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62 results for “code smells”
A dataset of code smells
<p>This dataset contains code smells (implementation, design, and architecture smells) mined from 3073 open-source C# projects.</p>
Replication package with data used in the study: "The effect of code smells on the relationship between design patterns and defects. An empirical study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells on the relationship between design patterns and defects. An empirical study"</p> <p>This dataset contains the following folders:</p> <ul> <li> <p>"Analyzed systems" folder:</p> <ul> <li> <p>For each subject system (Ant-1.7, JEdit-4.2, Lucene-2.4, Camel-1.6, Log4j-1.2, Xalan-2.7, Poi-3.0, Ivy-2.0, Xerces-2.0, Velocity-1.6), we identify the following datasets: SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</p> </li> <li> <p>Those csv files include raw data about every class in every release. Each file includes columns which represent:</p> <ul> <li> <p>System: The analyzed system</p> </li> <li> <p>className: A fully qualified class name</p> </li> <li> <p>Pattern: if the class is part of any pattern the cell contains the name of the pattern, and “null” otherwise</p> </li> <li> <p>Smell: if the class is part of affected by any smell the cell contains the name of the smell, and “null” otherwise</p> </li> <li> <p>Bugs: Number of defects reported inside the class (extracted from the PROMISE dataset)</p> </li> </ul> </li> </ul> </li> <li> <p>A "detailed analysis" folder:</p> <ul> <li> <p>For each pattern, we report all the classes which participate in it in all the analyzed systems. The csv files inside this folder follow the same structure as the other csv files reported above</p> </li> </ul> </li> </ul> <p><br> </p>
Replication Package for the Paper: "Code Smells Detection via Code Review: An Empirical Study"
<p>This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020.</p> <p> </p> <p><strong>1. data folder</strong></p> <p>The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2. scripts floder</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>smell-term/general_smell_terms.txt</em> contains general code smell terms, such as "code smell".</li> <li><em>smell-term/specific_smell_terms.txt</em> contains specific code smell terms, such as "dead code".</li> <li><em>smell-term/misspelling_terms_of_smell.txt</em> contains the misspelling terms of 'smell', such as "ssell".</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>smell_search.py</em> is used for searching review comments that contain code smell terms.</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>
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
Can personality types be blamed for code smells
Open the record for dataset details and reuse information.
Detecting Code Smells in React-based Web Apps
<p>Detecting Code Smells in React-based Web Apps - Dataset</p>
Detecting Code Smells in React-based Web Apps
<p>Detecting Code Smells in React-based Web Apps - Dataset</p>
Sistematic review on the code smell effect (2000-2017)
<p>Dataset of the paper "A systematic review on the code smell effect".</p>
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
Detecting Code Smells in React-based Web Systems
<p>Dataset - Detecting Code Smells in React-based Web Systems</p>
Detecting Code Smells in React-based Web Systems
<p>Detecting Code Smells in React-based Web Systems</p>
Study of the impacts of Code Smells on code Testability
<p>Data used for the Thesis : Study of the impacts of Code Smells on code Testability</p>
Supplemental Material for Predictive Reranking using Code Smells for Information Retrieval Fault Localization
<pre># Predictive Reranking using Code Smells for Information Retrieval Fault Localization This repository constitutes the supplementary material, data, and source code for the paper "Predictive Reranking using Code Smells for Information Retrieval Fault Localization", by Thomas Hirsch and Birgit Hofer, 2023. Source code and results are also made available on GitHub: https://github.com/AmadeusBugProject/PredictiveRerankingUsingCodeSmellsForIRFL </pre> <pre>## Preliminaries ### Python environment - python=3.8 - pandas - numpy - joblib - scikit-learn==1.0.2 - keras - tensorflow - nltk - sentence-transformers - matplotlib - seaborn Conda files are located in the root directory of the repository, [conda_from_history.yml](<em>conda_from_history.yml</em>). ### Datasets The [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) dataset was used in our evaluation. All data necessary for our machine learning and localization experiments is included in this repository. However, if the data is to be re-imported and recalculated directly from Bench4BL: Bench4BL has to be downloaded and paths to the benchmark root set accordingly in [paths.py](<em>paths.py</em>). BugLocator, BRTracer, and BLIA have to be run on the Bench4BL dataset using the scripting provided by the benchmark. PMD has to be installed in version 6.45.0 and path to PMD set accordingly in [paths.py](<em>paths.py</em>). # Structure of this repository ## General utility functions - [constants.py](<em>constants.py</em>) Contains parameters for the NN model, and other parameters. - [paths.py](<em>paths.py</em>) Contains paths to external datasources and tools, e.g. Bench4BL and PMD. - [utils/bench4bl_utils.py](<em>utils/bench4bl_utils.py</em>) Helper functions for resolving paths to Bench4BL benchmark. - [utils/dataset_utils.py](<em>utils/dataset_utils.py</em>) Helper functions for loading datasets and performing dataset splits. - [utils/Logger.py](<em>utils/Logger.py</em>) Logging. - [utils/nn_classifier.py](<em>utils/nn_classifier.py</em>) NN classifier model. - [utils/scoring_utils.py](<em>utils/scoring_utils.py</em>) Metrics. - [utils/stats_utils.py](<em>utils/stats_utils.py</em>) Wrapper methods for statistical tests. ## Experiment ### Dataset setup and preparation The following scripts are responsible to create, import, and set up data that is used in our experiments. The produced data is already part of this repository, the execution of these scripts is therefore only necessary when data is to be re-imported from the Bench4BL repository. - [a00_pmd_bench4bl.py](<em>a00_pmd_bench4bl.py</em>) Runs PMD on all projects and versions contained in the Bench4BL dataset. The utilized ruleset is defined in [all_java_ruleset.xml](<em>all_java_ruleset.xml</em>). Results are stored in [pmd_results](<em>pmd_results</em>). - [a01_vectorize_pmd.py](<em>a01_vectorize_pmd.py</em>) Creates csv vectors from PMD outputs. - [a02_cloc_bench4bl.py](<em>a02_cloc_bench4bl.py</em>) Runs cloc on all projects and versions contained in the Bench4BL dataset. Only Java files are considered. Results are stored in [cloc_results](<em>cloc_results</em>). - [a02_pmd_usage_in_bench4bl_projects.py](<em>a02_pmd_usage_in_bench4bl_projects.py</em>) Searches for occurrence of PMD in the build files of all projects and versions contained in the Bench4BL dataset. Results are stored in [pmd_usage_in_bench4bl_projects](<em>pmd_usage_in_bench4bl_projects</em>). - [a03_import_bugs_from_bench4bl.py](<em>a03_import_bugs_from_bench4bl.py</em>) Imports textual bug reports and corresponding fixed files ground truth from Bench4BL. Results are stored in [bench4bl_summary](<em>bench4bl_summary</em>). - [a04_normalize_smells_by_loc.py](<em>a04_normalize_smells_by_loc.py</em>) Normalizes the smell vectors for each file with its LOC count. Results are stored in [pmd_results](<em>pmd_results</em>). - [a05_bench4bl_file_features.py](<em>a05_bench4bl_file_features.py</em>) Creates feature vectors for each bug report from PMD smells. Results are stored in [bug_smell_vectors](<em>bug_smell_vectors</em>). - [a05_bench4bl_ranking_results.py](<em>a05_bench4bl_ranking_results.py</em>) Imports the results of BugLocator, BRTracer, and BLIA from the Bench4BL benchmark. Results are stored in [bench4bl_localization_results](<em>bench4bl_localization_results</em>). - [a09_bench4bl_stackoverflow_mpnet.py](<em>a09_bench4bl_stackoverflow_mpnet.py</em>) Creates document embeddings for all textual bug reports using the [stackoverflow_mpnet-base](<em>https://huggingface.co/flax-sentence-embeddings/stackoverflow_mpnet-base</em>) model. Results are stored in [stackoverflow_mpnet_embeddings](<em>stackoverflow_mpnet_embeddings</em>). ### Preliminary experiments and dataset splitting The following scripts perform data set splitting, and the preliminary experiments used for feature selection as discussed in Section VI of the paper. - [b00_analyze_most_promising_smells.py](<em>b00_analyze_most_promising_smells.py</em>) Assumes a perfect smell oracle (by using the known smells of the ground truth files) and applies it to rerank the IRFL tools outputs on the Classification Training Set (the older half of versions in the dataset). Then evaluates the achievable localization performance increase for each smell group. Results are stored in [h_analyze_most_promising_smells](<em>h_analyze_most_promising_smells</em>). - [c00_make_dataset_splits_bootstrap.py](<em>c00_make_dataset_splits_bootstrap.py</em>) Performs dataset splitting. Splits are performed on a temporal ordering of versions of each contained software project. Data is greedily split into 50/25/25, resulting in a Classification training set (used for NN model training), a ranking training set (used to estimate weights for linear combination of smell distance and IRFL suspicousness scores), and a test set (used for evaluating the localization performance achievable by our pipeline). Bootstrapping is applied by resampling fractions of 0.8, 20 times, resulting in 20 sets of the three splits to be used in the following eperiments. Datasets are stored in [p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap](<em>p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap</em>) for the full Bench4BL dataset, for the single project experiments please refer to p_FINAL_CAMEL, p_FINAL_HBASE, and p_FINAL_ROO accordingly. - [c01_full_dataset_stats.py](<em>c01_full_dataset_stats.py</em>) Calculates various statistics on the created datasets. Results are stored in [px_summary_dataset](<em>px_summary_dataset</em>). - [d00_model_for_smell_classification_performance_all_groups_bootstrap.py](<em>d00_model_for_smell_classification_performance_all_groups_bootstrap.py</em>) Trains a NN model on the Classification data set and evaluates its classification performance on the Ranking training set. This is performed for all 20 bootstrap iterations, the resulting data is stored in [p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap](<em>p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap</em>) for the full Bench4BL dataset, for the single project experiments please refer to p_FINAL_CAMEL, p_FINAL_HBASE, and p_FINAL_ROO accordingly. - [d01_classification_performance_evaluation_for_feature_selection_preliminariy_all_smell_groups.py](<em>d01_classification_performance_evaluation_for_feature_selection_preliminariy_all_smell_groups.py</em>) Creates summary and bootstrap statistics from the previous step. Results are stored in [p_FINAL_Bench4BL/p_summary_classification](<em>p_FINAL_Bench4BL/p_summary_classification</em>) for the full Bench4BL dataset. ### Localization experiments The following scripts perform our actual localization experiments. These scripts are applied to bootstrapped dataset splits. Results are stored in [p_FINAL_Bench4BL](<em>p_FINAL_Bench4BL</em>) for the full Bench4BL dataset, for the single project experiments please refer to p_FINAL_CAMEL, p_FINAL_HBASE, and p_FINAL_ROO accordingly. For a detailed experiment setup we refer to our paper. - [e01_model_for_localization_bootstrap.py](<em>e01_model_for_localization_bootstrap.py</em>) Trains NN models for smell classification and performs predictions on the corresponding test sets. Results are stored in [p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap](<em>p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap</em>). - [e02_ranking_training_bootstrap.py](<em>e02_ranking_training_bootstrap.py</em>) Performs reranking of the Ranking training set based on predicted smells by the model created in the previous step. Score combination is performed by linear combination of IRFL tools suspicousness scores and smell distances calculated based on our predictions. Results are stored in [p_FINAL_Bench4BL/p_ranking_training_bootstrap](<em>p_FINAL_Bench4BL/p_ranking_training_bootstrap</em>). - [e03_get_best_weigths_per_project_bootstrap.py](<em>e03_get_best_weigths_per_project_bootstrap.py</em>) Evaluates the outputs of the previous steps in order to pick the best weights for each project and IRFL tool. Results are stored in [p_FINAL_Bench4BL/p_ranking_training_bootstrap](<em>p_FINAL_Bench4BL/p_ranking_training_bootstrap</em>). - [e04_ranking_test_project_wise_bootstrap.py](<em>e04_ranking_test_project_wise_bootstrap.py</em>) Uses predictions of the final NN model and the weights obtained from the previous step to perform rerankings on the Test set. Results are stored in [p_FINAL_Bench4BL/p_ranking_test_proejct_wise_bootstrap](<em>p_FINAL_Bench4BL/p_ranking_test_proejct_wise_bootstrap</em>). ### Result collection and evaluation The following scripts calculate final scores and statistics from the 20 bootstrap iterations of the previous block of scripts. - [f00_bootstrap_summary_compare_map_and_ttest.py](<em>f00_bootstrap_summary_compare_map_and_ttest.py</em>) Calculates localization performance using the MAP metric and performs statistical tests. Results are stored in [p_FINAL_Bench4BL/p_summary_bootstrap](<em>p_FINAL_Bench4BL/p_summary_bootstrap</em>). - [f01_boostrap_summary_compare_classification_performance.py](<em>f01_boostrap_summary_compare_classification_performance.py</em>) Calculates classifier performance of our final model. Results are stored in [p_FINAL_Bench4BL/p_summary_bootstrap](<em>p_FINAL_Bench4BL/p_summary_bootstrap</em>). - [f02_bootstrap_summary_model_classification_performance_eval_test_set_for_all_projects.py](<em>f02_bootstrap_summary_model_classification_performance_eval_test_set_for_all_projects.py</em>) Calculates classifier performance and project wise classifier performance of our final model. Results are stored in [p_FINAL_Bench4BL/p_summary_classification_test_set](<em>p_FINAL_Bench4BL/p_summary_classification_test_set</em>). ### Further analysis The following scripts collect statistics and results to create latex tables and additional analysis used in our paper. - [g00_project_wise_perf_stats.py](<em>g00_project_wise_perf_stats.py</em>) Creates overview latex table comparing the Bench4BL and single project trained pipelines. Results are stored in [px_summary_performance](<em>px_summary_performance</em>). - [g01_project_multiple_file_smell_distances.py](<em>g01_project_multiple_file_smell_distances.py</em>) Analyses smell distances within each bug's ground truth files. Results are stored in [px_summary_dataset](<em>px_summary_dataset</em>). - [g02_performance_correlation_analysis.py](<em>g02_performance_correlation_analysis.py</em>) Performs correlation analysis of our pipeline's MAP localization performance, classification performance, and smell distance measures from previous step. Results are stored in [p_FINAL_Bench4BL/p_summary_correlations](<em>p_FINAL_Bench4BL/p_summary_correlations</em>) for the full Bench4BL dataset. ## Results - [pmd_catalogue/all_smells.json](<em>pmd_catalogue/all_smells.json</em>) lists all PMD smells and associated groups that occur in the dataset. - [px_summary_dataset](<em>px_summary_dataset</em>) contains statistics and information about the utilized dataset. Results for preliminary experiments for feature selection: - [h_analyze_most_promising_smells](<em>h_analyze_most_promising_smells</em>) contains the results of our preliminary experiment into each smell group's information content towards localization. - [p_FINAL_Bench4BL/p_summary_classification](<em>p_FINAL_Bench4BL/p_summary_classification</em>) contains the results of our preliminary experiments into the classifiability of smell groups from textual bug reports. Results of our localization eperiments: - [p_FINAL_Bench4BL/p_summary_bootstrap](<em>p_FINAL_Bench4BL/p_summary_bootstrap</em>) contains the results of our localization experiments, project wise MAP performance summary can be found in [p_FINAL_Bench4BL/p_summary_bootstrap/project_scores_tool_wise_others.tex](<em>p_FINAL_Bench4BL/p_summary_bootstrap/project_scores_tool_wise_others.tex</em>). - [p_FINAL_Bench4BL/p_summary_classification_test_set](<em>p_FINAL_Bench4BL/p_summary_classification_test_set</em>) contains the results of our classification performance analysis of the predictions used in localization. A project wise classification performance summary can be found in [p_FINAL_Bench4BL/p_summary_classification_test_set/macro_average_classification_performances_per_projectother.tex](<em>p_FINAL_Bench4BL/p_summary_classification_test_set/macro_average_classification_performances_per_projectother.tex</em>) ## Licence All code and results are licensed under [AGPL v3](<em>https://www.gnu.org/licenses/agpl-3.0.html.en</em>), according to LICENSE file. Other licences may apply for some tools and datasets contained in this repo: [cloc-1.92.pl](<em>https://github.com/AlDanial/cloc</em>) under [GPL v2](<em>https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html</em>), and data originating from [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) under [CCA 4.0](<em>https://creativecommons.org/licenses/by/4.0/</em>). </pre>
Empirical Study of the Relationship between Design Patterns and Code Smells
<p>Software systems are often developed in such a way that good practices in the object-oriented paradigm are not met, causing the occurrence of specific disharmonies, which are sometimes called code smells. Design patterns catalogue best practices for developing object-oriented software systems. Although code smells and design patterns are widely divergent, there might be a co-occurrence relation between them. The objective of this paper is to empirically evaluate if the presence of design patterns is related to the presence of code smells at different granularity levels. We performed an empirical replication study using 20 design patterns, and 13 code smells in ten small-size to medium-size, open-source Java-based systems. We applied statistical analysis and association rules. Results confirm that classes participating in design patterns have less smell-proneness and smell frequency than classes not participating in design patterns. We also noticed that every design pattern category act in the same way in terms of smell-proneness in the subject systems. However, we observed, based on the association rules learning and the proposed validation technique, that some patterns may be associated with certain smells in some cases. For instance, Command patterns can co-occur with God Class, Blob and External Duplication smell.</p> <p><strong>The published data set contains the following:</strong></p> <ol> <li>List of the selected systems (source code files)</li> <li>The P-MARt: the design pattern repository as XML for the selected systems. </li> <li>Data of design patterns and code smells: We processed this data by parsing the design pattern XML file and running the smell detection tool (inFusion).</li> <li>The data of the data mining analysis.</li> </ol>
On The Relation of Test Smells to Software Code Quality. Appendix.
<p>Dataset of "On The Relation of Test Smells to Software Code Quality"</p> <p>This is the replication package of the paper "On The Relation of Test Smells to Software Code Quality", accepted at the 34th IEEE International Conference on Software Maintenance and Evolution (ICSME 18).</p>
Democracia em Xeque: Um Estudo Comparativo sobre Detecção de Code Smells [DATASET]
<p>Dataset usado na pesquisa</p>
Detecting Multiple Code Smells Simultaneously based on Heterogeneous Graph
Open the record for dataset details and reuse information.
Replication Package for the Paper: "Code Smells Detection via Code Review: An Empirical Study"
<p>This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020.</p> <p> </p> <p><strong>1. data folder</strong></p> <p>The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2. scripts floder</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>smell-term/general_smell_terms.txt</em> contains general code smell terms, such as "code smell".</li> <li><em>smell-term/specific_smell_terms.txt</em> contains specific code smell terms, such as "dead code".</li> <li><em>smell-term/misspelling_terms_of_smell.txt</em> contains the misspelling terms of 'smell', such as "ssell".</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>smell_search.py</em> is used for searching review comments that contain code smell terms.</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>
Replication Package for the Paper: "Code Smells Detection via Code Review: An Empirical Study"
<p>This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020.</p> <p> </p> <p><strong>1. data folder</strong></p> <p>The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2. scripts floder</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>smell-term/general_smell_terms.txt</em> contains general code smell terms, such as "code smell".</li> <li><em>smell-term/specific_smell_terms.txt</em> contains specific code smell terms, such as "dead code".</li> <li><em>smell-term/misspelling_terms_of_smell.txt</em> contains the misspelling terms of 'smell', such as "ssell".</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>smell_search.py</em> is used for searching review comments that contain code smell terms.</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>
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" 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 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.</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>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> <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 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>
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