Cross-project Software Defect Prediction Research Articles

An efficient instance selection algorithm for fast training of support vector machine for cross-project software defect prediction pairs

SVM is limited in its use for cross-project software defect prediction because of its very slow training process. So, this research article proposes a new instance selection (IS) algorithm called boundary detection among classes (BDAC) to reduce the training dataset size for faster training of SVM without degrading the prediction performance. The proposed algorithm is evaluated against six existing IS algorithms based on accuracy, running time, data reduction rate, etc. using 23 general datasets, 18 software defect prediction datasets, and two shape-based datasets, and results prove that BDAC is better than the selected algorithm based on collective comparison.

SDP-MTF: A Composite Transfer Learning and Feature Fusion for Cross-Project Software Defect Prediction

Software defect prediction is critical for improving software quality and reducing maintenance costs. In recent years, Cross-Project software defect prediction has garnered significant attention from researchers. This approach leverages transfer learning to apply the knowledge from existing projects to new ones, thereby enhancing the universality of predictive models. It provides an effective solution for projects with limited historical defect data. Nevertheless, current methodologies face two main challenges: first, the inadequacy of feature information mining, where code statistical information or semantic information is used in isolation, ignoring the benefits of their integration; second, the substantial feature disparity between different projects, which can lead to insufficient effect during transfer learning, necessitating additional efforts to narrow this gap to improve precision. Addressing these challenges, this paper proposes a novel methodology, SDP-MTF (Software Defect Prediction using Multi-stage Transfer learning and Feature fusion), that combines code statistical features, deep semantic features, and multiple feature transfer learning methods to enhance the predictive effect. The SDP-MTF method was empirically tested on single-source cross-project software defect prediction across six projects from the PROMISE dataset, benchmarked against five baseline algorithms that employ distinct features and transfer methodologies. Our findings indicate that SDP-MTF significantly outperforms five classical baseline algorithms, improving the F1-Score by 8% to 15.2%, thereby substantively advancing the precision of cross-project software defect prediction.

Cross-project software defect prediction through multiple learning

Cross-project defect prediction is a method that predicts defects in one software project by using the historical record of another software project. Due to distribution differences and the weak classifier used to build the prediction model, this method has poor prediction performance. Cross-project defect prediction may perform better if distribution differences are reduced, and an appropriate individual classifier is chosen. However, the prediction performance of individual classifiers may be affected in some way by their weaknesses. As a result, in order to boost the accuracy of cross-project defect prediction predictions, this study proposed a strategy that makes use of multiple classifiers and selects attributes that are similar to one another. The proposed method's efficacy was tested using the Relink and AEEEM datasets in an experiment. The findings of the experiments demonstrated that the proposed method produces superior outcomes. To further validate the method, we employed the Wilcoxon sum rank test at 95% significance level. The approach was found to perform significantly better than the baseline methods.

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.

Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross‐project defect prediction method based on genetic algorithm feature selection

AbstractWith the continuous development of Internet technology, the role of software in life is increasing, and software defect prediction (SDP) is a key means to ensure software reliability. SDP is to predict the modules that may have defects in advance based on the historical data of software projects, and its purpose is to maximize the use of testing resources. However, in the actual development process, the project that needs to be predicted is often a new project for which there is little or no historical data. Therefore, how to use the massive data of other related projects to build a cross‐project software defect prediction (CPDP) model has received extensive attention from scholars. However, due to the differences in data distribution and class imbalance between different projects, the performance of CPDP is greatly affected. Therefore, on the basis of CPDP, this article proposes a feature selection method based on genetic algorithm (genetic algorithm feature selection, GAFS). GAFS mainly includes two stages: feature selection and ensemble training. In the feature selection stage, this article proposes a global search adaptive feature selection method based on genetic algorithm, which uses the integrated training results of candidate feature subsets on target data to migrate the optimal feature subset. In the ensemble training phase, the EasyEnsemble method is used to alleviate the class imbalance problem, multiple naive Bayesian classifiers are constructed, and then the final model is constructed through ensemble learning. In this article, F1‐score and MCC are used as the test indicator, and comparative experiments are carried out on AEEEM and Promise. The results show that compared with the five comparison methods, GAFS can improve the average F1‐score and MCC much more. For example, GAFS can improve the average F1‐score value by 38.9%, 31.6%, 35.1%, 22.0%, and 31.6%, respectively. In most cases, it can effectively improve the performance of the model and achieve better prediction results.

Graph-Based Feature Learning for Cross-Project Software Defect Prediction

Graph-Based Feature Learning for Cross-Project Software Defect Prediction

A Hybrid Multiple Models Transfer Approach for Cross-Project Software Defect Prediction

For a new project, it is impossible to get a reliable prediction model because of the lack of sufficient training data. To solve the problem, researchers proposed cross-project defect prediction (CPDP). For CPDP, most researchers focus on how to reduce the distribution difference between training data and test data, and ignore the impact of class imbalance on prediction performance. This paper proposes a hybrid multiple models transfer approach (HMMTA) for cross-project software defect prediction. First, several instances that are most similar to each target project instance are selected from all source projects to form the training data. Second, the same number of instances as that of the defected class are randomly selected from all the non-defect class in each iteration. Next, instances selected from the non-defect classes and all defected class instances are combined to form the training data. Third, the transfer learning method called ETrAdaBoost is used to iteratively construct multiple prediction models. Finally, the prediction models obtained from multiple iterations are integrated by the ensemble learning method to obtain the final prediction model. We evaluate our approach on 53 projects from AEEEM, PROMISE, SOFTLAB and ReLink four defect repositories, and compare it with 10 baseline CPDP approaches. The experimental results show that the prediction performance of our approach significantly outperforms the state-of-the-art CPDP methods. Besides, we also find that our approach has the comparable prediction performance as within-project defect prediction (WPDP) approaches. These experimental results demonstrate the effectiveness of HMMTA approach for CPDP.

A three-stage transfer learning framework for multi-source cross-project software defect prediction

Transfer learning techniques have been proved to be effective in the field of Cross-project defect prediction (CPDP). However, some questions still remain. First, the conditional distribution difference between source and target projects has not been considered. Second, facing multiple source projects, most studies only rarely consider the issues of source selection and multi-source data utilization; instead, they use all available projects and merge multi-source data together to obtain one final dataset. To address these issues, in this paper, we propose a three-stage weighting framework for multi-source transfer learning (3SW-MSTL) in CPDP. In stage 1, a source selection strategy is needed to select a suitable number of source projects from all available projects. In stage 2, a transfer technique is applied to minimize marginal differences. In stage 3, a multi-source data utilization scheme that uses conditional distribution information is needed to help guide researchers in the use of multi-source transferred data. First, we have designed five source selection strategies and four multi-source utilization schemes and chosen the best one to be used in stage 1 and 3 in 3SW-MSTL by comparing their influences on prediction performance. Second, to validate the performance of 3SW-MSTL, we compared it with four multi-source and six single-source CPDP methods, a baseline within-project defect prediction (WPDP) method, and two unsupervised methods on the data from 30 widely used open-source projects. Through experiments, bellwether and weighted vote are separately chosen as a source selection strategy and a multi-source utilization scheme used in 3SW-MSTL. And, our results indicate that 3SW-MSTL outperforms four multi-source, six single-source CPDP methods and two unsupervised methods. And, 3SW-MSTL is comparable to the WPDP method. The proposed 3SW-MSTL model is more effective for considering the two issues mentioned before.

Tsbagging: A Novel Cross-Project Software Defect Prediction Algorithm Based on Semisupervised Clustering

Software defect prediction (SDP) is an important technology which is widely applied to improve software quality and reduce development costs. It is difficult to train the SDP model when software to be test only has limited historical data. Cross-project defect prediction (CPDP) has been proposed to solve this problem by using source project data to train the defect prediction model. Most of CPDP methods build defect prediction models based on the similarity of feature space or data distance between different projects. However, when the target project has a small amount of label data, these methods usually do not consider this part of data information. Therefore, when the distribution between source project and target project is quite different, these methods are difficult to achieve good prediction performance. To solve this problem, this paper proposes a CPDP method based on a semisupervised clustering (namely, Tsbagging). Tsbagging has two stages; in the first stage, we cluster to the source project data based on the limited labeled data in the target project and assign different weights to these source project data according to the clustering results. In the second stage, we use bagging method to train the prediction model based on the weight assigned in the first stage. The experimental results show that the performance achieved by Tsbagging is better than other existing SDP methods.

Using active learning selection approach for cross-project software defect prediction

Cross-project defect prediction (CPDP) technology can effectively ensure software quality, which plays an important role in software engineering. When encountering a newly developed project with insufficient training data, CPDP can be used to build defect predictors using other projects. However, CPDP does not take into account the prior knowledge of the target items and the class imbalance in the source item data. In this paper, we design an active learning selection algorithm for cross-project defect prediction to alleviate the above problems. First, we use clustering and active learning algorithms to filter and label some representative data from the target items and use these data as prior knowledge to guide the selection of source items. Then, the active learning algorithm is used to filter representative data from the source items. Finally, the balanced cross-item dataset is constructed using the active learning algorithm, and the defect prediction model is built. In this article, we selected 10 open-source projects by using common defect prediction models, active learning algorithms, and common evaluation metrics. The results show that the proposed algorithm can effectively filter the data, solve the class imbalance problem in cross-project data, and improve the defect prediction performance.

A novel cross-project software defect prediction algorithm based on transfer learning

Software Defect Prediction (SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years. However, the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets. Another major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction (CPDP). In this work, we propose a transfer-leaning algorithm (TSboostDF) that considers both knowledge transfer and class imbalance for CPDP. The experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.

Cross-Project Software Defect Prediction Based on Class Code Similarity

Software defect prediction techniques can help software developers find software defects as soon as possible. It can also reduce the cost of software development. This technique usually predicts the target project through the entire source project. However, the data distribution difference between the entire source project and the target project is generally large, so the software defect prediction accuracy is not high. we propose a cross-project software defect prediction technique based on class code similarity CCS-CPDP. Firstly, this technique converts the code set extracted by AST(Abstract Syntax Tree) into a vector set through the DTI (Doc2Bow and TF-IDF) strategy; Secondly, the similarity will be calculated between the vector set of target items and training items; Finally, according to the principle of the majority decision subordinate category in KNN, the number of most similar class instances of the training item is determined, and the source item is refined by selecting the class instance, thereby we can realize software defect prediction. This method compares with four traditional classification models (KNN, Random Forest, Naive Bayes, and Logistic Regression) for defect prediction. Experimental results show that CCS-CPDP compared with the baseline, recall and f1 increased by 18.03% and 14.1% respectively. In addition, the refined source projects selected by CCS-CPDP compared with the current source project selection technology, recall and f1 had improved by 37.6% and 12.7%.

Deep Learning Software Defect Prediction Methods for Cloud Environments Research

This paper provides an in-depth study and analysis of software defect prediction methods in a cloud environment and uses a deep learning approach to justify software prediction. A cost penalty term is added to the supervised part of the deep ladder network; that is, the misclassification cost of different classes is added to the model. A cost-sensitive deep ladder network-based software defect prediction model is proposed, which effectively mitigates the negative impact of the class imbalance problem on defect prediction. To address the problem of lack or insufficiency of historical data from the same project, a flow learning-based geodesic cross-project software defect prediction method is proposed. Drawing on data information from other projects, a migration learning approach was used to embed the source and target datasets into a Gaussian manifold. The kernel encapsulates the incremental changes between the differences and commonalities between the two domains. To this point, the subspace is the space of two distributional approximations formed by the source and target data transformations, with traditional in-project software defect classifiers used to predict labels. It is found that real-time defect prediction is more practical because it has a smaller amount of code to review; only individual changes need to be reviewed rather than entire files or packages while making it easier for developers to assign fixes to defects. More importantly, this paper combines deep belief network techniques with real-time defect prediction at a fine-grained level and TCA techniques to deal with data imbalance and proposes an improved deep belief network approach for real-time defect prediction, while trying to change the machine learning classifier underlying DBN for different experimental studies, and the results not only validate the effectiveness of using TCA techniques to solve the data imbalance problem but also show that the defect prediction model learned by the improved method in this paper has better prediction performance.

Cross-project software defect prediction based on multi-source data sets

Cross-project software defect prediction based on multi-source data sets

An Empirical Comparison of Resampling Ensemble Methods of Deep Learning Neural Networks for Cross-Project Software Defect Prediction

An Empirical Comparison of Resampling Ensemble Methods of Deep Learning Neural Networks for Cross-Project Software Defect Prediction

An Empirical Study on Software Defect Prediction Using CodeBERT Model

Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns in software defect prediction using CodeBERT models. The empirical results are further discussed.

Multi-source cross-project software defect prediction based on deep integration

Cross-project defect prediction (CPDP), training a machine learning model by using training data from other projects, has attracted much attention in recent years. This approach provides a feasible way for small-scale or new developed project with insufficient training data to carry out defect prediction. This paper focus on the bottleneck issue of CPDP, poor accuracy, and propose a deep learning model integration-based approach (MTrADL) for CPDP. This paper consists of two main phases. First, the similarity between target project and source projects is measured by the modified maximum mean discrepancy (MMD) and the top K source projects with high similarity to the target project are selected as training data. Second, for the selected training data, this paper use convolutional neural network (CNN) to build the defect predictor. Each selected training data corresponds to one CNN predictor. Then, multiple predictors are integrated to get the final prediction result. To examine the performance of the proposed approach, this paper conduct experiments on 41 datasets of PROMISE and compare our approach with three state-of-the-art baseline approaches: a training data selection model (TDS), a two-stage transfer learning model (TPTL), and the multi-source transfer learning model (MTrA). The experimental results show that the average F1-score of our approach is 0.76. Across the 41 datasets, on average, MTrADL respectively improves these baseline models by 39.8%, 28.50%, and 10% in terms of F1-score.

Cross-project software defect prediction based on domain adaptation learning and optimization

Software defect prediction (SDP) is very helpful for optimizing the resource allocation of software testing and improving the quality of software products. The cross-project defect prediction (CPDP) model based on machine learning is first learned through the existing training data with sufficient number and defect labels on one project, and then used to predict the defect labels of another new project with insufficient number and fewer labeled data. However, its prediction performance has a large gap compared with the within-project defect prediction (WPDP) model. The main reason is that there are usually differences between the distributions of training data in different software projects, and it has a greater impact on the prediction performance of the CPDP model. To solve this problem, the kernel twin support vector machines (KTSVMs) is used to implement domain adaptation (DA) to match the distributions of training data for different projects. Moreover, KTSVMs with DA function (called DA-KTSVM) is further used as the CPDP model in this paper. Since the parameters of DA-KTSVM have an impact on its predictive performance, these parameters are optimized by an improved quantum particle swarm optimization algorithm (IQPSO), and the optimized DA-KTSVM is called as DA-KTSVMO. In order to confirm the effectiveness of DA-KTSVMO, some experiments are implemented on 17 open source software projects. Experimental results and analysis show that DA-KTSVMO can not only achieve better prediction performance than other CPDP models compared, but also achieve almost the same or better compared performance than WPDP models when the training sample data is sufficient. In addition, DA-KTSVMO can make better use of existing sufficient data knowledge and realize the reuse of defective data to improve the prediction performance of DA-KTSVMO.

Correlation feature and instance weights transfer learning for cross project software defect prediction

Due to the differentiation between training and testing data in the feature space, cross-project defect prediction (CPDP) remains unaddressed within the field of traditional machine learning. Recently, transfer learning has become a research hot-spot for building classifiers in the target domain using the data from the related source domains. To implement better CPDP models, recent studies focus on either feature transferring or instance transferring to weaken the impact of irrelevant cross-project data. Instead, this work proposes a dual weighting mechanism to aid the learning process, considering both feature transferring and instance transferring. In our method, a local data gravitation between source and target domains determines instance weight, while features that are highly correlated with the learning task, uncorrelated with other features and minimizing the difference between the domains are rewarded with a higher feature weight. Experiments on 25 real-world datasets indicate that the proposed approach outperforms the existing CPDP methods in most cases. By assigning weights based on the different contribution of features and instances to the predictor, the proposed approach is able to build a better CPDP model and demonstrates substantial improvements over the state-of-the-art CPDP models.