Feature Selection in Cross-Project Software Defect Prediction

Software: Practice and Experience, 2011

The selection of software metrics for building software quality prediction models is a search-based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault-proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real-world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85% of the software metrics were eliminated.

International Journal of Embedded Systems

Software is a collection of computer programs written in a programming language. Software contains various modules which make it a complex entity and it can increase the defect probability at the time of development of the modules. In turn, cost and time to develop the software can be increased. Sometimes, these defects can lead to failure of entire software. It will lead to untimely delivery of the software to the customer. This untimely delivery can responsible for withdrawal or cancellation of project in future. Hence, in this research work, some machine learning algorithms are applied to ensure timely delivery and prediction of defects. Further, several feature selection techniques are also adopted to determine relevant features for defect prediction.

International Journal of Advanced Science and Technology, 2014

Software is a complex entity composed in various modules with varied range of defect occurrence possibility. Efficient and timely prediction of defect occurrence in software allows software project managers to effectively utilize people, cost, time for better quality assurance. The presence of defects in a software leads to a poor quality software and also responsible for the failure of a software project. Sometime it is not possible to identify the defects and fixing them at the time of development and it is required to handle such defects any time whenever they are noticed by the team members. So it is important to predict defect-prone software modules prior to deployment of software project in order to plan better maintenance strategy. Early knowledge of defect prone software module can also help to make efficient process improvement plan within justified period of time and cost. This can further lead to better software release as well as high customer satisfaction subsequently. Accurate measurement and prediction of defect is a crucial issue in any software because it is an indirect measurement and is based on several metrics. Therefore, instead of considering all the metrics, it would be more appropriate to find out a suitable set of metrics which are relevant and significant for prediction of defects in any software modules. This paper proposes a feature selection based Linear Twin Support Vector Machine (LSTSVM) model to predict defect prone software modules. F-score, a feature selection technique, is used to determine the significant metrics set which are prominently affecting the defect prediction in a software modules. The efficiency of predictive model could be enhanced with reduced metrics set obtained after feature selection and further used to identify defective modules in a given set of inputs. This paper evaluates the performance of proposed model and compares it against other existing machine learning models. The experiment has been performed on four PROMISE software engineering repository datasets. The experimental results indicate the effectiveness of the proposed feature selection based LSTSVM predictive model on the basis standard performance evaluation parameters.

IEEE Access

In the traditional software defect prediction methodology, the historical record (dataset) of the same project is partitioned into training and testing data. In a practical situation where the project to be predicted is new, traditional software defect prediction cannot be employed. An alternative method is cross-project defect prediction, where the historical record of one project (source) is used to predict the defect status of another project (target). The cross-project defect prediction method solves the limitations of the historical records in the traditional software defect prediction method. However, the performance of cross-project defect prediction is relatively low because of the distribution differences between the source and target projects. Furthermore, the software defect dataset used for cross-project defect prediction is characterized by high-dimensional features, some of which are irrelevant and contribute to low performance. To resolve these two issues, this study proposes a transformation and feature selection approach to reduce the distribution difference and high-dimensional features in cross-project defect prediction. A comparative experiment was conducted on publicly available datasets from the AEEEM. Analysis of the results obtained shows that the proposed approach in conjugation with random forest as the classification model outperformed the other four state-of-the-art cross-project defect prediction methods based on the commonly used performance evaluation metric F1_score.

Second International Conference in Software and …, 2007

Abstract: Defect prediction is an important topic in software quality research. Statistical models for defect prediction can be built on project repositories. Project repositories store software metrics and defect information. This information is then matched with software modules. Naïve ...

Symmetry

Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and many FS methods have been proposed in the context of software defect prediction (SDP). Moreover, many empirical studies on the impact and effectiveness of FS methods on SDP models often lead to contradictory experimental results and inconsistent findings. These contradictions can be attributed to relative study limitations such as small datasets, limited FS search methods, and unsuitable prediction models in the respective scope of studies. It is hence critical to conduct an extensive empirical study to address these contradictions to guide researchers and buttress the scientific tenacity of experimental conclusions. In this study, we investigated the impact of 46 FS methods using Naïve Bayes and Decision Tree classifiers over 25 software defect datasets from 4 software repositories (NASA, PROMISE, ReLink, and AEEEM). The ensuing prediction models were evaluated based on accuracy and AUC va...

Journal of Systems Engineering and Electronics, 2021

Software defect prediction (SDP) is used to perform the statistical analysis of historical defect data to find out the distribution rule of historical defects, so as to effectively predict defects in the new software. However, there are redundant and irrelevant features in the software defect datasets affecting the performance of defect predictors. In order to identify and remove the redundant and irrelevant features in software defect datasets, we propose ReliefF-based clustering (RFC), a clusterbased feature selection algorithm. Then, the correlation between features is calculated based on the symmetric uncertainty. According to the correlation degree, RFC partitions features into k clusters based on the k-medoids algorithm, and finally selects the representative features from each cluster to form the final feature subset. In the experiments, we compare the proposed RFC with classical feature selection algorithms on nine National Aeronautics and Space Administration (NASA) software defect prediction datasets in terms of area under curve (AUC) and Fvalue. The experimental results show that RFC can effectively improve the performance of SDP.

2020

Feature selection is a technique used to select an optimal feature subset from the original input features according to a specific criterion. The criterion is often formulated as an objective function that finds which features are most appropriate for some tasks at hand. The reason why it is interesting to find a subset of features is because that it always easier to solve a problem in a lower dimension. This helps in understanding the nonlinear mapping between input and output variables. This paper reviewed the basic Feature Selection Techniques for Software Defect Prediction Model and their domain applications. The Subsets selection are categorized into three distinct models and are discussed in a concise form to provide young researchers with the general methods of Subset Selection. Support Vector Machine with Recursive Feature Elimination for both Logistic Regression and Random Forest was introduced to evaluate the performance between filter, wrapper, and embedded feature select...

International Journal on Electrical Engineering and Informatics

Software engineering activities comprise of several activities to ensure that the quality product will be achieved at the end. Some of these activities are software testing, inspection, formal verification and software defect prediction. Many researchers have been developed several models for defect prediction. These models are based on machine learning techniques and statistical analysis techniques. The main objective of these models are to identify the defects before the delivery of the software to the end user. This prediction helps project managers to effectively utilize the resources for better quality assurance. Sometimes, a single defect can cause the entire system failure and most of the time they drop the quality of the software system drastically. Early identification of defects can also help to make a better process plan which can handle the defects effectively and increase the customer satisfaction level. But the accurate prediction of defects in software is not an easy task because this is an indirect measure. Therefore, it is important to find suitable and significant measures which are most relevant for finding the defects in the software system. This paper presents a feature selection based model to predict the defects in a given software module. The most relevant features are extracted from all features with the help of seven feature selection techniques and eight classifiers are used to classify the modules. Six NASA software engineering defects prediction data sets are used in this work. Several performance parameters are also calculated for measuring the performance and validation of this work and the results of the experiments revealed that the proposed model has more capability to predict the software defects.

International Journal of Computer Applications, 2014

Feature subset selection is the process of choosing a subset of good features with respect to the target concept. A clustering based feature subset selection algorithm has been applied over software defect prediction data sets. Software defect prediction domain has been chosen due to the growing importance of maintaining high reliability and high quality for any software being developed. A software quality prediction model is built using software metrics and defect data collected from a previously developed system release or similar software projects. Upon validation of such a model, it could be used for predicting the fault-proneness of program modules that are currently under development. The proposed clustering based algorithm for feature selection uses minimum spanning tree based method to cluster features. And then the algorithm is applied over four different data sets and its impact is analyzed.