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2 results for “Smell-based Defect Prediction”
Extremely Imbalanced Smell-based Defect Prediction
<p><strong>Abstract: </strong>In continuous integration/continuous delivery, one of the main requirements for high-speed delivery of software is to find bugs efficiently. For this reason, multiple solutions were introduced in the literature. For instance, defect prediction approaches based on bad code smells detected in modules from each version of the software. Nevertheless, these approaches do not consider the problem where there may exist an extremely higher percentage of non-defective modules compared to defective modules. Given that, each version of the software may only have a small number of defects. As a result, in this thesis, we introduce a new model with an autoencoder algorithm that uses design and implementation smells to detect defective modules. Therefore, we trained five autoencoders with distinct architectures. Ad- ditionally, for evaluation, we compared each model against autoencoders with the same architecture, trained with traditional object-oriented metrics and the combination of both. Our analysis did not show promising results, as the use of only smells and the combination of features did not provide an improve- ment compared with the use of metrics. However, we introduce a starting point for smell-based defect prediction in the context of dataset imbalance. Furthermore, we introduce a baseline for future work.</p> <p> </p> <p><strong>Dataset Description:</strong></p> <p>We provide three datasets. The first results from the extraction of traditional object-oriented metrics (metric.csv). The second results from the extraction of design and implementation smells (smell.csv). The third is the combination of all the features (metricsmell.csv). Moreover, these features were extracted from Designite and Bugsdorjar software archives.</p>
Exploring Design Smells for Smell-Based Defect Prediction
<p>The archived file datasets.zip includes the datasets used for supporting the conclusions in the article <em>Exploring Design Smells for Smell-Based Defect Prediction.</em></p> <p>In this paper, we answer two research questions:</p> <p><strong>RQ1.</strong> Do Design code smells contribute to the performance of defect prediction models trained with Traditional code smells?</p> <p><strong>RQ2. </strong>How do the different categories of Design smells impact the performance of the defect prediction models?</p> <p>Therefore, after extracting the archived file documents, you will find two sub-directories, respectively named "RQ1" and "RQ2". They include the results obtained for each one of the research questions, thus supporting our conclusions.</p> <p>(You will also find a README.pdf file with these same instructions regarding the datasets.)</p> <p>Inside "RQ1," you will find two directories, respectively named "configuration_1" and "configuration_2". They represent the different configurations for the experiments. <strong>"configuration_1"</strong> contains the datasets with results for the ten classifiers configurations with the highest scores and <strong>"configuration_2" </strong>contains the datasets with the results classifier configuration with the overall best results - Support Vector Machine with C=0.1. Furthermore, within each directory, there are three sub-directories, respectively named "designite," "designite_traditional," and "traditional." These have the datasets for each of the considered smell sets in our study. Inside "RQ2," you will find four directories. Each corresponds to a category from the design smells for the dataset "designite_traditional." These datasets were build from the same configuration as "configuration_2".</p> <p>Then, within every directory, there are 97 sub-directories representing the 97 projects analyzed in this study.</p> <p>Every project folder follows the same structure, which we define as follows.</p> <ul> <li>The "dataset" directory contains the original training and testing dataset used.</li> <li>The "oversamples" directory contains the training dataset after oversampling for each of the feature selection approaches.</li> <li>The "score_summary" directory contains all classifier configurations considered, not only the 10 with the highest scores.</li> <li>The "scores.csv" file contains all the scores for the main classifier configurations studied in the particular experiment.</li> <li>The "selected_features" directory contains the selected features' information and the selected features dataset for each feature_selection method.</li> <li>The "selected_testing_X" directory contains the testing datasets.</li> <li>The "top_scores_summary" directory contains the classifier configurations and hyper-parameter scores for the top 10 highest scores.</li> </ul>
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