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116 results for “Code Review”
Data and code to accompany 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record'
<p>Data and analysis code to accompany the manuscript 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record', published in <em>Oceanography and Marine Biology: An Annual Review </em>(<a href="https://doi.org/10.1201/9781003363873-2">https://doi.org/10.1201/9781003363873-2</a>). Data files include records of sea surface temperature (SST) collected in Pacific Grove, California, USA from January 20, 1919 to the end of 2020. The analysis code produces a continuous 100+ year record with adjustments made for time of day the data were collected, and filling gaps in the data set where necessary. </p> <p>This dataset makes use of an earlier 83-year version of the sea surface temperatures produced by Breaker et al. 2005 available at <a href="https://aquadocs.org/handle/1834/20890">https://aquadocs.org/handle/1834/20890</a>, with the data file available at <a href="https://purl.stanford.edu/rc833pc4972">https://purl.stanford.edu/rc833pc4972</a> </p>
Data and Code for "Value dissonance in research(er) assessment: Individual and institutional priorities in review, promotion and tenure criteria related to research quality, quantity, openness and responsibility"
<p>This snapshot contains code and data for the preprint "Value dissonance in research(er) assessment: Individual and institutional priorities in review, promotion and tenure criteria related to research quality, quantity, openness and responsibility".</p> <p>Instructions on re-using the data and running the code can be found in the README.md.</p> <p>Changes:</p> <ul> <li>Added survey instrument and informed consent.</li> </ul>
Supplementary data for A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms
<p>The attached Microsoft Excel files contain the data and diagrams of the paper:</p> <p><strong>A systematic literature review on the code smells datasets and validation mechanisms</strong></p> <p>The article is under review in the ACM Computing Surveys.</p> <p> </p>
Supplementary data for a systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges
<p>The Microsoft Excel files containing the supplementary data and diagrams for the paper:</p> <p><strong>A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges</strong></p> <p>The article is under review in the Journal of Systems and Software.</p> <p>In this version, the literature search has been performed between April and May 2023, and studies published until that date has been mentioned in the attached Excel files.</p> <p>This version (v3.4.0) corresponds to the third revision (R3) of the manuscript.</p> <p> </p>
Appendix 1. The papers included in the scoping review: Exploring the Applications of QR Codes in STEM Subjects
<p>This is a list of the papers included in the scoping review: <strong>Exploring the Applications of QR Codes in STEM Subjects.</strong></p> <p><strong>Published paper:</strong></p> <p>Tsoukala, E., Lefkos, I., Fachantidis, N. (2024). Exploring the Applications of QR Codes in STEM Subjects. In: Auer, M.E., Tsiatsos, T. (eds) Smart Mobile Communication & Artificial Intelligence. IMCL 2023. Lecture Notes in Networks and Systems, vol 936. Springer, Cham. <a href="https://doi.org/10.1007/978-3-031-54327-2_13">https://doi.org/10.1007/978-3-031-54327-2_13</a></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>
Replication Package for the Paper: "An Empirical Analysis of the Manual Detection of Code Smells via Code Review"
<p>This repository contains the data and results from the paper "An Empirical Analysis of the Manual Detection of Code Smells via Code Review" submitted to SANER 2021.</p> <p> </p> <p><strong>1. "data.zip" file contains the following three folders: </strong></p> <p> </p> <p><strong>1). data folder</strong></p> <p>The data folder contains the retrieved 1,174 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2). scripts folder</strong></p> <p>The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>keywords.txt</em> contains the keywords keywords asociated with code smells, such as "smell, duplication, and dead".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li>keywords_improve.py is used for improving the keyword-based mining approach.</li> <li>tools.py is used for supporting the process of keywords improving.</li> </ul> <p> </p> <p><strong>3). project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <ul> <li><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</li> <li><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</li> </ul> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords asociated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Dataset - Assessing the Students' Understanding and their Mistakes in Code Review Checklists -An Experience Report of 1,791 Code Review Checklists Questions from 394 Students
<p>This is the assignment specification and artifacts related to the paper "Assessing the Students' Understanding and their Mistakes in Code Review Checklists -An Experience Report of 1,791 Code Review Checklists Questions from 394 Students", ICSE-JSEET 2021.</p>
Review4Repair: Code Review Aided Automatic Program Repairing
<p>The natural language instructions scripted on the review comments are enormous sources of information about code bug’s nature and expected solutions. In this study, we investigate the performance improvement of repair techniques using code review comments. We train a sequence-to-sequence model on 55,060 code reviews and associated code changes. We also introduce new tokenization and preprocessing approaches that help to achieve significant improvement over state-of-the-art learning-based repair techniques. We boost the top-1 accuracy by 20.33% and top-10 accuracy by 34.82%. We could provide a suggestion for stylistics and non-code errors unaddressed by prior techniques.</p>
Replication Package for the Paper: "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community"
<p>This repository contains the data and results from the paper "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community" submitted to ICPC 2021.</p> <p> </p> <p><strong>1. "data.zip" contains the following three folders:</strong></p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the retrieved 1,190 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2) scripts folder</strong></p> <p>The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms.</p> <ul> <li> <p><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</p> </li> <li> <p><em>get_changes.py</em> is used for getting code changes from OpenStack.</p> </li> <li> <p><em>get_comments.py</em> is used for getting review comments for each code change.</p> </li> <li> <p><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</p> </li> <li> <p><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</p> </li> <li> <p><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</p> </li> <li> <p><em>tools.py</em> is used for supporting the process of keywords improving.</p> </li> </ul> <p> </p> <p><strong>3) project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at <a href="https://www.maxqda.com/">https://www.maxqda.com/</a> for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at <a href="https://www.maxqda.com/trial">https://www.maxqda.com/trial</a> for download.</p> <ul> <li> <p><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</p> </li> <li> <p><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</p> </li> </ul> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords associated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Improving Automated Code Reviews: Learning From Experience
<p>This is the replication package for "Improving Automated Code Reviews: Learning From Experience"</p> <p>Included contents:</p> <p>- requirements.txt</p> <p>- Mining Scripts</p> <p>- Datasets</p> <p>- Generated Reviews</p> <p>- Manual Analysis Pack</p> <p>- Checkpoints</p> <p> </p> <p>To access CodeReviewer please visit this link:<br>https://github.com/microsoft/CodeBERT/tree/master/CodeReviewer</p> <p>To fine-tune and evaluate the review comment generation model refer to:</p> <p>code/sh/finetune-msg.sh</p> <p>code/sh/test-msg.sh</p> <p> </p> <p> </p>
Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs
<p>The replication package for the paper "<span>Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs</span>".</p> <p>Accountability is an innate part of social systems. It maintains stability and ensures positive pressure on individuals' decision-making. As actors in a social system, software developers are accountable to their team and organization for their decisions. However, the drivers of accountability and how it changes behavior in software development are less understood. In this study, we look at how the social process of code review affects software engineers' sense of accountability for code quality. Since code review is increasingly involving LLM assistance, we also evaluate the impact on accountability when introducing LLM-assisted code reviews. We carried out a two-phased sequential qualitative study ($\textbf{interviews} \rightarrow \textbf{focus groups}$). In Phase I (16 interviews), we sought to investigate the intrinsic drivers of software engineers influencing their sense of accountability for code quality, relying on self-reported claims. In Phase II, we tested these traits in a more natural setting by simulating traditional peer-led reviews with focus groups and then LLM-assisted review sessions. We found that there are four key intrinsic drivers of accountability for code quality: personal standards, professional integrity, pride in code quality, and maintaining one's reputation. In a traditional peer-led review, we observed a transition from \emph{individual} to \emph{collective accountability} when code reviews are initiated. We also found that the introduction of LLM-assisted reviews disrupts this accountability process, challenging the reciprocity of accountability taking place in peer-led evaluations, i.e., one cannot be accountable to an LLM. Our findings imply that the introduction of AI into SE must preserve social integrity and collective accountability mechanisms.</p>
Code repository that supports the research presented in the paper "The gender gap in higher STEM studies: a Systematic Literature Review"
<p>Resources for the Systematic Literature Review (SLR) carries out as part of PhD thesis about the gender gap in STEM studies in higher education by Sonia Verdugo-Castro and supervised by Alicia García-Holgado and Mª Cruz Sánchez Gómez.</p> <p>The SLR covers papers in WoS and Scopus from 2015 to 2021.</p> <p>All the papers retrieved and the different steps in the SLR selection process are contained and documented in:</p> <ul> <li><a href="https://docs.google.com/spreadsheets/d/1ldml-Mg-oguX9gXayllojBRtZ1kjdD4WRSqYwf1yZ0o/edit?usp=sharing">https://docs.google.com/spreadsheets/d/1ldml-Mg-oguX9gXayllojBRtZ1kjdD4WRSqYwf1yZ0o/edit?usp=sharing</a></li> </ul>
Data and Material for 'Interpersonal Conflicts During Code Review'
<p>Data and Material supporting the paper 'Interpersonal Conflicts During Code Review: Developers’ Experience and Practices'</p>
Replication Package for the Paper: "Understanding Code Snippets in Code Reviews: A Preliminary Study of the OpenStack Community"
<p>This is the replication package for the paper: "Understanding Code Snippets in Code Reviews: A Preliminary Study of the OpenStack Community", including dataset and so on (see the description below) : </p> <ul> <li> <p><strong>Data of Code Snippets in Code Review.xlsx</strong> is the dataset of our paper, which contains 10,790 review comments collected from the Nova project and Neutron project of OpenStack community. Among all the review comments, 626 review comments contain code snippets. For the rows of review comments with code snippets, we filled them with blue color as an indicator.</p> </li> <li> <p><strong>Examples for Each Purpose.xlsx</strong> contains six review comment examples for the six detailed purposes mentioned in our paper (see Section 4.2).</p> </li> <li> <p><strong>README.md</strong></p> </li> </ul>
Replication Package for the Paper: "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities"
<p>This repository contains the data and results from the paper "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities" submitted to the ICPC 2021 special issue of the Empirical Software Engineering Journal, 2021.</p> <p> </p> <p>The replication package contains the following two folders:</p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the following four folders, which is organized by research questions (RQs).</p> <ul> <li>RQ1: The RQ1 folder contains the retrieved 1,539 code reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</li> <li>RQ2: The RQ2 folder contains the coded data for RQ2, called <em>Data Labeling & Encoding for RQ2.mx18</em>. It is the results of data labeling and encoding for RQ2, which was analyzed by the MAXQDA tool.</li> <li>RQ3 and RQ5: <ul> <li><em>Extracted data for RQ3.1.xlsx</em>: this file contains the extracted data (i.e., specific refactoring actions suggested by reviewers) for RQ3.1.</li> <li><em>Data Labeling & Encoding for RQ3 and RQ5.mx18</em>: this file contains the extracted data for RQ3 (excluding the specific refactoring actions in RQ3.1) and RQ5.</li> <li><em>Code change status for RQ5.xlsx</em>: this file contains the information of status of code changes where the developers disagreed with the reviewers and chose to ignore the identified code smells.</li> </ul> </li> <li>RQ4: The RQ4 folder contains the extracted data for RQ4, called <em>Extracted data for RQ4.xlsx</em>.</li> </ul> <p>Note: The mx18 files can be opened by MAXQDA 18 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <p> </p> <p><strong>2) scripts folder</strong></p> <p>The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack and Qt.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</li> <li><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</li> <li><em>tools.py</em> is used for supporting the process of keywords improving.</li> </ul>
Dataset and Screening code for Reviewing Water Conflicts in a Changing Climate
<p>A scoping review was performed to quantify concepts covered in the scientific literature relating to water conflict in a changing climate. The uploaded files consists of:</p> <ul> <li>A list of all studies found using the search terms ("Included studies and categories")</li> <li>All meta categories and concept categories, with all search strings used, ("Included studies and categories") and </li> <li>The MATLAB code used to screen and remove irrelevant studies after the queries. ("Screening_review")</li> <li>An example Excel sheet on how to structure papers for screening using the MATLAB code. ("Example_screening")</li> </ul> <p>All searches were using Scopus and performed 9 Jan 2024. Search terms used were:</p> <ol> <li>(“climate change” OR “global warming” OR “environmental change”) AND (conflict OR dispute) AND (water OR hydrology)</li> <li>(conflict OR dispute) AND hydroclimat*</li> <li>(“water conflict” OR “water dispute”) AND (precipitation OR temperature OR drought)</li> <li>(conflict OR dispute) AND “climate change”</li> <li>hydropolitic*</li> <li>“water conflict” OR “water dispute”</li> </ol> <p>The final publication selection for the scoping review was made based on four criteria, all of which needed to be fulfilled for each selected study:<br>i) the study is considering water-related conflicts (conflicts directly or indirectly related to freshwater aspects, such as water scarcity or lack of access to water); <br>ii) the study considers the water-related conflict(s) in a climate change context (directly or indirectly related to climate change, including direct impact of climate change adaptation measures); <br>iii) the study addresses factors relating to a previous or ongoing act of conflict (violent or non-violent) and not just potential conflicts or conflict mitigation measures, for example for future water resource decline. <br>Relevance based on these criteria was first evaluated by reading the abstracts and the full text was examined only when further clarification on the relevance of a study was needed.</p> <p>For our analysis, we created categories that bring together several similar words around the same broader concepts. For example, the category ‘governance’ includes the words policy, politic\w*, polici\w*, democra\w*, govern\w*, supreme court, minist\w*, and federal (for all concept categories, see the uploaded file). The different categories were further grouped together into category groups: political, livelihoods and industry, climate change, conflict, cooperation, and scale.</p> <p>Results will be found in the open-source article after publication and then the DOI and reference to that article will be added here.</p>
(supplementary material) Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation
<div> <div> <div> <div>Supplementary material for paper <strong>"Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation"</strong></div> <div> </div> <div> <div> <div>The script for the paper can be found in this GitHub repository: https://github.com/awsm-research/LLM-for-code-review-automatiton</div> </div> </div> </div> </div> </div>
The role of non-native plant species in modulating riverbank erosion: a systematic review - extracted coded articles dataset
<p>Data extraction sheet of code typologies for the review titled: The role of non-native plant species in modulating riverbank erosion: a systematic review. The uploaded csv file contains all the extracted values for each typology. </p>
Dataset and R code for 'Do Morphometric Data Improve Phylogenetic Reconstruction? A Systematic Review and Assessment'
<p>Dataset of tree (.tre) files and R code for running generalized Robinson-Foulds distance (Smith, 2020a;b) analysis. </p> <p>The .tre files can be read into R (R Core Team., 2023) using the ape::read.tree function (Paradis et al., 2003), full details in R code file. </p> <p> </p> <p> </p> <p>Paradis, E., Claude, J., & Strimmer, K. (2004). APE: analyses of phylogenetics and evolution in R language. Bioinformatics, 20(2), 289-290. </p> <p>R Core Team. (2023). R: A Language and Environment for Statistical Computing. (Version 4.2.2). R Foundation for Statistical Computing, Vienna, Austria: https://www.R-project.org/. </p> <p>Smith, M. R. (2020a). Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees. Bioinformatics, 36(20), 5007-5013. https://doi.org/10.1093/bioinformatics/btaa614 </p> <p>Smith, M. R. (2020b). TreeDist: distances between phylogenetic trees. R package version 2.7.0. doi:10.5281/zenodo.3528124. </p>
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