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47 results for “Static Analysis”
Dataset for 'Assessing Golang Static Analysis Tools on Real-World Issues'
<p>Go Linter Evaluation Dataset</p> <p>This is a publicly available dataset for 'An empirical evaluation of Golang static code analysis tools for real-world issues.' Please refer to the data according to the names of the spreadsheets.</p> <p>Authors: Jianwei Wu, James Clause</p> <p>Collected Survey Data:<br>- This Excel file contains the collected survey data for the empirical study in details.</p> <p>R Scripts and Raw Data:<br>- These scripts are used for data analysis and processing.<br>- This is the initial data collected from surveys or other sources before any processing or analysis.</p> <p>Surveys for External Participants:<br>- This Excel file contains survey data collected for the evaluation of Go linters.<br>- This folder contains the surveys sent to external participants for collecting their feedback or data.</p> <p>Recruitment Letter.pdf:<br>- This PDF contains an example of the recruitment letter sent to potential survey participants, inviting them to take part in the study.</p> <p>Outputs from Existing Go Linters and Summarized Categories.xlsx:<br>- This Excel file contains outputs from various Go linters and categorized summaries of these outputs. It helps in comparing the performance and features of different linters.</p> <p>Selection of Go Linters.xlsx:<br>- This Excel file lists the Go linters selected for evaluation, along with criteria or reasons for their selection.</p> <p>UD IRB Exempt Letter.pdf:<br>- This PDF contains the Institutional Review Board (IRB) exemption letter from the University of Delaware (UD), indicating that the study involving human participants was exempt from full review.</p> <p>Survey Template.pdf:<br>- This PDF contains an example of the survey sent to the participants.</p> <p>govet issues.pdf:<br>- This PDF contains a list of reported issues about govet. Collected from various pull requests.</p> <p>Approved linters:<br>- staticcheck gofmt govet revive gosec deadcode errcheck.</p> <p>Table 2.jpg:<br>- A detailed figure to show the technical data in Table 2 of the paper.</p> <p>PR-summary-standalone:<br>- This is the AI tool we used to summarize the content of pull requests. Readme file is avaliable in this folder.</p>
Artifact for An Extensive Empirical Study of Nondeterministic Behavior in Static Analysis Tools
<p>This repository contains data for 'An Extensive Empirical Study of Nondeterministic Behavior in Static Analysis Tools' and the source code of the tool NDDetector that is used for performing the experiments in RQ2.</p><p>There are two directories, data and tool:</p><p><data> contains the data for the conclusion made in the two research questions, RQ1 and RQ2. (rq1 is Research Question 1s data)</p><p>In rq1/ there are:</p><p>final_results.csv - Contains 43 distinct results from 4 repositories (SOOT, WALA, FlowDroid, DroidSafe) that fix or report nondeterminism.</p><p>summary.pdf - Reports the number of nondeterminism results by tool repository at each stage of the qualitative study.</p><p>categorization.pdf - Reports the number of nondeterminism results by root cause categories at each component of analysis codebase in which the nondeterminism takes place</p><p>raw_data.zip - Contains the raw commits and issues extracted from 9 repositories (SOOT, DOOP, WALA, FlowDroid, DroidSafe, AmanDroid, TAJS, Code2Flow, PyCG)</p><p>key_words_results.zip - Contains the results extracted by each keyword (concurrency, concurrent, consistent, determinism, deterministic, different, flakiness, flaky, parallel, thread) from the raw data.</p><p>In rq2/ there are:</p><p>ICSE2024_AGGREGATE_DATA.csv - Contains the result distributions of each combination of target program, configuration hash, and tool aswell as the calculated consistency score.</p><p>analyze_results.py - Script that makes this data.</p><p>node_freqs - Contains the frequency of each node in the nondeterministic results we observed,it also keeps track of whether this particular node is a callee or caller or source/sink.</p><p>edge_dists - Contains the actual edge distributions of all of our results that behaved nondeterministically. it contains, for each result (edge/flow) across repetitions, which repetitions did or did not contain this edge/flow and which did. This means if you are interested in the actual differences across results generated by tool edge_dists/ is the place to look.</p><p>figure_8 - The raw data and occurences per node sheet for generating Figure_8.</p><p><tool> contains the framework and its source code that we used for conducting the experiments as well as the scripts that are used to post-process the detected nondeterminstic behavior and generate the summarized results in Section 4.</p>
Call Graph Soundness in Android Static Analysis
<div> <pre>## Artifact Folder Structure Below is a brief explanation of each directory of our artifacts: - **dataset/**: Contains the dataset used in our study. - **dynamic_analysis/**: Contains everything needed to reproduce our dynamic analysis experiments. - **static_analysis/**: Contains everything needed to reproduce our static analyses experiments. - **instrumentation/**: Contains the necessary files to instrument the apps for the dynamic analysis. - **SLR/**: Contains the excel files with the papers collected during our Systematic Literature Review (SLR). Please ensure that all the necessary files and resources are present in the respective directories before running any experiments.</pre> </div>
Static analysis evaluation experiment data
<p><span>This repository contains the experiment artifacts for our paper entitled </span><span>“Comprehensive Evaluation of Static Analysis Tools for Their Performance in Finding Vulnerabilities in Java Code” </span><span>submitted to the IEEE Access Journal.</span></p> <p><strong> </strong></p> <p><span>For each part of our experiment, we mention the related file name in this repository.</span></p> <p><strong> </strong></p> <p><span>Research Methodology:</span></p> <p><span>A. Experiment design: </span><span>no related documents.</span></p> <p><strong> </strong></p> <p><span>B. Preparing the Juliet Test Suite: </span></p> <p><span>In this section, we prepared Juliet for being analyzed by the five tools of the study. The related document for this section is called </span><span>juliet_preparation.pdf.</span></p> <p><strong> </strong></p> <p><span>C. Evaluation metrics: </span><span>no related documents.</span></p> <p><strong> </strong></p> <p><span>D. Experiment execution: </span></p> <p><span>Step 1: </span><span>For each of the five tools, review the documentation to identify and activate the related checker(s).</span></p> <p><span>The related documents of this section are </span><span>pmd_checkers.pdf.</span><span> </span><span>spotbugs_fsb_checkers.pdf,</span><span> </span><span>infer_checkers.pdf</span><span>, and </span><span>sonar_checkers.pdf</span><span>. Those documents include all the checkers that have been used and activated (if they were not active by default) to enable the Juliet analysis using the relevant checkers.</span></p> <p><strong> </strong></p> <p><span>Step 2: </span><span>Run each tool on each CWE and get the output reports.</span></p> <p><span>The related document is called </span><span>running_the_tools.pdf,</span><span> which includes the detailed steps for running each tool.</span></p> <p><strong> </strong></p> <p><span>Step 3: </span><span>For each tool, and each CWE, consider the relevant checker's results.</span></p> <p><span>no related document.</span></p> <p><strong> </strong></p> <p><span>Step 4:</span><span> For each tool, and each CWE, compute TP, FP, TN, and FN.</span></p> <p><span>The related document is </span><span>response_variables.xls</span></p> <p><strong> </strong></p> <p><span>Step 5: </span><span>Compute the response variables for each tool detecting each CWE.</span></p> <p><span>The related document is </span><span>response_variables.xls</span></p> <p><strong> </strong></p> <p><span>Step 6: </span><span>For each tool, compute collective evaluation metrics.</span></p> <p><span>The related document is </span><span>response_variables.xls</span><strong></strong></p>
Static Analysis Warnings and Automatic Fixing: A Replication for C# Projects : Dataset
<p>Dataset used in the paper accepted for publication at the REproducibility Studies and NEgative Results (RENE) Track of SANER 2022.</p> <p>Martin Odermatt, Diego Marcilio, Carlo A. Furia. Static Analysis Warnings and Automatic Fixing: A Replication for C# Projects, International Conference on Software Analysis, Evolution, and Reengineering (SANER) 2022.</p>
Data for the thesis "Exploring Heuristics for Predicting Microbenchmark Stability and Code Coverage using Static Code Analysis"
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Stability analysis of roadside backfill body at gob-side entry retaining under combined static and dynamic loading
<p>The applications of gob-side entry retaining (GER) techniques tends to increase over time as it can increase the minerals recovery rate and reduce the output of the waste. In the literature, only the performance of the roadside backfill body (RBB) at gob-side entry retaining under static loading has been investigated. However, failure mechanisms of the RBB under dynamic loading with various roof cutting angles have not been addressed. This study presented a numerical simulation of the fracture propagation and distribution in the roadside backfill body along the gob-side under combined static and dynamic loadings using the Trigon model built in UDEC (Discrete element methods) software. The influence of the roof cutting angles on the behavior of the RBB was also discussed. The input parameters were determined by back analysis with the field data. Results of the model show that static loading is a bigger contributor to the failure of the RBB compared to the dynamic loading. Several clear fractures were observed at top left and bottom right of the RBB and the fracture was more intensive at top left. In addition, it was found that if the roof cutting angle is 70°, the influence of the dynamic loading on the RBB is minimum, and the area and severity of the shearing failure in the RBB is minimum as well. As a result, it was determined the optimal roof cutting angle was 70° and a combined support measure of "roof cutting + roof support above RBBs + RBB reinforcement" is proposed. The application of roof cutting in gob-side entry retaining (RCGER) techniques in the Lingzhida coal mine indicates that deformation of the surrounding rocks can be effectively controlled.</p>
Dataset and tool - A Static Analysis for Discovering Type-Related Coding Idioms in Python Programs
<p>The archive file contains the following materials:</p> <ul> <li>`repos.tar.gz`: dataset of the collected Python repositories</li> <li>`tool.tar.gz`: programs used in the case studies</li> </ul>
Valar: Streamlining Alarm Ranking in Static Analysis with Value-Flow Assisted Active Learning
<p>static analysis alarm data</p>
Valar: Streamlining Alarm Ranking in Static Analysis with Value-Flow Assisted Active Learning
<p>static analysis alarm dataset</p>
Call Graph Soundness in Android Static Analysis
<pre>## Artifact Folder Structure Below is a brief explanation of each directory of our artifacts: - **dataset/**: Contains the dataset used in our study. - **dynamic_analysis/**: Contains everything needed to reproduce our dynamic analysis experiments. - **static_analysis/**: Contains everything needed to reproduce our static analyses experiments. - **instrumentation/**: Contains the necessary files to instrument the apps for the dynamic analysis. - **SLR/**: Contains the excel files with the papers collected during our Systematic Literature Review (SLR). - **results/**: Contains the results of our static analysis. Please ensure that all the necessary files and resources are present in the respective directories before running any experiments.</pre>
Stability analysis of roadside backfill body at gob-side entry retaining under combined static and dynamic loading
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Data set of ICSE 2021 paper submission "Static and Dynamic Analysis for the Migration of Monolith Systems to a Microservices Architecture"
<p>Data set of ICSE 2021 paper submission "Static and Dynamic Analysis for the Migration of Monolith Systems to a Microservices Architecture"</p>
Static and Dynamic Analysis
<p>Contains the JSON files with the collected data and the results of the study done.</p>
Data Set of From Monolith to Microservices: Static and Dynamic Analysis Comparison
<p>Contains the JSON files with the collected data and the results of the study done.</p>
Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset
<p>This is a replication data package for a paper titled "<em>Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset</em>".</p> <p>Paper abstract: Even though Static Code Analysis (SCA) tools are integrated into many modern software building and testing pipelines, their practical impact is still seriously hindered by the excessive number of false positive warnings they usually produce. To cope with this problem, researchers have proposed several post-processing methods that aim to filter out false hits (or equivalently identify ``actionable'' warnings) after the SCA tool produced its results. However, we found that most of these approaches are targeted (i.e., deal with only a few SCA warning types) and evaluated on synthetic benchmarks or small-scale manually collected data sets (i.e., with typical sample sizes of several hundred).</p> <p>In this paper, we present a code embedding-based approach for filtering false positive warnings produced by 160 different SonarQube rule checks, one of the most widely adopted SCA tools today. We evaluate the method on a dataset containing 224,484 real-world warning samples fixed (true positive samples) or ignored (false positive samples) by the developers, which we collected from 9,958 different open-source Java projects from GitHub using a data mining approach. It is the most extensive real-world study and public dataset we know of in this area. Our method works with an accuracy of 91% (best F1-score of 81.3% and AUC of 95.3%) for the classification of SonarQube warnings.</p>
Are automated static analysis tools worth it? An investigation into relative warning density and external software quality on the example of Apache open source projects
<p>This is the dataset for the publication "Are automated static analysis tools worth it? An investigation into relative warning density and external software quality on the example of Apache open source projects".</p> <p>It contains just-in-time defect-prediction style data in jit_data2.tar.gz as well as warnings generated by PMD 6.31.0 in warnings_data3.tar.gz.</p> <p>Further information and scripts for generating the plots and tables can be found in the <a href="https://github.com/atrautsch/emse2021a_replication">replication kit</a>.</p>
Data from: Scaling of morphological characters across trait type, sex and environment: a meta-analysis of static allometries
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Next Generation Sequencing Facilities Quantitative Analysis of Wild type CD8+ T cells with 0.3T static magnetic field or not
GEO Series GSE113858. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Comparative Analysis of the Physiological and Transport Functions of Various Sources of Renal Proximal Tubule Cells Under Static and Fluidic Conditions in PhysioMimix TC12 Platform
GEO Series GSE246180. Homo sapiens. 69 samples. Type: Expression profiling by high throughput sequencing.
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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)
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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.