Software Defect Prediction Research Articles

Hybrid Defect Prediction Model Based on Counterfactual Feature Optimization

Software defect prediction is critical to ensuring software quality. Researchers have worked on building various defect prediction models to improve the performance of defect prediction. Existing defect prediction models are mainly divided into two categories: models constructed based on artificial statistical features and models constructed based on semantic features. DP-CNN [Li J, He P, Zhu J, et al. Software defect prediction via convolutional neural network. In: 2017 IEEE international conference on software quality, reliability and security (QRS). IEEE, 2017; 318–328.] is one of the best defect prediction models, because it combines both artificial statistical features and semantic features, so its performance is greatly improved compared to traditional defect prediction models. This paper is based on the DP-CNN model and makes the following two improvements: first, using a new Struc2vec network representation technique to mine existing information between software modules, which specializes in learning node representations from structural identity and can further extract structural features associated with defects. Let the DP-CNN model once again incorporate the newly mined structural features. Then, this paper proposes a feature selection method based on counterfactual explanations, which can determine the importance score of each feature by the feature change rate of counterfactual samples. The origin of these feature importance scores is interpretable. Under the guidance of these interpretable feature importance scores, better feature subsets can be obtained and used to optimize artificial statistical features within the DP-CNN model. Based on the above methods, this paper proposes a new hybrid defect prediction model DPS-CNN-STR. Evaluating our model on six open source projects in terms of F1 score in defect prediction. Experimental results show that DPS-CNN-STR improves the state-of-the-art method by an average of 3.3%.

Industrial applications of software defect prediction using machine learning: A business-driven systematic literature review

Industrial applications of software defect prediction using machine learning: A business-driven systematic literature review

Software Defect Prediction Based on Optimized Machine Learning Models: A Comparative Study

Software defect prediction is crucial used for detecting possible defects in software before they manifest. While machine learning models have become more prevalent in software defect prediction, their effectiveness may vary based on the dataset and hyperparameters of the model. Difficulties arise in determining the most suitable hyperparameters for the model, as well as identifying the prominent features that serve as input to the classifier. This research aims to evaluate various traditional machine learning models that are optimized for software defect prediction on NASA MDP (Metrics Data Program) datasets. The datasets were classified using k-nearest neighbors (k-NN), decision trees, logistic regression, linear discriminant analysis (LDA), single hidden layer multilayer perceptron (SHL-MLP), and Support Vector Machine (SVM). The hyperparameters of the models were fine-tuned using random search, and the feature dimensionality was decreased by utilizing principal component analysis (PCA). The synthetic minority oversampling technique (SMOTE) was implemented to oversample the minority class in order to correct the class imbalance. k-NN was found to be the most suitable for software defect prediction on several datasets, while SHL-MLP and SVM were also effective on certain datasets. It is noteworthy that logistic regression and LDA did not perform as well as the other models. Moreover, the optimized models outperform the baseline models in terms of classification accuracy. The choice of model for software defect prediction should be based on the specific characteristics of the dataset. Furthermore, hyperparameter tuning can improve the accuracy of machine learning models in predicting software defects.

A Study of Software Defect Prediction Using Artificial Intelligent and Business Risk Management Approach

Abstract: The usage of several artificial intelligence algorithms in the area of software defect prediction is reviewed in the literature in this paper, it review the use of various techniques for artificial intelligence in the field of software defect prediction. Software system flaws remain a significant issue, and modern software development now faces many difficulties. Identification, estimation, and evaluation of the risks are crucial steps in order to avoid, minimize, and monitor the risks and their effects.This literature review is quite helpful because it improves understanding of the studied area. A software fault is a flaw in an executable product that results in software failure. Saving money, time, and effort may be accomplished by identifying general software process areas that may require attention from the start of a project and understanding the potential causes of problems. Planning, managing, and carrying out software development activities could be aided by the ability for early fault estimation. In order to study how many prediction approaches have been developed over this time to address the problems linked to software defect, this research conducts a literature review of works from the previous many years.In the field of software development, the study also discusses software risk management. This study makes a significant contribution by proposing a method for analyzing, organizing, and utilizing information technology initiatives in the context of software defect prediction and risk management.

Learning based Software Defect Prediction

Learning based Software Defect Prediction

Pre-trained Model-based Software Defect Prediction for Edge-cloud Systems

Edge-cloud computing is a distributed computing infrastructure that brings computation and data storage with low latency closer to clients. As interest in edge-cloud systems grows, research on testing the systems has also been actively studied. However, as with traditional systems, the amount of resources for testing is always limited. Thus, we suggest a function-level just-in-time (JIT) software defect prediction (SDP) model based on a pre-trained model to address the limitation by prioritizing the limited testing resources for the defect-prone functions. The pre-trained model is a transformer-based deep learning model trained on a large corpus of code snippets, and the fine-tuned pre-trained model can provide the defect proneness for the changed functions at a commit level. We evaluate the performance of the three popular pre-trained models (i.e., CodeBERT, GraphCodeBERT, UniXCoder) on edge-cloud systems in within-project and cross-project environments. To the best of our knowledge, it is the first attempt to analyse the performance of the three pre-trained model-based SDP models for edge-cloud systems. As a result, we can confirm that UniXCoder showed the best performance among the three in the WPDP environment. However, we also confirm that additional research is necessary to apply the SDP models to the CPDP environment.

Implicit and explicit mixture of experts models for software defect prediction

Implicit and explicit mixture of experts models for software defect prediction

Implement Classification Approach for Software Defect Prediction

Implement Classification Approach for Software Defect Prediction

A Hybrid Software Defects Prediction Model for Imbalance Datasets Using Machine Learning Techniques: (S-SVM Model)

Software defect prediction (SDP) is an essential task for developing quality software, and various models have been developed for this purpose. However, the imbalanced nature of software defect datasets has challenged these models, resulting in decreased performance. To address this challenge, the author has proposed a hybrid machine learning model that combines Synthetic Minority Oversampling Technique (SMOTE) with Support Vector Machine (SVM)—SMOTE-SVM (S-SVM) model. The author has empirically examined SDP using multiple datasets (CM1, PC1, JM1, PC3, KC1, EQ and JDT) from the PROMISE and AEEEM repositories. The experimental study indicates that the S-SVM model involved training and compared with previously developed balanced and imbalanced test datasets using four evaluation metrics: Precision, Recall, F1 score, and Accuracy. For the balanced dataset, the S-SVM model achieved precision values ranging from 70 to 96, recall values ranging from 52 to 94, F1-score values ranging from 67 to 90, and accuracy values ranging from 69 to 98. For the imbalanced dataset, the S-SVM model achieved precision values ranging from 60 to 93, recall values ranging from 64 to 97, F1-score values ranging from 69 to 91, and accuracy values ranging from 67 to 87. The proposed S-SVM model outperforms other models’ ability to classify and predict software defects. Therefore, the hybridisation of SMOTE and SVM improved the model’s ability to categories and predict balanced and imbalanced datasets when sufficient defective and non-defective data is provided.

Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

AbstractJust‐In‐Time (JIT) defect prediction aims to predict the defect proneness of software changes when they are initially submitted. It has become a hot topic in software defect prediction due to its timely manner and traceability. Researchers have proposed many JIT defect prediction approaches. However, these approaches cannot effectively utilise line labels representing added or removed lines and ignore the noise caused by defect‐irrelevant files. Therefore, a JIT defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT‐FF) is proposed. Firstly, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Secondly, to obtain semantics‐enhanced code representation, a cross‐attention‐based line label fusion method to perform complementary feature enhancement is proposed. Thirdly, to generate code changes containing fewer defect‐irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning‐based file filtering method. Finally, based on generated code changes, CodeBERT‐based commit representation and multi‐layer perceptron‐based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT‐FF can predict defective software changes more effectively.

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

Class Imbalance Reduction and Centroid based Relevant Project Selection for Cross Project Defect Prediction

Cross-Project Defect Prediction (CPDP) is the process of predicting defects in a target project using information from other projects. This can assist developers in prioritizing their testing efforts and finding flaws. Transfer Learning (TL) has been frequently used at CPDP to improve prediction performance by reducing the disparity in data distribution between the source and target projects. Software Defect Prediction (SDP) is a common study topic in software engineering that plays a critical role in software quality assurance. To address the cross-project class imbalance problem, Centroid-based PF-SMOTE for Imbalanced data is used. In this paper, we used a Centroid-based PF-SMOTE to balance the datasets and Centroid based relevant data selection for Cross Project Defect Prediction. These methods use the mean of all attributes in a dataset and calculating the difference between mean of all datasets. For experimentation, the open source software defect datasets namely, AEEM, Re-Link, and NASA, are considered.

Just-in-time defect prediction for mobile applications: using shallow or deep learning?

Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of continuous integration and continuous testing paradigm. Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. While deep learning (DL) algorithms can provide state-of-the-art performance in many application domains, they should be carefully selected and designed for a software engineering problem. In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. Experimental results demonstrated that DL algorithms leveraging sampling methods perform significantly worse than the decision tree-based ensemble method. The XGBoost-based model appears to be 116 times faster than the multilayer perceptron-based (MLP) prediction model. This study indicates that DL-based models are not always the optimal solution for software defect prediction, and thus, shallow, traditional machine learning can be preferred because of better performance in terms of accuracy and time parameters.

Hybrid Optimization-Based Neural Network Classifier for Software Defect Prediction

The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality. The robustness and reliability are the major concerns in the existing SDP approaches. Hence, in this paper, the hybrid optimization-based neural network (Optimized NN) is developed for the effective detection of the defects in the software. The two main steps involved in the Optimized NN-based SDP are feature selection and SDP utilizing Optimized NN. The data is fed forwarded to the feature selection module, where relief algorithm selects the significant features relating to the defect and no-defects. The features are fed to the SDP module, and the optimal tuning of NN classifier is obtained by the hybrid optimization developed by the integration of the social spider algorithm (SSA) and gray wolf optimizer (GWO). The comparative analysis of the developed prediction model reveals the effectiveness of the proposed method that attained the maximum accuracy of 93.64%, maximum sensitivity of 95.14%, maximum specificity of 99%, maximum [Formula: see text]-score of 93.53%, and maximum precision of 99% by considering the [Formula: see text]-fold.

Semantic feature learning for software defect prediction from source code and external knowledge

Software defects not only reduce operational reliability but also significantly increase overall maintenance costs. Consequently, it is necessary to predict software defects at an early stage. Existing software defect prediction studies work with artificially designed metrics or features extracted from source code by machine learning-based approaches to perform classification. However, these methods fail to make full use of the defect-related information other than code, such as comments in codes and commit messages. Therefore, in this paper, additional information extracted from natural language text is combined with the programming language codes to enrich the semantic features. A novel model based on Transformer architecture and multi-channel CNN, PM2-CNN, is proposed for software defect prediction. Pretrained language model and CNN-based classifier are utilized in the model to obtain context-sensitive representations and capture the local correlation of sequences. A large and widely used dataset is utilized to verify the effectiveness of the proposed method. The results show that the proposed method has improvements in generic evaluation metrics compared with the optimal baseline method. Accordingly, external information can have a positive impact on software defect prediction, and our model effectively incorporates such information to improve detection performance.

Defect prediction of big data computer network based on deep learning model

Abstract Computer network software prediction is a good way to improve software quality, and the predictions of the software prediction method are close to the characteristics of the data set. In order to solve the problem that the invisible size of special data set is too large for computer software prediction, the author proposed a computer network-based software prediction method with deep computer coding and power learning. deep exploration of data features. data features. This type of model first uses an unsupervised learning-based evaluation model to evaluate the data set of 6 open projects, which solves the problem of classification uncertainty in the data; Deep self-encoding network models were then investigated. The model reduces the size of the data set, which is used to connect our model at the end of the model, the model uses training sets of shortened length to train the workers, and finally it makes predictions using the benchmarks. Experiments show that this model's prediction is better than standard software defect prediction, better than existing model-based software prediction models for fabric processes with repeated data volumes, and can be used in different categories. algorithms.

Gaussian kernelized feature selection and improved multilayer perceptive deep learning classifier for software fault prediction

Software fault prediction is the significant process of identifying the errors or defects or faults in a software product. But, accurate and timely detection is the major challenging issue in different existing approaches to predicting software defects. A novel Gaussian linear feature embedding-based statistical test piecewise multilayer perceptive deep learning classifier (GLFE-STPMPDLC) is introduced to improve software fault prediction accuracy and minimize time consumption. First, the input data are collected from the dataset. Next, the software metrics selection is carried out to select the significant metrics using Gaussian kernelized locally linear embedding with lesser software fault prediction. Then classification is carried out by Kaiser Meyer piecewise multilayer perceptive deep learning classifier for software fault prediction. The novelty of Kaiser–Meyer–Olkin (KMO) correlation test analyzes testing and training instances. The innovation of the Heaviside step activation function is applied for analyzing the KMO correlation test results and providing the final software fault prediction results. Finally, accurate fault prediction outcomes are achieved at the output layer with lesser error. Simulation of proposed GLFE-STPMPDLC technique achieves better 5%, 3%, 3% and 3% enhancement of fault prediction accuracy, precision, recall, and f-measure and 13% faster prediction time compared to conventional methods.

Application of Deep Learning in Software Defect Prediction: Systematic Literature Review and Meta-analysis

Application of Deep Learning in Software Defect Prediction: Systematic Literature Review and Meta-analysis

Software Defect Prediction with Bayesian Approaches

Software defect prediction is an important area in software engineering because it helps developers identify and fix problems before they become costly and hard-to-fix bugs. Early detection of software defects helps save time and money in the software development process and ensures the quality of the final product. This research aims to evaluate three algorithms to build Bayesian Networks to classify whether a project is prone to defects. The choice is based on the fact that the most used approach in the literature is Naive Bayes, but no works use Bayesian Networks. Thus, K2, Hill Climbing, and TAN are used to construct Bayesian Networks. On the other hand, three public PROMISE data sets are used based on McCabe and Halstead complexity metrics. The results are compared with the most used approaches in the literature, such as Decision Tree and Random Forest. The results from different performance metrics applied to a cross-validation process show that the classification results are comparable to Decision Tree and Random Forest, with the advantage that Bayesian algorithms show less variability, which helps engineering software to have greater robustness in their predictions since the selection of training and test data do not give variable results, unlike Decision Tree and Random Forest.

Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

A program’s bug, fault, or mistake that results in unintended results is known as a software defect or fault. Software flaws are programming errors due to mistakes in the requirements, architecture, or source code. Finding and fixing bugs as soon as they arise is a crucial goal of software development that can be achieved in various ways. So, selecting a handful of optimal subsets of features from any dataset is a prime approach. Indirectly, the classification performance can be improved through the selection of features. A novel approach to feature selection (FS) has been developed, which incorporates the Golden Jackal Optimization (GJO) algorithm, a meta-heuristic optimization technique that draws on the hunting tactics of golden jackals. Combining this algorithm with four classifiers, namely K-Nearest Neighbor, Decision Tree, Quadrative Discriminant Analysis, and Naive Bayes, will aid in selecting a subset of relevant features from software fault prediction datasets. To evaluate the accuracy of this algorithm, we will compare its performance with other feature selection methods such as FSDE (Differential Evolution), FSPSO (Particle Swarm Optimization), FSGA (Genetic Algorithm), and FSACO (Ant Colony Optimization). The result that we got from FSGJO is great for almost all the cases. For many of the results, FSGJO has given higher classification accuracy. By utilizing the Friedman and Holm tests, to determine statistical significance, the suggested strategy has been verified and found to be superior to prior methods in selecting an optimal set of attributes.