A Classification of Software Defect Prediction Models

Pal, Sourabh; Sillitti, Alberto

doi:10.1109/nir52917.2021.9666110

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…Over time, numerous methods have been put forth and used in software development, assisting practitioners in allocating limited testing resources to modules that frequently exhibit defects. Early studies concentrated on within-project defect prediction (WPDP), which learned the SDP model from previous data from the same project and then used it to predict defects in the releases that were coming soon [9][10][11]. The SDP model's preliminary research indicates that, if there are adequate sample data from the same project, learning-derived prediction performance will be effective in the same project.…”

Section: Introductionmentioning

confidence: 99%

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Javed,

Shengbing,

Asim

et al. 2024

Algorithms

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Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1- measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1- measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance.

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Section: Introductionmentioning

confidence: 99%

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Javed,

Shengbing,

Asim

et al. 2024

Algorithms

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“…In our previous work, we classified the defect prediction models into 10 categories [8]. Most of the papers identified focus on within-project software defect prediction (SDP) models.…”

Section: Introductionmentioning

confidence: 99%

Cross-Project Defect Prediction: A Literature Review

Pal

Sillitti²

2022

IEEE Access

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Background: Software defect prediction models aim at identifying the potential faulty modules of a software project based on historical data collected from previous versions of the same project. Due to the lack of availability of software engineering data from the same project, the researchers proposed crossproject defect prediction (CPDP) models where the data collected from one or more projects are used to predict faults in other project. There are a number of approaches proposed with different levels of success and very limited repeatability. Goals: The purpose of this paper is to investigate the existing studies of cross-project models for defect prediction. It synthesizes the literature focusing on characteristics such as project type, software metrics, data preprocessing techniques, features selection approaches, classifiers, and performance measures used. Method: This paper follows the well-known Systematic Literature Review (SLR) approach proposed by Barbara Kitchenham in 2007. Results: Our finding shows that most of the article was published between 2015 and 2021. Moreover, the studies are mostly based on open-source datasets and the software metrics used to create the models are mainly product metrics. We also found out that most studies attempted to improve their models improving data preprocessing and feature selection approaches. Furthermore, logistic regression followed by naive bayes and random forest are the most adopted classifier techniques in such models. Finally, the f-measure followed by recall and AUC are the most preferred evaluation measure used to evaluate the performance of the models. Conclusions: This study provides an overview of the different approaches used to improve the CPDP models analyzing the different techniques used for data preprocessing, feature selection, and the selection of the classifiers. Moreover, we identified some aspects that need further investigation.

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“…To improve software testing effectiveness, an AI-based SDP named Defect Prediction as a Service was created, along with six best defect prediction models (20) . Eleven software defect prediction models are identified and compared to improvise the quality and security in software testing which provides accuracy of 80% (21) .…”

Section: Introductionmentioning

confidence: 99%

Impact of Unbalanced Classification on the Performance of Software Defect Prediction Models

Eldho¹

2022

IJST

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Objectives:To propose a suitable imbalanced data classification model to split the dataset into two new datasets and to test the created imbalanced dataset by the prediction models. Methods: The imbalance defect data sets are taken from the PROMISE library and used for the performance evaluation. The results clearly demonstrate that the performance of three existing prediction classifier models, K-Nearest Neighbor (KNN), Naive Bayes (NB), and Back Propagation (BPN), is very susceptible in terms of unbalance of classification, while Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are more stable. Findings: The outcome of this research reveals that applied SVM and ELM machine learning models improves the performance in defect prediction and records 29% more than KNN, and 19% more than NB and BPN. Novelty: According to the findings of a comprehensive study, the proposed machine learning new classification imbalance impact analysis method outperforms the existing ones in order to transform the original imbalance data set into a new data set with an increasing imbalance rate and be able to select models to evaluate different predictions on the new data set.

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A Classification of Software Defect Prediction Models

Cited by 6 publications

References 43 publications

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Cross-Project Defect Prediction: A Literature Review

Impact of Unbalanced Classification on the Performance of Software Defect Prediction Models

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