Cross-project Defect Prediction Research Articles

Selective Pseudo-Labeling Based Subspace Learning for Cross-Project Defect Prediction

Selective Pseudo-Labeling Based Subspace Learning for Cross-Project Defect Prediction

Within‐project and cross‐project just‐in‐time defect prediction based on denoising autoencoder and convolutional neural network

Just-in-time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of artificial intelligence, which can combine basic defect features into deep semantic features and make up for the shortcomings of machine learning algorithms. However, the mainstream deep learning techniques have not been applied yet in just-in-time defect prediction. Therefore, the authors propose a novel just-in-time defect prediction model named DAECNN-JDP based on denoising autoencoder and convolutional neural network in this study, which has three main advantages: (i) Different weights for the position vector of each dimension feature are set, which can be automatically trained by adaptive trainable vector. (ii) Through the training of denoising autoencoder, the input features that are not contaminated by noise can be obtained, thus learning more robust feature representation. (iii) The authors leverage a powerful representation-learning technique, convolution neural network, to construct the basic change features into the abstract deep semantic features. To evaluate the performance of the DAECNN-JDP model, they conduct extensive within-project and cross-project defect prediction experiments on six large open source projects. The experimental results demonstrate that the superiority of DAECNN-JDP on five evaluation metrics.

Within-project and cross-project software defect prediction based on improved transfer Naive Bayes algorithm

With the continuous expansion of software scale, software update and maintenance have become more and more important. However, frequent software code updates will make the software more likely to introduce new defects. So how to predict the defects quickly and accurately on the software change has become an important problem for software developers. Current defect prediction methods often cannot reflect the feature information of the defect comprehensively, and the detection effect is not ideal enough. Therefore, we propose a novel defect prediction model named ITNB (Improved Transfer Naive Bayes) based on improved transfer Naive Bayesian algorithm in this paper, which mainly considers the following two aspects: (1) Considering that the edge data of the test set may affect the similarity calculation and final prediction result, we remove the edge data of the test set when calculating the data similarity between the training set and the test set; (2) Considering that each feature dimension has different effects on defect prediction, we construct the calculation formula of training data weight based on feature dimension weight and data gravity, and then calculate the prior probability and the conditional probability of training data from the weight information, so as to construct the weighted bayesian classifier for software defect prediction. To evaluate the performance of the ITNB model, we use six datasets from large open source projects, namely Bugzilla, Columba, Mozilla, JDT, Platform and PostgreSQL. We compare the ITNB model with the transfer Naive Bayesian (TNB) model. The experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary, precision and pd for within-project and cross-project defect prediction.

Heterogeneous Cross Project Defect Prediction in Software

Software defect prediction is one of the software engineering's most active research fields. Most of the existing work focuses on Homogeneous Cross Project Defect Prediction (CPDP), in which the model is trained by the use of a common metric set extracted from the source and target project. Our article emphasizes the heterogeneous CPDP modeling (HCPDP) that develops a defect prediction model based on a metric choice and metric matching strategy and also shows a comparable value distribution for a specified couple of datasets. It assesses experimentally and theoretically HCPDP modeling whose three primary elements include feature ranking and feature selection, metric matching an binary classification of unlabeled target instances. Results indicate that feature selection techniques have a very tiny effect on the performance of defect prediction, and the Gradient Boosting classification system provides the highest findings when used in combination with the Pearson Correlation technique compared to other classifiers used.

Software Defect Prediction Via Deep Learning

Existing models on defect prediction are trained on historical limited data which has been studied from a variety of pioneering and researchers. Cross-project defect prediction, which is often reuse data from other projects, works well when the data of training models is completely sufficient to meet the project demands. However, current studies on software defect prediction require some degree of heterogeneity of metric values that does not always lead to accurate predictions. Inspired by the current research studies, this paper takes the benefit with the state-of-the-art of deep learning and random forest to perform various experiments using five different datasets. Our model is ideal for predicting of defects with 90% accuracy using 10-fold cross-validation. The achieved results show that Random Forest and Deep learning are giving more accurate predictions with compared to Bayes network and SVM on all five datasets. We also derived Deep Learning that can be competitive classifiers and provide more robust for detecting defect prediction.

Dimensional Reduction on Cross Project Defect Prediction

The complexity of the software can increase the possibility of defects. Defective software can cause high losses. The software containing defects can cause large losses. Most software developers don’t document their work properly so that making it difficult to analyse software development history data. The cross-project software defect prediction used several datasets from different projects and combining for training and testing. The dataset with high dimension can cause bias, contain irrelevance data, and require large resources to process it. In this study, several dimensional reduction algorithm and Decision Tree as classifier. Based on the analysis using ANOVA, all models that implement dimensional reduction can significantly improve the performance of the Decision Tree model.

Boosted Relief Feature Subset Selection and Heterogeneous Cross Project Defect Prediction using Firefly Particle Swarm Optimization

The exponential growth in the field of information technology, need for quality-based software development is highly demanded. The important factor to be focused during the software development is software defect detection in earlier stages. Failure to detect hidden faults will affect the effectiveness and quality of the software usage and its maintenance. In traditional software defect prediction models, projects with same metrics are involved in prediction process. In recent years, active topic is dealing with Cross Project Defect Prediction (CPDP) to predict defects on software project from other software projects dataset. Still, traditional cross project defect prediction approaches also require common metrics among the dataset of two projects for constructing the defect prediction techniques. Suppose if cross project dataset with different metrics has to be used for defect prediction then these methods become infeasible. To overcome the issues in software defect prediction using Heterogeneous cross projects dataset, this paper introduced a Boosted Relief Feature Subset Selection (BRFSS) to handle the two different projects with Heterogeneous feature sets. BRFSS employs the mapping approach to embed the data from two different domains into a comparable feature space with a lower dimension. Based on the similarity measure the difference among the mapped domains of dataset are used for prediction process. This work used five different software groups with six different datasets to perform heterogeneous cross project defect prediction using firefly particle swarm optimization. To produce optimal defect prediction in the Heterogeneous environment, the knowledge of particle swarm optimization by inducing firefly algorithm. The simulation result is compared with other standard models, the outcome of the result proved the efficiency of the prediction process while using firefly enabled particle swarm optimization.

Cross-version defect prediction: use historical data, cross-project data, or both?

Although a long-running project has experienced many releases, removing defects from a product is still a challenge. Cross-version defect prediction (CVDP) regards project data of prior releases as a useful source for predicting fault-prone modules based on defect prediction techniques. Recent studies have explored cross-project defect prediction (CPDP) that uses the project data from outside a project for defect prediction. While CPDP techniques and CPDP data can be diverted to CVDP, its effectiveness has not been investigated. To investigate whether CPDP approaches and CPDP data are useful for CVDP. The investigation also compared the usage of prior release data. We chose a style of replication of a previous comparative study on CPDP approaches. Some CPDP approaches could improve the performance of CVDP. The use of the latest prior release was the best choice. If one has no CVDP data, the use of CPDP data for CVDP was found to be effective. 1) Some CPDP approaches could improve CVDP, 2), if one can access project data from the latest release, project data from older releases would not bring clear benefit, and 3) even if one has no CVDP data, appropriate CPDP approaches would be able to deliver quality prediction with CPDP data.

An Empirical Study on Heterogeneous Defect Prediction Approaches

Software defect prediction has always been a hot research topic in the field of software engineering owing to its capability of allocating limited resources reasonably. Compared with cross-project defect prediction (CPDP), heterogeneous defect prediction (HDP) further relaxes the limitation of defect data used for prediction, permitting different metric sets to be contained in the source and target projects. However, there is still a lack of a holistic understanding of existing HDP studies due to different evaluation strategies and experimental settings. In this paper, we provide an empirical study on HDP approaches. We review the research status systematically and compare the HDP approaches proposed from 2014 to June 2018. Furthermore, we also investigate the feasibility of HDP approaches in CPDP. Through extensive experiments on 30 projects from five datasets, we have the following findings: (1) metric transformation-based HDP approaches usually result in better prediction effects, while metric selection-based approaches have better interpretability. Overall, the HDP approach proposed by Li <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (CTKCCA) currently has the best performance. (2) Handling class imbalance problems can boost the prediction effects, but the improvements are usually limited. In addition, utilizing mixed project data cannot improve the performance of HDP approaches consistently since the label information in the target project is not used effectively. (3) HDP approaches are feasible for cross-project defect prediction in which the source and target projects have the same metric set.

A Suitable AST Node Granularity and Multi-Kernel Transfer Convolutional Neural Network for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a feasible way to perform software defect prediction (SDP) when lacking historical data. Recent CPDP approaches have employed deep learning techniques to better exploit the information from the program's abstract syntax trees (ASTs). However, the granularity of the AST nodes and the data distribution difference between projects may have negative impacts on the prediction performance, which many CPDP studies didn't take into consideration. To handle these issues, this paper explores a better AST node granularity and proposes a CPDP framework based on multi-kernel transfer convolutional neural networks. Specifically, for AST node granularity, we explore the difference of three AST node granularities and then compare the prediction performance of each granularity on several prediction models. For the CPDP framework, we first parse the program source code into ASTs and then encode the AST nodes into numerical vectors using the embedding technique. Secondly, to mine transferable semantic features, the encoded ASTs are fed into a convolutional neural network, in which a multi-kernel matching layer is added to minimize the data distribution divergence between the source and target project. Finally, to make use of the information from the handcrafted features, the semantic features mined from the AST are joint with handcrafted features to form the joint features for CPDP. We evaluate our approach on 110 CPDP tasks formed by 11 open-source projects and results show that the proposed CPDP method outperforms most deep learning-based approaches.

An Adversarial Discriminative Convolutional Neural Network for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a promising approach to help to allocate testing efforts efficiently and guarantee software reliability in the early software lifecycle. A CPDP method usually trains a software defect classifier based on labeled data sets. Then the trained classifier can predict new projects without labeled data. Most previous CPDP techniques focused on manually designing handcrafted features. However, these handcrafted features ignore the programs’ semantic information. Moreover, some other existing defect prediction approaches learned semantic features from source code to build classifiers directly. However, they did not consider the distribution divergence between source and target projects. To address these limitations, we put forward a new method called Adversarial Discriminative Convolutional Neural Network (ADCNN). It can extract the transferable semantic features from source code for CPDP tasks. Specifically, we first parse source files into token vectors and then map them to integer vectors via word embedding. Second, we combine adversarial learning with discriminative feature learning to train the ADCNN model. The key of the ADCNN model is to learn the discriminative mapping of the target project to the source feature space by deceiving a domain discriminator. A domain discriminator tries to distinguish the target project files from the source project files. Finally, we use the extracted transferable semantic features to build a classifier for CPDP tasks. We evaluate our method on ten benchmark projects in terms of F-measure, AUC, and PofB20 (an effort-aware evaluation metric). The experimental results demonstrate that our ADCNN method performs better compared with other related CPDP methods.

Adversarial Learning for Cross-Project Semi-Supervised Defect Prediction

Cross-project defect prediction (CPDP) aims to build a prediction model on existing source projects and predict the labels of target project. The data distribution difference between different projects makes CPDP very challenging. Besides, most existing CPDP methods usually require sufficient and labeled data. However, acquiring lots of labeled data for a new project is difficult while obtaining the unlabeled data is relatively easy. A desirable approach is building a prediction model on unlabeled data and labeled data. CPDP in this scenario is called cross-project semi-supervised defect prediction (CSDP). Recently, generative adversarial networks have achieved impressive results with these strong ability of learning data distribution and discriminative representation. For effectively learning the discriminative features of data from different projects, we propose a Discriminative Adversarial Feature Learning (DAFL) approach for CSDP. DAFL consists of feature transformer and project discriminator, which compete with each other. A feature transformer tries to generate feature representation, which learns the discriminant information and preserves intrinsic structure inferred from both labeled and unlabeled data. A project discriminator tries to discriminate source and target instances on the generated representation. Experiments on 16 projects show that DAFL performs significantly better than baselines.

ALTRA: Cross-Project Software Defect Prediction via Active Learning and Tradaboost

Cross-project defect prediction (CPDP) methods can be used when the target project is a new project or lacks enough labeled program modules. In these new target projects, we can easily extract and then measure these modules with software measurement tools. However, labeling these program modules is time-consuming, error-prone and requires professional domain knowledge. Moreover, directly using labeled modules in the other projects (i.e., the source projects) can not achieve satisfactory performance due to the large data distribution difference in most cases. In this article, to our best knowledge, we are the first to propose a novel method ALTRA, which can utilize both active learning and TrAdaBoost to alleviate this issue. In particular, we firstly use Burak filter to select similar labeled modules from the source project after analyzing the unlabeled modules in the target project. Then we use active learning to choose representative unlabeled modules from the target project and ask experts to label the type (i.e., defective or non-defective) of these modules. Later, we use TrAdaBoost to determine the weights of labeled modules in the source project and the target project, and then construct the model via weighted support vector machine. After selecting a small number of modules (i.e., only 5% modules) in the target project, we terminate the method ALTRA and return the final constructed model. To show the effectiveness of our proposed method ALTRA, we choose 10 large-scale open-source projects from different application domains. In terms of both F1 and AUC performance indicators, we find ALTRA can perform significantly better than seven state-of-the-art CPDP baselines. Moreover, we also show that the usage of Burak filter, the uncertainty active learning strategy, the class imbalanced learning method and TrAdaBoost are competitive in our proposed method ALTRA.

Heterogeneous cross-project defect prediction with multiple source projects based on transfer learning.

Cross-project defect prediction (CPDP) aims to predict the defect proneness of target project with the defect data of source project. Existing CPDP methods are based on the assumption that source and target projects should have the same metrics. Heterogeneous cross-project defect prediction (HCPDP) builds a prediction model using heterogeneous source and target projects. Existing HCPDP methods just focus on one source project or multiple source projects with the same metrics. These methods limit the scope of getting the source project. In this paper, we propose Heterogeneous Defect Prediction with Multiple source projects (HDPM) which can use multiple heterogeneous source projects for defect prediction. HDPM based on transfer learning which can learn knowledge from one domain and use it to help with other domain. HDPM constructs a projective matrix between heterogeneous source and target projects to make the distributions of source and target projects similar. We conduct experiments on 14 projects from four public datasets and the results show that HDPM can achieve better performance compared with existing CPDP methods, and outperforms or is comparable to within-project defect prediction method. The use of multiple heterogeneous source projects for defect prediction can effectively extend the data acquisition range of defect prediction and make software defect prediction better applied to software engineering.

An Investigation of Imbalanced Ensemble Learning Methods for Cross-Project Defect Prediction

Machine-learning-based software defect prediction (SDP) methods are receiving great attention from the researchers of intelligent software engineering. Most existing SDP methods are performed under a within-project setting. However, there usually is little to no within-project training data to learn an available supervised prediction model for a new SDP task. Therefore, cross-project defect prediction (CPDP), which uses labeled data of source projects to learn a defect predictor for a target project, was proposed as a practical SDP solution. In real CPDP tasks, the class imbalance problem is ubiquitous and has a great impact on performance of the CPDP models. Unlike previous studies that focus on subsampling and individual methods, this study investigated 15 imbalanced learning methods for CPDP tasks, especially for assessing the effectiveness of imbalanced ensemble learning (IEL) methods. We evaluated the 15 methods by extensive experiments on 31 open-source projects derived from five datasets. Through analyzing a total of 37504 results, we found that in most cases, the IEL method that combined under-sampling and bagging approaches will be more effective than the other investigated methods.

Tackling Imbalanced Class on Cross-Project Defect Prediction Using Ensemble SMOTE

The dataset with imbalanced class can reduce the performance of the classifiers. In this study proposed a cross-project software defect prediction model that applies the SMOTE (Synthetic Minority Oversampling Technique) to balance classes in datasets and ensembles technique to reduce misclassification. The ensemble technique using AdaBoost and Bagging algorithms. The results of the study show that the model that integrates SMOTE and Bagging provides better performance. The proposed model can find more software defects and more precise.

Ensemble Undersampling to Handle Unbalanced Class on Cross-Project Defect Prediction

There has been much research which proposed for cross-project software defect prediction models but no models that perform very well with various datasets in general. Software defect dataset usually imbalanced because it contains far more the not defected modules than the defected modules. Class imbalances in the dataset can reduce the performance of classifiers in the software defect prediction model. In this study proposed a Random Undersampling algorithm to balance classes and ensemble techniques to reduce misclassification. The ensemble technique used is the AdaBoost and Bagging algorithm. The results showed that the software defect prediction model that integrates the Random Undersampling algorithm and AdaBoost provides better performance and can find more defects than other models.

Joint distribution matching model for distribution–adaptation‐based cross‐project defect prediction

Using classification methods to predict software defect is receiving a great deal of attention and most of the existing studies primarily conduct prediction under the within-project setting. However, there usually had no or very limited labelled data to train an effective prediction model at an early phase of the software lifecycle. Thus, cross-project defect prediction (CPDP) is proposed as an alternative solution, which is learning a defect predictor for a target project by using labelled data from a source project. Differing from previous CPDP methods that mainly apply instances selection and classifiers adjustment to improve the performance, in this study, the authors put forward a novel distribution–adaptation-based CPDP approach, joint distribution matching (JDM). Specifically, JDM aims to minimise the joint distribution divergence between the source and target project to improve the CPDP performance. By constructing an adaptive weight vector for the instances of the source project, JDM can be effective and robust at reducing marginal distribution discrepancy and conditional distribution discrepancy simultaneously. Extensive experiments verify that JDM can outperform related distribution–adaptation-based methods on 15 open-source projects that are derived from two types of repositories.

Substantiation of multinomial classification using ensemble learning approach

The software defect prediction is the most operative research domain in software engineering as it enhances its reliability. The availability of defect data related to different projects leads to cross project defect prediction an open issue. This paper instanced on multiclass/multinomial classification of defect prediction on different categories of cross projects. The ensemble learning statistical models – Random forest and Gradient Boosting are used for classification. An empirical study is carried out to determine the predictive performance of the within project and cross project prediction models. Depending on the number of defects, class level information is classified into one of three defined multiclass. The homogeneous set of object oriented metrics is used for training the model. Furthermore, k-fold cross validation is done to evaluate the training accuracy of the statistical models. Major outcome of the paper concludes that multinomial/multiclass classification is applicable on cross project data and has comparable results to within project defect data with statistical significance.

Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning

Defect prediction assists the rational allocation of testing resources by detecting the potentially defective software modules before releasing products. When a project has no historical labeled defect data, cross project defect prediction (CPDP) is an alternative technique for this scenario. CPDP utilizes labeled defect data of an external project to construct a classification model to predict the module labels of the current project. Transfer learning based CPDP methods are the current mainstream. In general, such methods aim to minimize the distribution differences between the data of the two projects. However, previous methods mainly focus on the marginal distribution difference but ignore the conditional distribution difference, which will lead to unsatisfactory performance. In this work, we use a novel balanced distribution adaptation (BDA) based transfer learning method to narrow this gap. BDA simultaneously considers the two kinds of distribution differences and adaptively assigns different weights to them. To evaluate the effectiveness of BDA for CPDP performance, we conduct experiments on 18 projects from four datasets using six indicators (i.e., F-measure, g-means, Balance, AUC, EARecall, and EAF-measure). Compared with 12 baseline methods, BDA achieves average improvements of 23.8%, 12.5%, 11.5%, 4.7%, 34.2%, and 33.7% in terms of the six indicators respectively over four datasets.