Cross Project Research Articles

Availability Evaluation of Cross Project Software Based on GAKT and Supervised Learning in Personalized Innovation Education for College Students

The availability evaluation of cross project software is crucial for personalized innovation education and learning of college students. Ensuring the stability and efficiency of educational resources and tools is the key to improving learning outcomes. This study proposes an evaluation model that integrates MeanShift clustering method and K-nearest neighbor algorithm. The approach enhanced the feature selection of the model and improved the model by combining attention mechanism. The experimental results showed that the proposed model performed well in multiple aspects. In addition to exhibiting superior convergence performance, the proposed model also demonstrated a significant advantage over naive Bayesian and gradient enhanced decision tree models in terms of delay performance. The optimal delay was 2.47s, and the data volume was as low as 14.12MB. Moreover, its accuracy reached 94.11%, which was 6.61% and 19.11% higher than naive Bayesian and gradient enhanced decision trees, respectively. This model was of practical significance for optimizing the software tools for personalized and innovative education and learning of college students. The MeanShift K-nearest neighbor model can provide educational institutions with a more reliable and efficient evaluation tool. The proposed model can better adapt to and meet the personalized learning needs of students, while also reducing potential learning interruptions caused by software unavailability. In addition, the efficient data processing and accuracy performance of the model means that high-quality availability assessments can also be achieved in resource constrained environments.

Cross Project Defect Prediction using Dropout Regularized Deep Learning and Unique Matched Metrics

The primary goal of software defect prediction (SDP) is to predict the software defects for a specific software using historical data or data from past releases of software projects. The existing state of arts on SDP primarily discusses two prediction scenarios: Within Project Defect Prediction (WPDP) and Cross Project Defect Prediction (CPDP). The prediction model belongs to the WPDP scenario, which means that the model is trained and tested on different parts of the same dataset or trained on the dataset belonging to the previous version of the same project. While in the CPDP scenario, training and testing occur on different software project datasets. Due to the unavailability of historical datasets or prior releases of software defect datasets, CPDP is more useful in real-life scenarios. So, CPDP analysis is a very challenging issue in the SDP domain. Sometimes, machine learning (ML) models perform poorly due to inadequate training in the CPDP scenario. To support better CPDP performance, we must carefully build an ML model focusing on lower training error and overfitting issues. To address these issues, we have proposed a cross-project data preprocessing method to correlate the metrics of different project datasets, namely Unique Selection of Matched Metrics (USMM), using the KS test and Hungarian method. To further improve the CPDP performance, we have also used the dropout regularized deep learning (DRDL) model. We have deployed 34 software defect datasets to validate the DRDL model and USMM method. The experimental results demonstrate that the DRDL model using the USMM method (DRDL-USMM) is a promising model to enhance the prediction accuracy, and an improvement in the range of 3.3% to 8.5% as compared to the existing works in the CPDP scenario has been found.

KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling.

The performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced datasets. To address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and crossover interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and noise. KCO first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by interpolating different data templates within the selected data clusters. According to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline techniques. The experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results.

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 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).

An Approach for Cross Project Defect Prediction Using Identical Metrics Matching and Deep Neural Network

An Approach for Cross Project Defect Prediction Using Identical Metrics Matching and Deep Neural Network

Bug summary entropy based training candidates identification in cross project severity prediction

Bug summary entropy based training candidates identification in cross project severity prediction

CPVD: Cross Project Vulnerability Detection Based on Graph Attention Network and Domain Adaptation

Code vulnerability detection is critical for software security prevention. Vulnerability annotation in large-scale software code is quite tedious and challenging, which requires domain experts to spend a lot of time annotating. This work offers CPVD, a cross-domain vulnerability detection approach based on the challenge of ”learning to predict the vulnerability labels of another item quickly using one item with rich vulnerability labels.” CPVD uses the code property graph to represent the code and uses the Graph Attention Network and Convolution Pooling Network to extract the graph feature vector. It reduces the distribution between the source domain and target domain data in the Domain Adaptation Representation Learning stage for cross-domain vulnerability detection. In this paper, we test each other on different real-world project codes. Compared with methods without domain adaptation and domain adaptation methods based on natural language processing, CPVD is more general and performs better in cross-domain vulnerability detection tasks. Specifically, for the four datasets of chr_deb, qemu, libav, and sard, they achieved the best results of 70.2%, 81.1%, 59.7%, and 78.1% respectively on the F1-Score, and 88.4%,86.3%, 85.2%, and 88.6% on the AUC.

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.

Candidate project selection in cross project defect prediction using hybrid method

Cross Project Defect Prediction (CPDP) is a process that develops a defect prediction model on source projects and then applies the same model to the target project. Day by day, new software projects are being developed, so selecting an appropriate training project from existing projects and from new upcoming projects, to train a predictor model is a challenging task in CPDP. In the present study, we have proposed a hybrid selection method to select a candidate project from existing projects and a probabilistic method to select a candidate project from new projects. The proposed hybrid method is a weighted combination of the Collaborative filtering (CF) method and the Content Based (CB) method. The probabilistic method is based on a Naïve Bayes classifier and is used to predict the relation between the target project and the new target project. In the CF method, a usability score is generated for each project by making use of classification techniques, and the CB method calculates the matching score of candidate projects by using the K-dimensional tree. Finally, both the methods are combined by parallelized hybridization design, and weights for the proposed method are estimated with an empirical bootstrapping method. The score generated by the proposed hybrid technique is then used to identify the most suitable candidate project for the new project. The experimental results show that the suggestion list of the best three candidate projects is consistent when employing different classifiers. The recommendation performance is evaluated in terms of F-score and Mean Average Precision (MAP), and the proposed method has shown improved performance as compared to the existing methods in both terms.

Optimal Feature Selection through Search-Based Optimizer in Cross Project

Cross project defect prediction (CPDP) is a key method for estimating defect-prone modules of software products. CPDP is a tempting approach since it provides information about predicted defects for those projects in which data are insufficient. Recent studies specifically include instructions on how to pick training data from large datasets using feature selection (FS) process which contributes the most in the end results. The classifier helps classify the picked-up dataset in specified classes in order to predict the defective and non-defective classes. The aim of our research is to select the optimal set of features from multi-class data through a search-based optimizer for CPDP. We used the explanatory research type and quantitative approach for our experimentation. We have F1 measure as our dependent variable while as independent variables we have KNN filter, ANN filter, random forest ensemble (RFE) model, genetic algorithm (GA), and classifiers as manipulative independent variables. Our experiment follows 1 factor 1 treatment (1F1T) for RQ1 whereas for RQ2, RQ3, and RQ4, there are 1 factor 2 treatments (1F2T) design. We first carried out the explanatory data analysis (EDA) to know the nature of our dataset. Then we pre-processed our data by removing and solving the issues identified. During data preprocessing, we analyze that we have multi-class data; therefore, we first rank features and select multiple feature sets using the info gain algorithm to get maximum variation in features for multi-class dataset. To remove noise, we use ANN-filter and get significant results more than 40% to 60% compared to NN filter with base paper (all, ckloc, IG). Then we applied search-based optimizer i.e., random forest ensemble (RFE) to get the best features set for a software prediction model and we get 30% to 50% significant results compared with genetic instance selection (GIS). Then we used a classifier to predict defects for CPDP. We compare the results of the classifier with base paper classifier using F1-measure and we get almost 35% more than base paper. We validate the experiment using Wilcoxon and Cohen’s d test.

Cross-Project Online Just-In-Time Software Defect Prediction

Cross-Project (CP) Just-In-Time Software Defect Prediction (JIT-SDP) makes use of CP data to overcome the lack of data necessary to train well performing JIT-SDP classifiers at the beginning of software projects. However, such approaches have never been investigated in realistic online learning scenarios, where Within-Project (WP) software changes naturally arrive over time and can be used to automatically update the classifiers. We provide the first investigation of when and to what extent CP data are useful for JIT-SDP in such realistic scenarios. For that, we propose three different online CP JIT-SDP approaches that can be updated with incoming CP and WP training examples over time. We also collect data on 9 proprietary software projects and use 10 open source software projects to analyse these approaches. We find that training classifiers with incoming CP+WP data can lead to absolute improvements in G-mean of up to 53.89% and up to 35.02% at the initial stage of the projects compared to classifiers using WP-only and CP-only data, respectively. Using CP+WP data was also shown to be beneficial after a large number of WP data were received. Using CP data to supplement WP data helped the classifiers to reduce or prevent large drops in predictive performance that may occur over time, leading to absolute G-Mean improvements of up to 37.35% and 48.16% compared to WP-only and CP-only data during such periods, respectively. During periods of stable predictive performance, absolute improvements were of up to 29.03% and up to 41.25% compared to WP-only and CP-only classifiers, respectively. Our results highlight the importance of using both CP and WP data together in realistic online JIT-SDP scenarios.

Performance Evaluation of Convolutional Neural Network for Multi-Class in Cross Project Defect Prediction

Cross-project defect prediction (CPDP) is a practical approach for finding software defects in projects which have incomplete or fewer data. Improvements to the defect prediction accuracy of CPDP—such as the PROMISE repository, the correct classification of the source data, removing the noise, reducing the distribution gap, and balancing the output classes—are an ongoing challenge, as is the selection of an optimal feature set. This research paper aims to achieve a higher defect prediction accuracy for multi-class CPDP by selecting an optimal feature set through XGBoost combined with an automatic feature extraction using a convolutional neural network (CNN). This research type is explanatory, and this research method is controlled experimentation, for which the independent variable prediction accuracy was dependent upon two variables, XGBoost and CNN. The Softmax layer was added to the output layers of the CNN classifier to classify the output into multiple classes. In our experimentation with CPDP, we selected all 28 versions of the multi-class, in which 11 versions were selected as the source projects, against which we predicted 28 target versions with an average AUC of 75.57%. We validated this research paper’s results through the Wilcoxon test. Therefore, after removing the noise, class imbalances, and the data distribution gap, and treating the PROMISE dataset as multi-class, the optimal features selected through XGBoost and classified through the CNN can substantially increase the prediction accuracy in CPDP as evident from our exploratory data analysis (EDA).

Impact of Optimal Feature Selection Using Hybrid Method for a Multiclass Problem in Cross Project Defect Prediction

The objective of cross-project defect prediction (CPDP) is to develop a model that trains bugs on current source projects and predicts defects of target projects. Due to the complexity of projects, CPDP is a challenging task, and the precision estimated is not always trustworthy. Our goal is to predict the bugs in the new projects by training our model on the current projects for cross-projects to save time, cost, and effort. We used experimental research and the type of research is explanatory. Our research method is controlled experimentation, for which our independent variable is prediction accuracy and dependent variables are hyper-parameters which include learning rate, epochs, and dense layers of neural networks. Our research approach is quantitative as the dataset is quantitative. The design of our research is 1F1T (1 factor and 1 treatment). To obtain the results, we first carried out exploratory data analysis (EDA). Using EDA, we found that the dataset is multi-class. The dataset contains 11 different projects consisting of 28 different versions of all the projects in total. We also found that the dataset has significant issues of noise, class imbalance, and distribution gaps between different projects. We pre-processed the dataset for experimentation by resolving all these issues. To resolve the issue of noise, we removed duplication from the dataset by removing redundant rows. We then covered the data distribution gap between different sources and target projects using the min-max normalization technique. After covering the data distribution gap, we generated synthetic data using a CTGANsynthesizer to solve class imbalance issues. We solved the class imbalance issue by generating an equal number of instances, as well as an equal number of output classes. After carrying out all of these steps, we obtained normalized data. We applied the hybrid feature selection technique on the pre-processed data to optimize the feature set. We obtained significant results of an average AUC of 75.98%. From the empirical study, it was demonstrated that feature selection and hyper-parameter tuning have a significant impact on defect prediction accuracy in cross-projects.

An empirical study of Aging Related Bug prediction using Cross Project in Cloud Oriented Software

Cloud oriented applications enable users to increase the scalability of computing infrastructure by using shared computer resources. These applications include the characteristics such as NoSQL database, BigData Analytics, distributed file system and MapReduce architecture which may face issues like software aging due to which ongoing system's performance decreases and failure rate increases. Aging Related Bugs (ARB) are bugs that are caused due to memory leakage, null pointer exception, resource depletion etc. in the ongoing system whose impact can be dangerous, so it’s better to predict them before releasing the software. Manual extraction of ARB reports are common but finding ARBs within thousand of bug reports is challenging. This is the first paper that presents the empirical study to automatically search aging related bug reports through SEARCH_KEYWORD algorithm and implement the ARB prediction in cross project for cloud oriented applications/softwares. To compare the efficacy of the prediction results, With-in Project Defect Prediction (WPDP) of ARBs is also performed. The work is divided in three phases: 1. ARB reports are extracted from the summary/description of bug in bug repository through automatic process. 2. Cross project bug prediction (CPDP) is performed to predict ARB due to limited availability of training data which is not implemented yet in cloud oriented softwares to the best of our knowledge. 3. Machine learning techniques are applied for ARB prediction to build fault prediction models. There is an imbalanced proportion between ARB-prone and ARB-free files, therefore Recall, FPR(False Positive Rate), Balance are used as major performance measures to predict ARBs. Kruskal Wallis Test and Friedman Test, are applied on the prediction results and it is proved that Naive Bayes performed significantly better than other classifiers. The results suggested that CPDP performed better than WPDP of ARBs using machine learning classifiers in cloud oriented datasets.

An unsupervised cross project model for crashing fault residence identification

It is a critical quality assurance activity to effectively detect the root cause of faults causing the software crashes (i.e. crashing faults). Previous studies extracted features to characterise crash instances and built models to identify whether the residences of crashing faults locate inside the stack traces. These models all belong to supervised learning methods which require labelled crash data to be involved. In this study, the introduction of an unsupervised model, called Transfer Spectral Clustering (TSC), for the task of crashing fault residence identification under the unlabelled data scenario is proposed. Unlike traditional unsupervised methods which are applied to individual project data, TSC transfers the knowledge of auxiliary unlabelled data from the source project to assist the clustering task on the unlabelled data from the target project. TSC is an unsupervised transfer learning method, and simultaneously considers the data manifold information of the individual project and feature manifold information across projects to facilitate the clustering effect. Extensive experiments are conducted on a benchmark dataset containing seven software projects. Five indicators were chosen for performance evaluation. The results show that TSC achieves better performance than four clustering based unsupervised methods, and competitive performance compared with eight supervised cross-project methods.

An optimized approach for class imbalance problem in heterogeneous cross project defect prediction

Background: In recent studies, Cross Project Defect Prediction (CPDP) has proven to be feasible in software defect prediction. When both the source as well as the target projects have the same metric sets, it is termed as a homogeneous CPDP. Current CPDP strategies are difficult to implement through projects with a variety of different metric sets. Aside from that, training data often has a problem with class imbalance. The number of defective/bug-ridden and non-defective/clean instances of the source class is usually unbalanced. To address this issue, we propose a heterogeneous cross-project defect prediction framework that can predict defects across projects with different metric sets. Methods: To construct a prediction framework between projects with heterogeneous metric sets, our heterogeneous cross project defect prediction approach uses metric selection, metric matching, class imbalance (CIB) learning followed by ensemble modelling. For our study, we have considered six open-source object-oriented projects. Results: The proposed model resolved the class imbalance issue and records the highest recall value of 7.5 with f-score value as 7.4 in comparison with other baseline models. The highest AUC (area under curve) value of 0.86 has also been recorded. K fold cross validation was performed to evaluate the training accuracy of the model. The proposed optimized model was validated using the Wilcoxon signed rank test (WSR) with a significance level of 5% (i.e., P-value=0.05). Conclusions: Our empirical research on these six projects shows that predictions based on our methodology outperform or are statistically comparable to Within-Project Defect Prediction (WPDP) and other heterogeneous CPDP baseline models.

Cross-Project Software Refactoring Prediction Using Optimized Deep Learning Neural Network With the Aid of Attribute Selection

Cross-project refactoring prediction is prominent research that comprises model training from one project database and testing it for a database under a separate project. While performing the refactoring process on the cross project, software programs want to be restructured by modifying or adding the source code. However, recognizing a piece of code for predicting refactoring purposes is remained to be actual challenge for software designers. To date the entire refactoring procedure is highly dependent on the skills and software inventers. In this manuscript, a deep learning model is utilized to introduce a predictive model for refactoring to highlight classes that need to be refactored. Specifically, the deep learning technique is utilized along with the proposed attribute selection phases to predict refactoring at the class level. The planned optimized deep learning-based method for cross-project refactoring prediction is experimentally conducted on open- source project and accuracy found as 0.9648 as comparison to other mentioned state of the art.

Improved SMOTE and Optimized Siamese Neural Networks for Class Imbalanced Heterogeneous Cross Project Defect Prediction

Improved SMOTE and Optimized Siamese Neural Networks for Class Imbalanced Heterogeneous Cross Project Defect Prediction

Experimental analysis of zero-natural-frequency damper for transmission line large cross project based on NES

The operation experience of transmission line large cross project shows that the traditional anti-aeolian-vibration devices had poor adaptability to complex terrain and micro meteorological conditions, and they were difficult to meet the application requirements of super large cross project at this stage. A zero-natural-frequency damper was designed by introducing nonlinear energy sink in this paper, and the solid prototypes were processed. According to the actual design parameters of conductor using in a large cross project, the anti-vibration effect was tested by using the indoor simulation test span. The test results showed that the anti-vibration effect of the scheme based on the zero-natural-frequency damper could meet the needs of practical engineering. The installation of this damper could improve conductors’ and fittings’ adaptability to complex terrain and enhance the wind and anti-vibration ability of transmission line.