Software Defect Prediction Research Articles

RETRACTED: Hybridization of fuzzy rough feature selection with ANFIS and turbulent flow of water optimization for managing software defect prediction uncertainty

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Predicting the Number of Software Faults using Deep Learning

The software testing phase requires considerable time, effort, and cost, particularly when there are many faults. Thus, developers focus on the evolution of Software Fault Prediction (SFP) to predict faulty units in advance, therefore, improving software quality significantly. Forecasting the number of faults in software units can efficiently direct software testing efforts. Previous studies have employed several machine learning models to determine whether a software unit is faulty. In this study, a new, simple deep neural network approach that can adapt to the type of input data was designed, utilizing Convolutional Neural Networks (CNNs) and Multi-Layer Perceptron (MLP), to predict the number of software faults. Twelve open-source software project datasets from the PROMISE repository were used for testing and validation. As data imbalance can negatively impact prediction accuracy, the new version of synthetic minority over-sampling technique (SMOTEND) was used to resolve data imbalance. In experimental results, a lower error rate was obtained for MLP, compared to CNN, reaching 0.195, indicating the accuracy of this prediction model. The proposed approach proved to be effective when compared with two of the best machine learning models in the field of prediction. The code will be available on GitHub.

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.

Improving transfer learning for software cross-project defect prediction

AbstractSoftware cross-project defect prediction (CPDP) makes use of cross-project (CP) data to overcome the lack of data necessary to train well-performing software defect prediction (SDP) classifiers in the early stage of new software projects. Since the CP data (known as the source) may be different from the new project’s data (known as the target), this makes it difficult for CPDP classifiers to perform well. In particular, it is a mismatch of data distributions between source and target that creates this difficulty. Transfer learning-based CPDP classifiers are designed to minimize these distribution differences. The first Transfer learning-based CPDP classifiers treated these differences equally, thereby degrading prediction performance. To this end, recent research has the Weighted Balanced Distribution Adaptation (W-BDA) method to leverage the importance of both distribution differences to improve classification performance. Although W-BDA has been shown to improve model performance in CPDP and tackle the class imbalance by balancing the class proportion of each domain, research to date has failed to consider model performance in light of increasing target data. We provide the first investigation studying the effects of increasing the target data when leveraging the importance of both distribution differences. We extend the initial W-BDA method and call this extension the W-BDA$$\mathbf {^{+}}$$ + method. To evaluate the effectiveness of W-BDA$$\mathbf {^{+}}$$ + for improving CPDP performance, we conduct eight experiments on 18 projects from four datasets, where data sampling was performed with different sampling methods. Data sampling was only performed on the baseline methods and not on our proposed W-BDA$$\mathbf {^{+}}$$ + and the original W-BDA because data sampling issues do not exist for these two methods. We evaluate our method using four complementary indicators (i.e., Balanced Accuracy, AUC, F-measure and G-Measure). Our findings reveal an average improvement of 6%, 7.5%, 10% and 12% for these four indicators when W-BDA$$\mathbf {^{+}}$$ + is compared to the original W-BDA and five other baseline methods (for all four of the sampling methods used). Also, as the target to source ratio is increased with different sampling methods, we observe a decrease in performance for the original W-BDA, with our W-BDA$$\mathbf {^{+}}$$ + approach outperforming the original W-BDA in most cases. Our results highlight the importance of having an awareness of the effect of the increasing availability of target data in CPDP scenarios when using a method that can handle the class imbalance problem.

An optimized deep learning method for software defect prediction using Whale Optimization Algorithm

An optimized deep learning method for software defect prediction using Whale Optimization Algorithm

Enhancing Software Defect Prediction accuracy using Modified Entropy Calculation in Random Forest Algorithm

Imagine you are trying to classify software defect for a large dataset. How will you choose the best algorithm to do that? For the above problem we have various algorithms like Random Forest, Support Vector Machine, Neural Networks, Naive Bayes, K-Nearest Neighbours, Decision Tree, Logistic Regression etc. One of the most used methods is Random Forest algorithm, which uses multiple Decision Trees to make predictions. However, this algorithm relies on a complex calculation called Entropy, which measures the uncertainty in the data. Entropy is a function that uses natural logarithm which may be time consuming calculation. Is there a better way to calculate entropy? In this research, we have explored a different way to calculate the natural logarithm using the Taylor series expression. It is a series consisting of sum of infinite terms that approximates any function by using its derivatives. We further modified the Random Forest algorithm by replacing the natural logarithm with the Taylor series expression in the Entropy formula. We tested our modified algorithm on dataset and compared its performance with the original Entropy formula. We found that our modification in the algorithm has improved the accuracy of the algorithm on software defect prediction

Effect of Data Sampling on Cone Shaped Embedded Normalization in Just in Time Software Defect Prediction

Effect of Data Sampling on Cone Shaped Embedded Normalization in Just in Time Software Defect Prediction

Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction

Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction

A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron

PurposeSoftware defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.Design/methodology/approachThe integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.FindingsThe performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.Originality/valueExperimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

An empirical study of just-in-time-defect prediction using various machine learning techniques

The goal of defect specificity is to pinpoint defective program components (such as faulty files, problematic procedures, or troublesome lines of code) based on defect symptoms, such as event logs or program spectrum. However, the problem is revealed and adverse effects are introduced when one experiences the defect indications. Consequently, one difficult task is to determine whether one can find buggy program before the signs of the defect manifests itself. Just-in-time defect prediction (JIT-DP) is a particular category of Software Defect Prediction (SDP) that includes this type of early detection of flawed changes to a software programs. This paper presents a novel five-phase JIT-DP framework that classifies the newly committed change as either a buggy change or a clean change relying on the project's historical data regarding the executed changes. The distinctive aspect of the model is that it uses the Chunk Balancing Algorithm (CBA) as a novel way of handling the Class Imbalance Problem (CIP) rather than utilizing conventional data re-sampling techniques. The experimental study uses 10 open-source projects with an average of 23,989 modifications to train the JIT-DP model. The research study examines the JIT-DP model's prediction abilities from both Within Project (WP) and Cross Project (CP) scenarios. The findings show that for within project setup, Logistic Regression (LR) outperforms among all other classifiers with the highest accuracy, TPR, and FPR as 0.85, 0.847, and 0.207, respectively. The model provides comparable prediction findings with an average accuracy of 0.90 for all seven testing samples when it was trained using commit logs from related six Java projects under CP context. However, taking into account the larger training pool of commit records in the former scenario, the model in the CP context (3.740 s) lags behind in terms of training and classification time periods when compared to the results of WI (0.984 s).

Efficient Cross-Project Software Defect Prediction Based on Federated Meta-Learning

Software defect prediction is an important part of software development, which aims to use existing historical data to predict future software defects. Focusing on the model performance and communication efficiency of cross-project software defect prediction, this paper proposes an efficient communication-based federated meta-learning (ECFML) algorithm. The lightweight MobileViT network is used as the meta-learner of the Model Agnostic Meta-Learning (MAML) algorithm. By learning common knowledge on the local data of multiple clients, and then fine-tuning the model, the number of unnecessary iterations is reduced, and communication efficiency is improved while reducing the number of parameters. The gradient information model is encrypted using the differential privacy of the Laplace mechanism, and the optimal privacy budget is determined through experiments. Experiments on three public datasets (AEEEM, NASA, and Relink) verified the effectiveness of ECFML in terms of parameter quantity, convergence, and model performance of cross-project software defect prediction.

An empirically based object-oriented testing using Machine learning

INTRODUCTION: The rapid growth of machine learning has the potential to revolutionize various industries and applications by automating complex tasks and enhancing efficiency. Effective software testing is crucial for ensuring software quality and minimizing resource expenses in software engineering. Machine learning techniques play a vital role in software testing by aiding in test case prioritization, predicting software defects, and analyzing test results. OBJECTIVES: The primary objective of this study is to explore the use of machine learning algorithms for software defect prediction. METHODS: Machine Learning models including Random Forest Classifier, Logistic Regression, K Nearest Neighbors, Gradient Boosting Classifiers, Catboost Classifier, and Convolutional Neural Networks have been employed for the study. The dataset includes a wide range of features relevant to software defect prediction and evaluates the performance of different prediction models. The study also focussed on developing hybrid models using stacking classifiers, which combine multiple individual models to improve accuracy. RESULTS: The experimental results show that the hybrid models combining CatBoost and Convolutional Neural Network have outperformed individual models, achieving the highest accuracy of 89.5%, highlighting the effectiveness of combining machine learning algorithms for software defect prediction. CONCLUSION: In conclusion, this study sheds light on the pivotal role of machine learning in enhancing software defect prediction.

Neighbor cleaning learning based cost‐sensitive ensemble learning approach for software defect prediction

SummaryThe class imbalance problem in software defect prediction datasets leads to prediction results that are biased toward the majority class, and the class overlap problem leads to fuzzy boundaries for classification decisions, both of which affect the model's prediction performance on the dataset. A neighbor cleaning learning (NCL) is an effective technique for defect prediction. To solve the class overlap problem and class imbalance problem, the NCL‐based cost‐sensitive ensemble learning approach for software defect prediction (NCL_CSEL) model is proposed. First, the bootstrap resampled data are trained using the base classifier. Subsequently, multiple classifiers are integrated by a static ensemble to obtain the final classification results. As the base classifier, the Adaptive Boosting (AdaBoost) classifier combining NCL and cost‐sensitive learning is proposed, and the class overlap problem and class imbalance problem are solved by balancing the proportion of overlap sample removal in NCL and the size of the cost factor in cost‐sensitive learning. Specifically, the NCL algorithm is used to initialize the sample weights, while the cost‐sensitive method is employed to update the sample weights. Experiments based on the NASA dataset and AEEEM dataset show that the defect prediction model can improve the bal value by approximately 7% and the AUC value by 9.5% when the NCL algorithm is added. NCL_CSEL can effectively solve the class imbalance problem and significantly improve the prediction performance compared with existing methods for solving the class imbalance problem.

Application of Weighted Combinations of Activation Functions to Defect Prediction in Software Development

Software defect prediction (SDP) method aims to identify potential bugs in programs or defective modules in software projects. SDP method can greatly help developers allocate needed testing- and debugging-efforts. Presently, SDP is typically divided into two procedures: extracting features from source code and building a classification model using machine learning methods, such as support vector machine, decision tree, and neural networks, to build a classifier for defect prediction. However, there are still some limitations for SDP. For example, machine learning models require a fixed input size, but the size of each program is mostly inconsistent. Activation functions play an essential role in the training of artificial neural networks, but every kind of activation function has its own particular strengths and inherent constraints. The main purpose of this article is to propose a general framework of combining different activation functions with given weights to improve the effectiveness of SDP. We construct 41 kinds of defect prediction models by deep belief networks (DBNs) built with double-weighted or triple-weighted combination of six most commonly used activation functions to improve the predictions. In the experiment of this study, some real data from open-source projects are selected to evaluate the performance of our proposed weighted combination methods. It is found experimentally that the weighted combinations methods can enhance the accuracy of SDP. It is also noticed that our proposed weighted combination scheme is not restricted to the DBN or a particular kind of activation functions.

Ensemble Kernel-Mapping-Based Ranking Support Vector Machine for Software Defect Prediction

Ensemble Kernel-Mapping-Based Ranking Support Vector Machine for Software Defect Prediction

Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning

Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA’s defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data’s integrity. In the second stage, a feature selection technique based on the genetic algorithm is applied to identify the optimal subset of features. In the third stage, three heterogeneous binary classifiers, namely random forest, support vector machine, and naïve Bayes, are implemented as base classifiers. Through iterative tuning, the classifiers are optimized to achieve the highest level of accuracy individually. In the fourth stage, an ensemble machine-learning technique known as voting is applied as a master classifier, leveraging the collective decision-making power of the base classifiers. The final stage evaluates the performance of the proposed framework using five widely recognized performance evaluation measures: precision, recall, accuracy, F-measure, and area under the curve. Experimental results demonstrate that the proposed framework outperforms state-of-the-art ensemble and base classifiers employed in software defect prediction and achieves a maximum accuracy of 95.1%, showing its effectiveness in accurately identifying software defects. The framework also evaluates its efficiency by calculating execution times. Notably, it exhibits enhanced efficiency, significantly reducing the execution times during the training and testing phases by an average of 51.52% and 52.31%, respectively. This reduction contributes to a more computationally economical solution for accurate software defect prediction.

Alleviating Class Imbalance Issue in Software Fault Prediction Using DBSCAN-Based Induced Graph Under-Sampling Method

Alleviating Class Imbalance Issue in Software Fault Prediction Using DBSCAN-Based Induced Graph Under-Sampling Method

Hybrid deep architecture for software defect prediction with improved feature set

Hybrid deep architecture for software defect prediction with improved feature set

Interpretable Software Defect Prediction from Project Effort and Static Code Metrics

Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to trust the model outcomes. The objective of this research is to create software defect prediction models with a focus on interpretability. Additionally, it aims to investigate the impact of size, complexity, and other source code metrics on the prediction of software defects. This research also assesses the reliability of cross-project defect prediction. Well-known machine learning techniques, such as support vector machines, k-nearest neighbors, random forest classifiers, and artificial neural networks, were applied to publicly available PROMISE datasets. The interpretability of this approach was demonstrated by SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) techniques. The developed interpretable software defect prediction models showed reliability on independent and cross-project data. Finally, the results demonstrate that static code metrics can contribute to the defect prediction models, and the inclusion of explainability assists in establishing trust in the developed models.

Development of Honey Badger-Cat Swarm Optimisation-Based Parallel Cascaded Deep Network for Software Bug Prediction Framework

Software bug prediction is mainly used for testing and code inspection. So, software bug prediction is carried out by network measures over the decades. But, the classical fault prediction method failed to obtain the semantic difference among various programs. Thus, it degrades the working of the prediction model, which is designed using these aspects. It is necessary to obtain the semantic difference for designing the prediction model accurately and effectively. In a software defect prediction system, it faces many difficulties in identifying the defect modules like correlation, irrelevance aspects, data redundancy, and missing samples or values. Consequently, many researchers are designed to detect software bug prediction that categorises faulty as well as non-faulty modules with software matrices. But, there are only a few works focussed to mitigate the class imbalance problem in bug prediction. In order to overcome the problem, it is required to develop an efficient software bug prediction method with the enhanced classifier. For this experimentation, the input data are taken from the standard online data sources. Initially, the input data undergo pre-processing phase and then, the pre-processed data are provided as input to the feature extraction by utilising the Auto-Encoder. These obtained features are utilised in getting the optimal fused features with the help of a new Hybrid Honey Badger Cat Swarm Algorithm (HHBCSA). Finally, these features are fed as input to the Optimised Parallel Cascaded Deep Network (OPCDP), where the “Extreme Learning Machine (ELM) and Deep Belief Network (DBN)” are used for the prediction of software bugs, in which the parameters from both classifiers are optimised by proposed HHBCSA algorithm. From the investigations, the recommended software bug prediction method offers a quicker bug prediction result, which helps to detect and remove the software bug easily and accurately.