPTs for Security Bug Report Identification – Supplementary Material
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FIGURE 1 in First report of the lace bug Neoplerochila paliatseasi (Rodrigues, 1981) (Hemiptera Tingidae) infesting cultivated olive trees in South Africa, and its complete mitochondrial sequence
FIGURE 1. Representative adult specimen of the olive lace bug Neoplerochila paliatseasi (Hemiptera: Tingidae), reported here for the first time as a pest of cultivated olives in the Western Cape Province of South Africa. South Africa Iziko Museum coden - SAM-HEM-A011644. A. Dorsal; B. Lateral; C. Ventral. Scale bars: 1 mm.
FIGURE 4 in First report of the lace bug Neoplerochila paliatseasi (Rodrigues, 1981) (Hemiptera Tingidae) infesting cultivated olive trees in South Africa, and its complete mitochondrial sequence
FIGURE 4. Linear map of the complete mitochondrial genome of the olive lace bug Neoplerochila paliatseasi (Hemiptera: Tingidae). The arrows represent the direction of the genes (right – majority strand; left – minority strand).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.