Empirical Study of Software Defect Prediction: A Systematic Mapping

Sơn, Lê Hoàng; Pritam, Nakul; Khari, Manju; Kumar, Raghvendra; Phuong, Pham Thi Minh; Thong, Pham Huy

doi:10.3390/sym11020212

Cited by 77 publications

(42 citation statements)

References 154 publications

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“…The usage of machine learning algorithms has increased in the last decade and is still one of the most popular methods for defect prediction [51,52]. Challagulla et al [53] conducted an empirical assessment to evaluate the performance of various machine learning techniques and statistical models for predicting software quality.…”

Section: Related Workmentioning

confidence: 99%

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

et al. 2020

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Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its own strengths and weaknesses. The main idea of the proposed approach is to develop expert and robust heterogeneous classification models. Two versions of the proposed approach are developed and experimented. The first is based on simple classifiers, and the second is based on ensemble ones. For evaluation, 21 publicly available benchmark datasets are selected to conduct the experiments and benchmark the proposed approach. The evaluation results show the superiority of the ensemble version over other well-regarded basic and ensemble classifiers.

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Section: Related Workmentioning

confidence: 99%

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

et al. 2020

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“…They reported that CPDP achieved more successful results than WPDP and in WPDP polynomial kernel of SVM got better results while in CPDP linear kernel have achieved higher AUC.The AUC values were increased in all SVM functions when hyper-parameter optimization was applied. Son et al [35] conducted a systematic mapping in which he selected 98 studies out of 156 regarding defect prediction. They have reported that the most studied searchbased techniques used in defect prediction are the Artificial Immune Recognition System (AIRS), Ant Colony Optimization (ACO), Genetic Programming (GP), Evolutionary Programming (EP), Evolutionary Subgroup Discovery (ESD), GA, and Gene Expression Programming (GeP) and Particle Swarm Optimization (PSO).…”

Section: Related Workmentioning

confidence: 99%

Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

et al. 2020

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Software testing is an important task in software development activities, and it requires most of the resources, namely, time, cost and effort. To minimize this fatigue, software bug prediction (SBP) models are applied to improve the software quality assurance (SQA) processes by predicting buggy components. The bug prediction models use machine learning classifiers so that bugs can be predicted in software components in some software metrics. These classifiers are characterized by some configurable parameters, called hyperparameters that need to be optimized to ensure better performance. Many methods have been proposed by researchers to predict the defective components but these classifiers sometimes not perform well when default settings are used for machine learning classifiers. In this paper, software bug prediction model is proposed which uses machine learning classifiers in conjunction with the Artificial Immune Network (AIN) to improve bug prediction accuracy through its hyper-parameter optimization. For this purpose, seven machine learning classifiers, such as support vector machine Radial base function (SVM-RBF), K-nearest neighbor (KNN) (Minkowski metric), KNN (Euclidean metric), Naive Bayes (NB), Decision Tree (DT), Linear discriminate analysis (LDA), Random forest (RF) and adaptive boosting (AdaBoost), were used. The experiment was carried out on bug prediction dataset. The results showed that hyper-parameter optimization of machine learning classifiers, using AIN and its applications for software bug prediction, performed better than when classifiers with their default hyper-parameters were used. INDEX TERMS Artificial immune network (AIN), artificial immune system (AIS), hyper-parameter optimization, optimized artificial immune network (opt-aiNet), software bug prediction (SBP).

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“…One of the techniques that are used for optimization and prediction is called Genetics algorithm, and it is defined by Reference 15 as “a problem‐solving algorithm that uses genetics as a model of problem‐solving, also It's a search technique to find approximate solutions to optimization and search problems”. According to Son et al, 16 genetics algorithms have been studied once in the domain of fault prediction. Generally, genetics algorithms and genetic programming are rarely used in software engineering to predict faults, see for example References 17‐22.…”

Section: Introductionmentioning

confidence: 99%

“…A recent study showed that classification techniques are commonly used for defect prediction, 16 see Figure 1. Classification techniques for software fault prediction work to classify software modules into a faulty or non‐faulty module, and this type of prediction does not offer enough logistics to effectively distribute the required resources.…”

Section: Introductionmentioning

confidence: 99%

Software fault prediction using Whale algorithm with genetics algorithm

Alsghaier

Akour

2020

Softw Pract Exp

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Software fault prediction became an essential research area in the last few years, there are many prediction and optimization techniques that have been developed for fault prediction. In this paper, an approach is developed by integrating genetics algorithm with support vector machine (SVM) classifier and Whale optimization algorithm for software fault prediction. The developed approach is applied to 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset, and Java open source projects are considered as a small‐scale dataset. Results indicate that integrating Genetics algorithm with SVM and Whale algorithm improves the performance of the software fault prediction process when it is applied to large‐scale and small‐scale datasets and overcome the limitations that appeared in the previous studies.

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Empirical Study of Software Defect Prediction: A Systematic Mapping

Cited by 77 publications

References 154 publications

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

Software fault prediction using Whale algorithm with genetics algorithm

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