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11 results for “Kotlin”
Taxonomy of Security Weaknesses in Java and Kotlin Android Apps
<p>This is the replication package for the paper "Taxonomy of security weaknesses in Java and Kotlin Android apps" accepted for inclusion in The Journal of Systems & Software</p>
Replication Package for "Type Inference in Kotlin: An Exploratory Study of Developer Usage Patterns"
<p>This dataset includes scripts and data files used to generate all analysis and results from the paper. A <strong>README.md</strong> file is included for details on using the scripts.</p> <p>The dataset is quite large. It is broken down into three archives. All scripts are in <strong>replication-pkg.zip</strong> and the other 2 files only contain data. So if you want to just inspect the analysis, you only need that single zip.</p> <p>If you grab the <strong>data-cache.zip</strong> file and extract it, it will need around 1GB of space. This is the processed dataset stored in Parquet files. Use this if you want to just recreate the tables/figures from the paper.</p> <p>If you want to make changes to the analyses, you will need the raw data in <strong>data.zip</strong>. This will need around 13GB of space once extracted. If you then generate the CSV files from those TXT files (which you will need to do for any custom analysis), you will need an additional 12GB of space.</p>
Extending Refactoring Detection to Kotlin: A Dataset and Comparative Study
<p><span>This page serves as supplementary material for the article </span><span>Extending Refactoring Detection to Kotlin: A Dataset and Comparative Study</span><span>. In detail, we offer a downloadable dataset in JSON format, comprising 2,043 instances of refactoring instances covering 21 distinct refactoring types. These refactorings are applied within 200 commits across 10 Kotlin repositories and are manually validated by the authors of the paper.</span><span> </span></p> <p><span>Results.json: The results as </span><span>a single</span><span> JSON file.</span></p> <ul> <li> <p><span>Overview of JSON Template: Each entry in this file provides details </span><span>about</span><span> commits, including </span><span>their </span><span>SHA-1 hash, GitHub URL, and an exhaustive list of refactorings </span><span>identified</span><span> </span><span>in </span><span>that specific commit. Refactorings are defined by their type (e.g., 'RenameClass') and are accompanied by detailed descriptions of the </span><span>refactoring</span><span>. Additionally, the entry specifies the tools that </span><span>detected</span><span> the refactoring, such as 'RefDetect' and 'KotlinRMiner.' The validation field indicates whether the refactoring was confirmed, using 'TP' for true positive and 'FP' for false positive. Optional comments and validator information may be also included</span><span>.</span></p> </li> </ul> <p><span>AllCommits.zip: The Results for each repository in a single JSON file.</span></p> <p><span>Times.xlsx: The Excel file containing the time taken by each tool (RefDetect and KotlinMiner) to identify refactoring in each commit.</span></p> <p><span>Results.xlsx: T</span><span>he Excel file contains precision, recall, and F-score metrics for all identified refactoring types.</span></p>
Replication package for "An Exploratory Study on Default Class Openness in Java and Kotlin"
<p>This dataset includes scripts, text files, and cached CSV/Parquet or raw TXT data files used to generate all analysis and results from the paper. A <strong>README.md</strong> file is included in <strong>replication-pkg.zip</strong> for details on using the scripts.</p> <p>If you only want to inspect the figures, you do not need a data ZIP.</p> <p>If you want to simply re-generate the figures without changes, download <strong>data-cached.zip</strong>. If you want to make any sort of change to the analyses, you will want to download <strong>data-csv.zip</strong> and/or <strong>data-raw.zip</strong>. The CSV files are converted versions of the raw TXT files.</p>
Replication package for "How does Migrating to Kotlin Impact the Run-time Efficiency of Android Apps?"
<p><strong>Replication Package</strong></p> <p>This section briefly describes the contents of the replication package, which makes it possible to replicate this study.</p> <p><strong>Dataset construction:</strong> The recreation of the GitLab AndroidTimeMachine instance is done using the snapshot related code found in the Kotlin program. This program uses a running instance of the AndroidTimeMachine’s Neo4j database and uses its data to make API calls to a running GitLab instance which imports the GitHub projects found. This step can be skipped, and a GitLab instance can be instantiated directly using our created Docker volumes. The is also capable of performing the Kotlin filtering steps. For this, it uses the results of the previous steps as input and clones each repository for performing the filtering procedure.</p> <p><strong>SLOC counting:</strong> SLOC counting related scripts are found in and implement our methods by cloning the project and running CLOC. It also plots the line graphs that we manually categorized into evolution trends. The raw SLOC data is found in , and all of the plotted evolution trends are found in.</p> <p><strong>Kotlin migration mining:</strong> The implementation for mining is located in , and implements our methods by cloning a repository from the running GitLab instance and executing described methods. All commits we found are located in.</p> <p><strong>Run-time efficiency:</strong> As mentioned in the thesis, we used AndroidRunner. A fork of AndroidRunner we used for conducting our experiments is located in . In the folder , each configuration combined with raw results is found per test subject. The final data we used after removing corrupt frame times is located in the folder, which also contains . This python script implements our statistical analysis methods.</p>
Replication package for "Method Chaining Redux: An Empirical Study of Method Chaining in Java, Kotlin, and Python"
<p>This dataset includes scripts and data files used to generate all analysis and results from the paper. A <strong>README.md</strong> file is included for details on using the scripts.</p> <p>The dataset is quite large. It is broken down into three archives. All scripts are in <strong>replication-pkg.zip</strong> and the other 2 files only contain data. So if you want to just inspect the analysis, you only need that single zip.</p> <p>If you grab the <strong>data-cached.zip</strong> file and extract it, it will need around 3GB of space. This is the processed dataset stored in Parquet files. Use this if you want to just recreate the tables/figures from the paper.</p> <p>If you want to make changes to the analyses, you will need the raw data in <strong>data-raw.zip</strong>. This will need around 29GB of space once extracted. If you then generate the CSV files from those TXT files (which you will need to do for any custom analysis), you will need an additional 22GB of space.</p>
Empirical Study Data for Test Program-Based Generative Fuzzing for Differential Testing of the Kotlin Compiler
<p>Empirical Study Dataset for MSc. Thesis titled "Test Program-Based Generative Fuzzing for Differential Testing of the Kotlin Compiler". The data contains automatically generated Kotlin files, the results of differentially testing the generated files, and aggregated data containing file information, and compilation features.</p>
Dataset and Tool for the Paper: Cross-Language Dependencies: An Empirical Study of Kotlin-Java
<ul> <li>Folder <strong>tool-to-extract-kotlin-java-dependencies</strong> contains a runnable jar of our tool to extract Kotlin-Java dependencies, please follow README in this folder to execute this tool.</li> <li>Folder <strong>accuracy-verification </strong>contains the source code of the ground-truth project, along with manual dependency inspections from file to file and from line to line. The extracted dependencies result of this project from our tool is also included in this folder.</li> <li>Folder <strong>rq1-dependencies-graph-for-each-project </strong>contains all dependency graphs of our 23 subjects in Json format.</li> <li>Folder<strong> rq1-circular-layout-for-each-project</strong> contains the circular layout of dependencies among source files of our 23 subjects.</li> <li>Folder <strong>rq2-maintenance-cost-for-file-with-out-kotlin-java-interactions </strong>contains <ul> <li><strong>details</strong> lists the #commits and loc for each type of source files in details <ul> <li>_java_in_cross_language.csv represents Java source files participating in Kotlin-Java interactions</li> <li>_java_not_in_cross_language.csv represents Java source files not participating in Kotlin-Java interactions </li> <li>_kt_in_cross_language.csv represents Kotlin source files participating in Kotlin-Java interactions</li> <li>_kt_not_in_cross_language.csv represents Kotlin source files not participating in Kotlin-Java interactions </li> </ul> </li> </ul> </li> <li>Folder <strong>rq3-common-mistakes </strong>contains 103 cases in the manual inspections.</li> </ul>
Subset of The Most Popular Kotlin Apps on Github
Open the record for dataset details and reuse information.
A comparison of compiler strategies for serverless functions written in Kotlin - dataset
<p>The result from the reaseach one for the thesis paper "A comparison of compiler strategies for serverless functions written in Kotlin".</p>
Dataset for "Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler"
<p>Dataset used in "Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler". <br> The data is based on open source code once publicly available on GitHub.</p>
ScienceDex guides
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International Brain Laboratory public data
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