Find research datasets worth reusing

opencc-by-4.0Apr 2024View details →

Codes and Data for "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy"

This file contains the analysis code and a condensed version of data to reproduce figures in the paper "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy".

opencc-by-4.0Jun 2020View details →

Combining dynamic and static analysis for automated grading SQL statements

Introduction Our experiment was conducted in an undergraduate Relational Database course at the Australian National University. The experiment was conducted on August 10th 2018 when students enrolled in the Relational Database course started to learn relational data model and SQL. The experiment was carried out fully online for three weeks and a total of 393 students were enrolled. The students were asked to login in an online assessment platform and complete 15 exercises. This platform provided an SQLite environment in students browsers by compiling the SQLite C code with Emscripten. Students were allowed to submit and execute their answers in the form of SQL statements. If the execution result of the statement submitted by the student is the same as that of the reference statement, the online assessment platforms will return a feedback message indicating that the execution result is correct. During the interaction with the assessment platform, statements submitted by students were recorded and archived. Overall, our experiment had collected 12,899 statements submitted by students. To create a benchmark dataset that can be used to evaluate different grading approaches, we randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we average the scores provided by the three assistants and take it as the final score of each statement. The dataset collected in this experiment is ready for public release. All experimental data are stored in Submission.sqlite, which is an SQLite database file. It is recommended to use software such as DB browser or SQLite expert to explore the database.   Datatable description   exercises_result This datatable stores the statements submitted by students. Based on the execution result of statement, statements were divided into three categories. <ul> <li>noninterpretable: the statement is non-executable.</li> <li>partially correct: the execution result of statement is different from the expected result.</li> <li>correct: the execution result of the SQL statement is the same as the expected result.</li> </ul> After analyzing the correct statements, we found that the correct set contains some statements carefully constructed by students to deceive the examination system. Take exercise 1 as an example, the task is to answer the following questions using SQL statements. Question: Assume persons who were born in the same year are the same age and there is only one youngest person (with no ties/draws) in this database, who is/are the second youngest person(s) in the database? List the id(s) of the person(s). The reference statement to this exercise is: <pre><code class="language-sql">SELECT p.id FROM person p WHERE p.year_born = (SELECT MAX(year_born) FROM person WHERE year_born < (SELECT MAX(year_born) FROM person)); </code></pre> By exploring the database or trying to execute different statements, some students found that the ID of the person who met the conditions was '00000842', so the following statement was submitted. <pre><code class="language-sql">select id from person where id ='00000842'; </code></pre> The execution result of the above code was correct, but it was obviously not what the tutor expected. Therefore, we identified such statements as 'cheating'. Table 1 Description of exercises_result table. <table> <thead> <tr> <th> field </th> <th> desc </th> <th> datatype </th> </tr> </thead> <tbody> <tr> <td> submission_id </td> <td> Submission ID </td> <td> INT </td> </tr> <tr> <td> submitted_answer </td> <td> statement submitted by student </td> <td> TEXT </td> </tr> <tr> <td> submission_time </td> <td> Submission time </td> <td> NUM </td> </tr> <tr> <td> exercise_id </td> <td> Exercise ID </td> <td> INT </td> </tr> <tr> <td> is_correct </td> <td> Mark whether the statement is correct </td> <td> INT </td> </tr> <tr> <td> student_id </td> <td> Student ID </td> <td> INT </td> </tr> <tr> <td> category </td> <td> categories of statement </td> <td> TEXT </td> </tr> </tbody> </table>   exercises_benchmark This datatable stores the scores provided by different assistants. We randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we averaged the scores provided by the three assistants as the final score of each statement. Table 2 Description of exercises_benchmark table. <table> <thead> <tr> <th> Field </th> <th> comment </th> <th> datatype </th> </tr> </thead> <tbody> <tr> <td> Submission_id </td> <td> Submission ID </td> <td> INT </td> </tr> <tr> <td> grade </td> <td> grade provided by tutor </td> <td> REAL </td> </tr> <tr> <td> tutor </td> <td> tutor </td> <td> TEXT </td> </tr> </tbody> </table>   exercises_exercise This datatable stores the exercises provided by tutor. Table 3 Description of exercises_exercise table. <table> <thead> <tr> <th> Field </th> <th> comment </th> <th> datatype </th> </tr> </thead> <tbody> <tr> <td> id </td> <td> Exercise ID </td> <td> INT </td> </tr> <tr> <td> title </td> <td> Title of exercise </td> <td> TEXT </td> </tr> <tr> <td> preamble </td> <td> Description of exercise </td> <td> TEXT </td> </tr> <tr> <td> difficulty </td> <td> Coefficient of difficulty </td> <td> integer </td> </tr> <tr> <td> ref </td> <td> Reference statement </td> <td> integer </td> </tr> </tbody> </table>   database schema Please refer to db_schema.pdf for the database schema used in the experiment.   BibTex if you want to cite our paper:   <blockquote> <pre>@article{wang2020combining, title={Combining dynamic and static analysis for automated grading SQL statements}, author={Wang, Jinshui and Zhao, Yunpeng and Tang, Zhengyi and Xing, Zhenchang}, journal={J Netw Intell}, volume={5}, number={4}, pages={179--190}, year={2020} }</pre> </blockquote>

opencc-by-4.0Mar 2019View details →

Are Static Analysis Violations Really Fixed? A Closer Look at Realistic Usage of SonarQube. Dataset for OSS organizations

Dataset containing all rules, files and issues mined for Apache Software Foundation and Eclipse Foundation.

opencc-by-4.0Jun 2023View details →

Alert Type Frequency Assessment of Open-Source Static Analysis Tools and Codebases

This includes all data needed to replicate and validate our frequency analysis of static analysis (SA) alerts produced using open-source SA tools on several OSS codebases. It includes instructions how to get and run the SA tools, a Dockerfile to conveniently get and use the SA tools, raw SA tool output, some python scripts to parse that output, parsed SA data and aggregate analyses, and SA data augmented with CERT coding rule and CWE data.   The SA tools used: <ul> <li>clang-tidy version 15.07   </li> <li>cppcheck version 2.9   </li> <li>CERT Rosecheckers   </li> </ul> The codebases analyzed: <ul> <li>zeek version 5.1.1</li> <li>git version 2.39.0</li> <li>dos2unix version 7.4.3</li> </ul>

opencc-by-4.0Mar 2020View details →

SATune: An Auto-tuning Approach for Configurable Static Analysis Tools

Version 2.0.0 updates: Renamed tool to SATune. ------- This contains both the executables and the data for our ICST 2021 submission, "SATune: An Auto-tuning Approach for Configurable Static Analysis Tools." The results are in results.tar.xz, and the SATune source code and experimental environment is is experiments.tar.xz.

openapache2.0Nov 2023View details →

Evaluation Artifacts of RESTRuler, a Static Analysis Tool to Detect Design Rule Violations in OpenAPI Definitions

All artifacts related to the empirical evaluation of the RESTRuler CLI, a Java-based tool to identify design rule violations in OpenAPI descriptions You can find the latest version here: <a href="https://github.com/restful-ma/rest-ruler-evaluation">https://github.com/restful-ma/rest-ruler-evaluation</a>

opencc-by-4.0Sep 2021View details →

What really changes when developers intend to improve their source code: A commit-level study of static metric value and static analysis warning changes

This is the dataset for the publication "What really changes when developers intend to improve their source code: A commit-level study of static metric value and static analysis warning changes". It contains a random sample of 2533 commits from 54 Java Apache open source projects classified by two researchers into perfective, corrective and other changes (manual_labels.csv).  Moreover, we include static source code metrics and static analysis warnings for the 2533 changes in al_changes_gt.csv.gz. In addition, we include the full dataset of 125482 commits in all_changes_sebert.csv.gz with all metrics and automatic labels for every commit that was not manually labeled. The automatic labels were provided by a fine-tuned transformer model (BERT) pre-trained exclusively on software engineering data. We also provide the fine tuned version of the pre-trained model in seBERT_fine_tuned_commit_intent.tar.gz as well as a Snapshot of the SmartSHARK MongoDB database used in gathering the raw data in smartshark_emse.agz. The model can be tested live on the <a href="https://user.informatik.uni-goettingen.de/~trautsch2/emse_2021/commit_intent.html">website</a> accompanying the publication.

openmit-licenseMay 2024View details →

Replication Package for "On the correlation between Architectural Smells and Static Analysis Warnings"

<div> <div> <div> <div> <div> <h1>Replication Package for the Paper: "On the Relation between Architectural Smells and Static Analysis Warnings"</h1> <h2>Authors</h2> <ul> <li>Matteo Esposito, University of Oulu, Finland</li> <li>Mikel Robredo, University of Oulu, Finland</li> <li>Francesca Arcelli Fontana, University of Milano-Bicocca, Italy</li> <li>Valentina Lenarduzzi, University of Oulu, Finland</li> </ul> <h2>Content Overview</h2> This replication package contains the following materials: <ul> <li>Tables: Excel files that include all hypothesis testing data, including normality tests for each hypothesis.</li> <li>Data: RAW Qualitas Corpus dataset and aggregated SAT output.</li> <li>SAT Instructions: A PDF file providing detailed instructions for the SAT setup and execution.</li> </ul> <h2>Contact Information</h2> For any issues, questions, or further assistance, please do not hesitate to contact the authors of the paper. We are here to help! </div> </div> </div> </div> </div>

opencc-by-4.0Feb 2019View details →

Experimental–Computational Analysis of Nucleation Sites for Primary Static Recrystallization

This repository contains supplementary material to our paper. Specifically, the Matlab, Python,a and Shell scripts and cellular automaton source code we used to run and post-process the simulations as well as the simulation results: MTEXEBSDMappingStructureInitialization.zip Specifies, using MTex v5.0.3, how we converted the measured SEM/EBSD mapping to a synthetic 2d microstructure. SCORESourceCode.zip Specifies the source code of SCORE. Version 1.2.1. Demands a local HDF5 installation. MPI/OpenMP parallelized. Inspect www.github.com/mkuehbach/SCORE for further details on how to compile and background to the model an implementation. ExecuteSimulations.zip Specifies shell scripts and UDS input files to execute the simulations. Details via these UDS files also all parameter settings we used to reproduce the runs. ComparisonXaXv.tar.gz Compares in summarized form, and extracted from the RXAreaFractionDepthProfile folder files, the area vs volume fraction at specified time snapshots for the z= [0.0, 0.5, 1.0] RDTD section.   Inherited_GrainSizeMicrostructure.zip ANG-like serial sectioning snapshot results and IPF visualization of microstructure evolution for those simulation cases in which the nuclei inherited the orientation from their site. Random_GrainSizeMicrostructure.zip ANG-like serial sectioning snapshot results and IPF visualization of microstructure evolution for those simulation cases in which the nuclei had random orientations form the SO3. RXAreaFractionDepthProfile.zip Specifies the evolution of the area fraction recrystallized with grains in cross-sectional area >=13px for every RDTD layer. The corresponding parameterization is detailed in the *.uds input file which specifies all constitutive parameter and log settings of the automaton. The simulation is executed by compiling the program and linking to HDF5. The OMP_NUM_THREADS environment variable should be set to not more than 10. The SCORE is executed as follows: mpirun -np 1 ./score <simid> <udsfile> <KAM Ang EBSD file> 1>STDOUT.txt 2>STDERR.txt Profiling.zip Details the execution log of the automaton ie runtime individual composition of nuclei volume transformation progression, interfacial area evolution, etc. SingleGrainData.zip Details the volume consumption / volume gain kinetics of every single deformed / recrystallized grain. TemperatureTimeProfile.zip Details the time/temperature and step profile of the numerical integration. This allows to map integration time steps to simulated microstructural states. ThreadProfilingGrowth.zip Details the evolution of the recrystallized volume versus time and number of active cells per thread sub-domain. MartinPostprocessingScripts.zip Is a collection of Python and MTex scripts to compile the area size distribution and compute ODFs.

opencc-by-4.0Jan 2019View details →

A time-course analysis using Differential Static Light Scattering (DSLS) of purified HTT1-3144 Q23 - 2019/01/28

Project: Biophysical investigation of purified HTT protein samples Experiment: A time-course analysis using Differential Static Light Scattering (DSLS) of purified HTT1-3144Q23  Date completed: 2019/01/28 Rationale: Time and resources in the HD field have been primarily focussed on understanding HTT aggregation looking as caspase cleavage products spanning aa. 1-586 or exon 1 spanning aa. 1-90. However, we know that HTT protein purified in its apo form is able to self-associate into larger oligomeric species and that monomer, dimer and larger species are found following FLAG-affinity chromatography as determined by size-exclusion chromatography (SEC) and SEC-multi-angle light scattering (SEC-MALS). This experiment aimed to begin to investigate how HTT self-associates and aggregates over time in a range of different conditions.

opencc-by-4.0Jun 2019View details →

Replication Package for "How Developers Engage with Static Analysis Tools in Different Contexts"

This is the replication package for the paper "How Developers Engage with Static Analysis Tools in Different Contexts". We include all the artifacts necessary to replicate the results obtained in our paper. Specifically, we provide (i) the survey questions together with all the valid answers we received including the demographics of our respondents, (ii) the most relevant statements that we extracted from the interviews including the demographics of our interviewees, (iii) the results of the card sorting performed on the development activities where our participants adopt Static Analysis Tools, (iv) all the data related to Krippendorff’s Alpha calculation for the performed card sorting, and (v) mapping of ASATs to the "rules" categories defined by Novak et al. (2010) and script for calculating occurrence, definition, and enforcement of the different ASAT types together with input and output data. Furthermore, we include the list of links to Reddit posts and inspected open-source projects together with their inspection data and the scripts for computing the inter-rater agreement during the inspection. Finally, we provide the Github features computed for each project and script for generating the sets of projects.

opencc-by-4.0Sep 2021View details →

Dataset for the paper Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification

Short Description: This is the dataset for the paper "Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification", accepted for publication in the Journal of Systems and Software.  Link to this repository: <a href="https://github.com/droidxp/paper-replication-package">https://github.com/droidxp/paper-replication-package</a> Authors of the Paper <ul> <li>Francisco Handrick da Costa</li> <li>Ismael Medeiros</li> <li>Thales Menezes</li> <li>João Victor da Silva</li> <li>Ingrid Lorraine da Silva</li> <li>Rodrigo Bonifácio</li> <li>Krishna Narasimhanb</li> <li>Márcio Ribeiro</li> </ul> Abstract The popularization of the Android platform and the growing number of Android applications (apps) that manage sensitive data turned the Android ecosystem into an attractive target for malicious software. For this reason, researchers and practitioners have investigated new approaches to address Android's security issues, including techniques that leverage dynamic analysis to mine Android sandboxes. The mining sandbox approach consists in running dynamic analysis tools on a benign version of an Android app. This exploratory phase records all calls to sensitive APIs. Later, we can use this information to (a) prevent calls to other sensitive APIs (those not recorded in the exploratory phase) or (b) run the dynamic analysis tools again in a different version of the app. During this second execution of the fuzzing tools, a warning of possible malicious behavior is raised whenever the new version of the app calls a sensitive API not recorded in the exploratory phase. The use of a mining sandbox approach is an effective technique for Android malware analysis, as previous research works revealed. Particularly, existing reports present an accuracy of almost 70% in the identification of malicious behavior using dynamic analysis tools to mine android sandboxes. However, although the use of dynamic analysis for mining Android sandboxes has been investigated before, little is known about the potential benefits of combining static analysis with a mining sandbox approach for identifying malicious behavior. Accordingly, in this paper we present the results of two studies that investigate the impact of using static analysis to complement the performance of existing dynamic analysis tools tailored for mining Android sandboxes, in the task of identifying malicious behavior. In the first study we conduct a non-exact replication of a previous study (hereafter BLL-Study) that compares the performance of test case generation tools for mining Android sandboxes. Differently from the original work, here we isolate the effect of an independent static analysis component (DroidFax) they used to instrument the Android apps in their experiments. This decision was motivated by the fact that DroidFax could have influenced the efficacy of the dynamic analyses tools positively---through the execution of specific static analysis algorithms DroidFax also implements. In our second study, we carried out a new experiment to investigate the efficacy of taint analysis algorithms to complement the mining sandbox approach previously used to identify malicious behavior. To this end, we executed the FlowDroid tool to mine the source-sink flows from benign/malign pairs of Android apps used in previous research work. Our study brings several findings. For instance, the first study reveals that DroidFax alone (static analysis) can detect 43.75% of the malwares in the BLL-Study dataset, contributing substantially in the performance of the dynamic analysis tools in the BLL-Study. The results of the second study show that taint analysis is also practical to complement the mining sandboxes approach, with a performance similar to that reached by dynamic analysis tools.

opencc-by-4.0Sep 2021View details →

Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components

The provided dataset contains the data used by "Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components", in order to evaluate the maintainability degree of a software class and identify software components that will eventually become non-maintainable.

opencc-by-4.0Oct 2022View details →

Energy Consumption Estimation of API-usage in Smartphone Apps via Static Analysis

OPEN CALL FOR COLLECTING ENERGY PROFILES @ <a href="https://github.com/AbdulAli/replication-kit-msr-2023">https://github.com/AbdulAli/replication-kit-msr-2023</a> Cite this work as: @inproceedings{bangash2023msr,     title={Energy Consumption Estimation of API-usage in Mobile Apps via Static Analysis},     author={Bangash, Abdul Ali and Jamal, Qasim and Eng, Kalvin and Ali, Karim and Hindle, Abram},     booktitle={2023 20th International Conference on Mining Software Repositories (MSR)},     pages={5721--5730},     year={2023},     organization={IEEE} } This is the replication-kit of the paper published at MSR 2023. It includes: <ul> <li>SQLite operations' benchmarks</li> <li>SQLite benchmarks' energy profiles</li> <li>The E-Factor Calculation program</li> </ul>

opencc-by-4.0May 2020View details →

Reuse of static analysis rule dataset (CADD system)

Reuse of static analysis rules study dataset (in Portuguese). Anonymous author for double-blind review.

opencc-by-4.0Sep 2020View details →

Eclipse Static Analysis - 10 Java projects

This dataset consists of Eclipse's static analysis performed on 10 Java projects. For each .java file of a test project, we ran a static analysis using Eclipse JDT Core allows us to retrieve all the possible function calls based on typing/imports for a given completion site). Each java project has three files structured as follows: <ul> <li>*.json file. The file contains all the method declarations of the project and the function calls in their body. For each function call, the file lists all the possible function call that could have been made at that place in the source code. For practical purposes, we splitted this file into two text files.</li> <li>*_sequences.txt file. This file consists of all the method declaration + function call sequences in the project. The last element of each line corresponds to a completion site.</li> <li>*_proposals.txt file. Each line is made of the function-call suggestions retrieved by static analysis for the corresponding line in the *_sequences.txt file.</li> </ul> The corpus was used for the experiments in the paper Combining Code Embedding with Static Analysis for Function-Call Completion. Github repository to replicate the experiments: https://github.com/mweyssow/cse-saner

opencc-by-4.0Oct 2020View details →

[CTD-ES] Automatically Fixing Static Analysis Tools Violations

Video in Portuguese with a short presentation summarizing some of the main points of the research conducted during my (Diego) masters' under the supervision of Prof. Dr. Rodrigo Bonifácio.