Performance Of Software Defect Prediction Research Articles

A Feature Selection Framework for Software Defect Prediction Using ISFLA

Aiming at the problem of feature space dimension reduction and large search space in feature selection of software defect, a defect prediction feature selection framework based on meta-heuristic search algorithm (ISFLA) is proposed. The framework improves generalization of predictions of unknown data samples, enhances the ability to search for features related to learning tasks, and completes further reductions in feature space dimensions. Using some NASA data sets, several common software defect prediction methods and ISFLA simulation experiments were carried out. The experimental results show that the software feature selection framework based on the improved shuffled frog leaping algorithm effectively improves the performance of software defect prediction.

An empirical study on pareto based multi-objective feature selection for software defect prediction

The performance of software defect prediction (SDP) models depend on the quality of considered software features. Redundant features and irrelevant features may reduce the performance of the constructed models, which require feature selection methods to identify and remove them. Previous studies mostly treat feature selection as a single objective optimization problem, and multi-objective feature selection for SDP has not been thoroughly investigated. In this paper, we propose a novel method MOFES (Multi-Objective FEature Selection), which takes two optimization objectives into account. One optimization objective is to minimize the number of selected features, this objective is related to the cost analysis of this problem. Another objective is to maximize the performance of the constructed SDP models, this objective is related to the benefit analysis of this problem. MOFES utilizes Pareto based multi-objective optimization algorithms (PMAs) to solve this problem. In our empirical study, we design and conduct experiments on RELINK and PROMISE datasets, which are gathered from real open source projects. Firstly, we analyze the influence of different PMAs on MOFES and find that NSGA-II can achieve the best performance on both datasets. Then, we compare MOFES method with 22 state-of-the-art filter based and wrapper based feature selection methods, and find that MOFES can effectively select fewer but closely related features to construct high-quality models. Moreover, we also analyze the frequently selected features by MOFES, and these findings can be used to provide guidelines on gathering high-quality SDP datasets. Finally, we analyze the computational cost of MOFES and find that MOFES only needs 107 seconds on average.

Multiview Transfer Learning for Software Defect Prediction

Most software defect prediction models usually assume that enough historical training instances with labels are available. Additionally, the training data and the predicted instances should share the same features to ensure the prediction accuracy. However, in practice, there are many datasets with different granularities containing information in different dimensions. Therefore, it is valuable to effectively use the small scale and different dimensions of data as training instances to improve the prediction performance of the model. We propose a heterogeneous data orienting multiview transfer learning for software defect prediction, denoted as MTDP, which can achieve different dimensions and granularities features to automatically learn labels through neural network models. With this multiview transfer method, lots of training instances are provided for software defect prediction model to ensure the effectiveness of training labels. The proposed MTDP method has four main stages: 1) build heterogeneous transfer models; 2) transfer heterogeneous instances to generate quasi-real instances; 3) label quasi-real instances through co-training and then expand the training set; and (4) construct improved software defect prediction models. The experimental results show that the quasi-real instances have similar effects compared with real instances. Moreover, the software defect prediction performance can be improved by introducing the quasi-real instances into the training dataset.

Severity of defect: an optimised prediction

To assure the quality of software an important activity is performed namely software defect prediction (SDP). Historical databases are used to detect software defects using different machine learning techniques. Conversely, there are disadvantages like testing becomes expensive, poor quality and so the product is unreliable for use. This paper classifies the severity of defects by using a method based on optimised neural network (NN). In full search space, a solution is found by many meta-heuristic optimisations and global search ability has been used. Hence, high-quality solutions are finding within a reasonable period of time. SDP performance is improved by the combination of meta-heuristic optimisation methods. For class imbalance problem, meta-heuristic optimisation methods such as genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) are applied. The above method is based on SFLA and the experimental outputs show that it can do better than Leven berg Marquardt based NN system (LM-NN).

Severity of defect: an optimised prediction

To assure the quality of software an important activity is performed namely software defect prediction (SDP). Historical databases are used to detect software defects using different machine learning techniques. Conversely, there are disadvantages like testing becomes expensive, poor quality and so the product is unreliable for use. This paper classifies the severity of defects by using a method based on optimised neural network (NN). In full search space, a solution is found by many meta-heuristic optimisations and global search ability has been used. Hence, high-quality solutions are finding within a reasonable period of time. SDP performance is improved by the combination of meta-heuristic optimisation methods. For class imbalance problem, meta-heuristic optimisation methods such as genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) are applied. The above method is based on SFLA and the experimental outputs show that it can do better than Leven berg Marquardt based NN system (LM-NN).

Ensemble MultiBoost Based on RIPPER Classifier for Prediction of Imbalanced Software Defect Data

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an E nsemble M ultiBoost based on R IPPER classifier for prediction of imbalanced S oftware D efect data, called EMR_SD . Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR_SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm.

Omni-Ensemble Learning (OEL): Utilizing Over-Bagging, Static and Dynamic Ensemble Selection Approaches for Software Defect Prediction

Machine learning methods in software engineering are becoming increasingly important as they can improve quality and testing efficiency by constructing models to predict defects in software modules. The existing datasets for software defect prediction suffer from an imbalance of class distribution which makes the learning problem in such a task harder. In this paper, we propose a novel approach by integrating Over-Bagging, static and dynamic ensemble selection strategies. The proposed method utilizes most of ensemble learning approaches called Omni-Ensemble Learning (OEL). This approach exploits a new Over-Bagging method for class imbalance learning in which the effect of three different methods of assigning weight to training samples is investigated. The proposed method first specifies the best classifiers along with their combiner for all test samples through Genetic Algorithm as the static ensemble selection approach. Then, a subset of the selected classifiers is chosen for each test sample as the dynamic ensemble selection. Our experiments confirm that the proposed OEL can provide better overall performance (in terms of G-mean, balance, and AUC measures) comparing with other six related works and six multiple classifier systems over seven NASA datasets. We generally recommend OEL to improve the performance of software defect prediction and the similar problem based on these experimental results.

TACKLING IMBALANCED CLASS IN SOFTWARE DEFECT PREDICTION USING TWO-STEP CLUSTER BASED RANDOM UNDERSAMPLING AND STACKING TECHNIQUE

The cost of finding and correcting the software defects are high and increases exponentially in the software development. The software defect prediction (SDP) can be used in the early phases to reduce the testing and maintenance time, cost and effort; thus, improves the quality of the software. SDP performance is poor caused by imbalanced class in datasets where defective modules as minority compared to defect-free ones. In this study, we propose the combination of random undersampling based on two-step cluster and stacking technique for improving the accuracy of SDP. In stacking technique, Decision Tree, Logistic Regression and k-Nearest Neighbor are used as base learner while Naive Bayes as stacking model learner. The proposed method is evaluated using nine datasets from NASA metrics data program repository and area under curve (AUC) as main evaluation. Results have indicated that the proposed method yield excellent performance for 5 of 9 datasets (AUC > 0.9). Compared to the prior researches, the proposed method has first position for 3 datasets, second position for 5 datasets and only 1 dataset in third position for AUC value comparison. Therefore, it can be concluded that the proposed method has an impressive and promising result in prediction performance for most datasets compared with prior research performance.

A package-based clustering approach to enhance the accuracy and performance of software defect prediction

To enhance the accuracy and performance, software defect prediction models considering clustering of dataset combine related and similar features to improve the learning process of the model. Here, a clustering approach named package-based clustering has been proposed to group the similar and related parts of software using object oriented classes' relationships and similarities. To segregate software into clusters, it performs textual analysis to identify all object-oriented classes from source codes. Then it uses package information of each class to divide those into clusters. To analyse the performance of the proposed algorithm, linear regression model is used, which learns from clusters of related and similar classes. The experiment has been conducted on eight releases of two open source software, which are Xalan and Ant, and results show that the proposed technique outperforms the existing clustering algorithms those are BorderFlow and the entire system.

A package-based clustering approach to enhance the accuracy and performance of software defect prediction

A package-based clustering approach to enhance the accuracy and performance of software defect prediction

Metaheuristic Optimization based Feature Selection for Software Defect Prediction

Software defect prediction has been an important research topic in the software engineering field, especially to solve the inefficiency and ineffectiveness of existing industrial approach of software testing and reviews. The software defect prediction performance decreases significantly because the data set contains noisy attributes and class imbalance. Feature selection is generally used in machine learning when the learning task involves high- dimensional and noisy attribute datasets. Most of the feature selection algorithms, use local search throughout the entire process, consequently near-optimal to optimal solutions are quiet difficult to be achieved. Metaheuristic optimization can find a solution in the full search space and use a global search ability, significantly increasing the ability of finding high-quality solutions within a reasonable period of time. In this research, we propose the combination of metaheuristic optimization methods and bagging technique for improving the performance of the software defect prediction. Metaherustic optimization methods (genetic algorithm and particle swarm optimization) are applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. Results have indicated that the proposed methods makes an impressive improvement in prediction performance for most classifiers. Based on the comparison result, we conclude that there is no significant difference between particle swarm optimization and genetic algorithm when used as feature selection for most classifiers in software defect prediction.

Genetic Feature Selection for Software Defect Prediction

Recently, software defect prediction is an important research topic in the software engineering field. The accurate prediction of defect prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Software defect data sets have an imbalanced nature with very few defective modules compared to defect-free ones. The software defect prediction performance also decreases significantly because the dataset contains noisy attributes. In this research, we propose the combination of genetic algorithm and bagging technique for improving the performance of the software defect prediction. Genetic algorithm is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the data sets from NASA metric data repository. Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers.

Combining Particle Swarm Optimization based Feature Selection and Bagging Technique for Software Defect Prediction

The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect‐prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Recently, static code attributes are used as defect predictors in software defect prediction research, since they are useful, generalizable, easy to use, and widely used. However, two common aspects of data quality that can affect performance of software defect prediction are class imbalance and noisy attributes. In this research, we propose the combination of particle swarm optimization and bagging technique for improving the accuracy of the software defect prediction. Particle swarm optimization is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the data sets from NASA metric data repository. Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers.