Software Defect Prediction Datasets Research Articles

DynGRAR: A dynamic approach to mining gradual relational association rules

Relational Association Rules (RARs) capture generic relations between attributes values in possibly large data sets. Due to their ability to uncover underlying semantically relevant patterns, they are of particular interest in data mining research and applicable in both unsupervised and supervised learning scenarios. With the aim of increasing the stability and expressiveness of the classical, non-gradual RARs, Gradual Relational Association Rules (GRARs) have been introduced. By generalizing the boolean relations to gradual relations, GRARs also capture the degrees to which generic relations are satisfied. In the current paper we introduce a new approach called DynGRAR (Dynamic Gradual Relational Association Rules Miner) for uncovering interesting GRARs in dynamic data sets which are incrementally extended with both new data instances and new data attributes. DynGRAR dynamically adjusts the set of all interesting GRARs. Through multiple experiments performed on publicly available software defect prediction data sets, we have evaluated DynGRAR versus applying the standard GRARs mining algorithm from scratch on the extended data. The results obtained emphasize the superior performance of the dynamic approach we propose.

Combining Data Preprocessing Methods With Imputation Techniques for Software Defect Prediction

Software Defect Prediction (SDP) models are used to predict, whether software is clean or buggy using the historical data collected from various software repositories. The data collected from such repositories may contain some missing values. In order to estimate missing values, imputation techniques are used, which utilizes the complete observed values in the dataset. The objective of this study is to identify the best-suited imputation technique for handling missing values in SDP dataset. In addition to identifying the imputation technique, the authors have investigated for the most appropriate combination of imputation technique and data preprocessing method for building SDP model. In this study, four combinations of imputation technique and data preprocessing methods are examined using the improved NASA datasets. These combinations are used along with five different machine-learning algorithms to develop models. The performance of these SDP models are then compared using traditional performance indicators. Experiment results show that among different imputation techniques, linear regression gives the most accurate imputed value. The combination of linear regression with correlation based feature selector outperforms all other combinations. To validate the significance of data preprocessing methods with imputation the findings are applied to open source projects. It was concluded that the result is in consistency with the above conclusion.

Which type of metrics are useful to deal with class imbalance in software defect prediction?

ContextThere are various ways to cope with class imbalance problem which is one of the main issues of software defect prediction. Sampling algorithms are implemented on both industrial and open-source software defect prediction data sets by practitioners to wipe out imbalanced data points. Sampling algorithms, up-to-date, have been employed either static or process code metrics. ObjectiveIn this study, sampling algorithms including Virtual, SMOTE, and HSDD (hybrid sampling for defect data sets) are explored using static code and quality metrics together. Our goal is not only to lead practitioners to decide the type of the metrics in defect prediction but also provide useful information for developers to design less defective software projects. MethodWe ran sampling experiments with three sampling algorithms on ten data sets (from GitHub). Feature selection is applied on large features of the data sets. Using five classifiers, the performance of the data sets after sampling is compared with initial data sets. Regression analyzes are implemented on quality metrics to find the most influential metrics for detecting defect proneness. ResultsRegardless of the type of the sampling, prediction performances are similar. Quality metrics surpassed static code metrics with respect to training times and prediction accuracies. ConclusionUsing quality metrics yields better prediction results rather than static code metrics in imbalanced data sets. As the count of project cloning increases, the number of defects decreases. Thus, approaches, related to the class imbalance, should be evaluated not only in terms of static code metrics but also for quality metrics.

Co-evolutionary multi-population genetic programming for classification in software defect prediction: An empirical case study

Evolving diverse ensembles using genetic programming has recently been proposed for classification problems with unbalanced data. Population diversity is crucial for evolving effective algorithms. Multilevel selection strategies that involve additional colonization and migration operations have shown better performance in some applications. Therefore, in this paper, we are interested in analysing the performance of evolving diverse ensembles using genetic programming for software defect prediction with unbalanced data by using different selection strategies. We use colonization and migration operators along with three ensemble selection strategies for the multi-objective evolutionary algorithm. We compare the performance of the operators for software defect prediction datasets with varying levels of data imbalance. Moreover, to generalize the results, gain a broader view and understand the underlying effects, we replicated the same experiments on UCI datasets, which are often used in the evolutionary computing community. The use of multilevel selection strategies provides reliable results with relatively fast convergence speeds and outperforms the other evolutionary algorithms that are often used in this research area and investigated in this paper. This paper also presented a promising ensemble strategy based on a simple convex hull approach and at the same time it raised the question whether ensemble strategy based on the whole population should also be investigated.

Tackling class overlap and imbalance problems in software defect prediction

Software defect prediction (SDP) is a promising solution to save time and cost in the software testing phase for improving software quality. Numerous machine learning approaches have proven effective in SDP. However, the unbalanced class distribution in SDP datasets could be a problem for some conventional learning methods. In addition, class overlap increases the difficulty for the predictors to learn the defective class accurately. In this study, we propose a new SDP model which combines class overlap reduction and ensemble imbalance learning to improve defect prediction. First, the neighbor cleaning method is applied to remove the overlapping non-defective samples. The whole dataset is then randomly under-sampled several times to generate balanced subsets so that multiple classifiers can be trained on these data. Finally, these individual classifiers are assembled with the AdaBoost mechanism to build the final prediction model. In the experiments, we investigated nine highly unbalanced datasets selected from a public software repository and confirmed that the high rate of overlap between classes existed in SDP data. We assessed the performance of our proposed model by comparing it with other state-of-the-art methods including conventional SDP models, imbalance learning and data cleaning methods. Test results and statistical analysis show that the proposed model provides more reasonable defect prediction results and performs best in terms of G-mean and AUC among all tested models.

Prediction of Defective Software Modules Using Class Imbalance Learning

Software defect predictors are useful to maintain the high quality of software products effectively. The early prediction of defective software modules can help the software developers to allocate the available resources to deliver high quality software products. The objective of software defect prediction system is to find as many defective software modules as possible without affecting the overall performance. The learning process of a software defect predictor is difficult due to the imbalanced distribution of software modules between defective and nondefective classes. Misclassification cost of defective software modules generally incurs much higher cost than the misclassification of nondefective one. Therefore, on considering the misclassification cost issue, we have developed a software defect prediction system using Weighted Least Squares Twin Support Vector Machine (WLSTSVM). This system assigns higher misclassification cost to the data samples of defective classes and lower cost to the data samples of nondefective classes. The experiments on eight software defect prediction datasets have proved the validity of the proposed defect prediction system. The significance of the results has been tested via statistical analysis performed by using nonparametric Wilcoxon signed rank test.

Evaluating Stratification Alternatives to Improve Software Defect Prediction

Numerous studies have applied machine learning to the software defect prediction problem, i.e. predicting which modules will experience a failure during operation based on software metrics. However, skewness in defect-prediction datasets can mean that the resulting classifiers often predict the faulty (minority) class less accurately. This problem is well known in machine learning, and is often referred to as “learning from imbalanced datasets.” One common approach for mitigating skewness is to use stratification to homogenize class distributions; however, it is unclear what stratification techniques are most effective, both generally and specifically in software defect prediction. In this article, we investigate two major stratification alternatives (under-, and over-sampling) for software defect prediction using Analysis of Variance. Our analysis covers several modern software defect prediction datasets using a factorial design. We find that the main effect of under-sampling is significant at α = 0.05, as is the interaction between under- and over-sampling. However, the main effect of over-sampling is not significant.

Implementation of Elliptic Curve Digital Signature Algorithms

Recent studies of software defect prediction typically produce datasets, methods and frameworks which allow software engineers to focus on development activities in terms of defect-prone code, thereby improving software quality and making better use of resources. Many software defect prediction datasets, methods and frameworks are published disparate and complex, thus a comprehensive picture of the current state of defect prediction research that exists is missing. This literature review aims to identify and analyze the research trends, datasets, methods and frameworks used in software defect prediction research betweeen 2000 and 2013. Based on the defined inclusion and exclusion criteria, 71 software defect prediction studies published between January 2000 and December 2013 were remained and selected to be investigated further. This literature review has been undertaken as a systematic literature review. Systematic literature review is defined as a process of identifying, assessing, and interpreting all available research evidence with the purpose to provide answers for specific research questions. Analysis of the selected primary studies revealed that current software defect prediction research focuses on five topics and trends: estimation, association, classification, clustering and dataset analysis. The total distribution of defect prediction methods is as follows. 77.46% of the research studies are related to classification methods, 14.08% of the studies focused on estimation methods, and 1.41% of the studies concerned on clustering and association methods. In addition, 64.79% of the research studies used public datasets and 35.21% of the research studies used private datasets. Nineteen different methods have been applied to predict software defects. From the nineteen methods, seven most applied methods in software defect prediction are identified. Researchers proposed some techniques for improving the accuracy of machine learning classifier for software defect prediction by ensembling some machine learning methods, by using boosting algorithm, by adding feature selection and by using parameter optimization for some classifiers. The results of this research also identified three frameworks that are highly cited and therefore influential in the software defect prediction field. They are Menzies et al. Framework, Lessmann et al. Framework, and Song et al. Framework.