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6 results for “Source Code Changes”
Improving source code change set analysis by using a visual tool. Controlled experiment dataset.
<p>Modern software development is performed by developing features in isolated branches by each member of a software development team. When these branches need to be integrated, they have to be manually reviewed by an integrator. Source code reviewing can be a tedious and time consuming task, which is normally performed by hand reviewing a textual diff of the change set.</p> <p>The difficulty of reviewing source code changes can have a negative impact on the accep- tance of these changes. It has been shown that this can imply the rejection of important bug fixes for a software project.</p> <p>In order to facilitate the task of reviewing source code change sets, we designed and implemented a visual tool. Our visual tool helps in assessing source code change sets by providing different views of the change set: an overall overview of the change set with metrics, and a visualization representing the structural changes in the source code. We evaluated our visual tool by performing a controlled experiment with software practitioners.</p> <p>Our experimental results show no significant differences between using our visual tool and a textual diff tool in the following terms: time of analysis, precision of the analysis, and inference of intention of changes. However, we did find a positive user perception, and a reduced mental load when using our visual tool.</p> <p>Anonymized datasets with the answers to the controlled experiments for evaluating the Git Thermite visual tool for assessing source code change sets. Copies of these datasets are provided in both, CSV format, and OpenDocument format.</p> <p>Exact copies of the learning materials that was provided to the participants during the execution of the controlled experiments is also included along the dataset.</p>
CROP: linking code reviews to source code changes
<p>he Code Review Open Platform, a.k.a. CROP, is an open-source dataset of code review data. CROP collects code review information from open-source software systems and links this data to complete versions of the code base for each of these systems. CROP was first designed by <a href="https://mhepaixao.github.io/homepage/">Matheus Paixao</a> as part of his PhD thesis in the <a href="http://crest.cs.ucl.ac.uk/about/">CREST Centre</a> at University College London. <a href="http://www0.cs.ucl.ac.uk/staff/j.krinke/">Dr. Jens Krinke</a>, <a href="https://donggyun.com/">Donggyun Han</a> and <a href="http://www0.cs.ucl.ac.uk/staff/M.Harman/">Prof. Mark Harman</a> have also contributed for the first incarnation of CROP.</p> <p> </p> <p>CROP collects code review information from open-source software systems and links this data to complete versions of the code base for each of these systems.</p> <p> </p> <p>Given a certain software system, CROP contains code review data and versions of the code base for each revision ever submitted for review, including intermediary revisions before merging and revisions that were even abandoned by the system's developers. Each version of the system represents a complete snapshot of the system's code base, in a way that each revision of the system is fully buildable, compilable and testable.</p> <p> </p> <p>By leveraging the data contained in CROP, software engineering researchers and practitioners can perform empirical studies to assess how effective the code review process is for different aspects of software development. Since CROP provides complete snapshots of the software system, these experiments can be enhanced by using a wide range of approaches for static and dynamic analysis.</p> <p> </p> <p>Moreover, during code review, developers are constantly providing reasoning and rationale for the changes they make in the system, both when they submit code for review and when they inspect code from their peers. Thus, the data contained in CROP is a valuable source of knowledge regarding motivation for and explanation of software changes.</p> <p> </p> <p>For more information on the CROP dataset, including its structure, technical details, publication history and so on, please visit its official website in <a href="https://crop-repo.github.io/">crop-repo.github.io</a>.</p>
What really changes when developers intend to improve their source code: A commit-level study of static metric value and static analysis warning changes
<p>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".</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p>
Changes in holopelagic Sargassum spp. biomass composition across an unusual year - supporting particle data and analysis source code
<p>As specified in 'Data, Materials, and Software Availability' of Tonon et al. (2024), PNAS, Vol. 121, e2312173121, https://doi.org/10.1073/pnas.2312173121, the following data and software are provided:</p> <p>Particle forward tracking (statistical data – for Fig. 2)</p> <p>Particle backward tracking (primary data – for Fig. 3)</p> <p>Analysis of primary data for gridded particle fractional coverage and mean age (Fortran source code)</p>
Replication Package for the paper "AI-based Fault-proneness Metrics for Source Code Changes"
<p>This is the replication package for the paper "<em>AI-based Fault-proneness Metrics for Source Code Changes</em>", submitted at the <em>IWSM-Mensura '23 </em>conference.</p> <p>The archive is a <em>Docker </em>image file with a fully setup and working environment to re-execute the experiments involved in the manuscript. We pre-loaded all libraries and codeBERT models to ease the replication process and avoid compatibility issues, as the environment cannot be easily managed using <em>Dockerfile</em>s.</p> <p>To run the image, a <em>Docker</em> installation is needed. Once downloaded, from the command line type:</p> <pre><code>docker load -i </path/to/downloaded/ai-proneness-replication.tar></code></pre> <p>After the loading process, you can run the container by typing:</p> <pre><code>docker run -it mensura/ai-proneness-replication:1.0</code></pre> <p>All the source code and the dataset to re-execute the experiment is located into the <em>/Replication</em> folder. The folder contains the results of our experimentation in CSV and MS Excel format, along with the following subdirectories:</p> <ul> <li><em>dataset</em>: a replication of the used dataset. The file <em>dataset.csv</em> gives information on all the entries, while the <em>code </em>folder contains a subdirectory for each sample, named by its id. In the folder, the file <em>old.txt </em>and<em> </em><em>new.txt </em>refers to the older and newer version of the method, respectively; <em>gitdiff.txt </em>stores the raw <em>git-diff</em> command output, while <em>diff.html</em> stores a more human-readable version of the differences.</li> <li><em>ai-fault-proneness-tk-replication</em>: the Java code used to apply Tree Kernel techniques on the dataset (we used JDK-11, embedded within the container). To build and execute the package, refer to the file <em>README.md</em> in the folder. For convenience, we also provided an executable JAR file <em>ai-fault-proneness-tk-replication-1.0-jar-with-dependencies.jar </em>that can be run directly and saves the output in a CSV file in the <em>results</em> folder of the replication package.</li> <li><em>code-embeddings-and-analysis</em>: python scripts to execute the <em>codeBERT</em>-based approaches and to extract the <em>diff</em> statistics. To execute all the steps, a convenience shell script <em>execute.sh</em> has been pre-loaded and can be executed to automatize all the process.</li> </ul>
Source data and code for: Climate change transforms the functional identity of Mediterranean coralligenous assemblages
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