Cross-company Defect Prediction Research Articles

Research on Cross-Company Defect Prediction Method to Improve Software Security

As the scale and complexity of software increase, software security issues have become the focus of society. Software defect prediction (SDP) is an important means to assist developers in discovering and repairing potential defects that may endanger software security in advance and improving software security and reliability. Currently, cross-project defect prediction (CPDP) and cross-company defect prediction (CCDP) are widely studied to improve the defect prediction performance, but there are still problems such as inconsistent metrics and large differences in data distribution between source and target projects. Therefore, a new CCDP method based on metric matching and sample weight setting is proposed in this study. First, a clustering-based metric matching method is proposed. The multigranularity metric feature vector is extracted to unify the metric dimension while maximally retaining the information contained in the metrics. Then use metric clustering to eliminate metric redundancy and extract representative metrics through principal component analysis (PCA) to support one-to-one metric matching. This strategy not only solves the metric inconsistent and redundancy problem but also transforms the cross-company heterogeneous defect prediction problem into a homogeneous problem. Second, a sample weight setting method is proposed to transform the source data distribution. Wherein the statistical source sample frequency information is set as an impact factor to increase the weight of source samples that are more similar to the target samples, which improves the data distribution similarity between the source and target projects, thereby building a more accurate prediction model. Finally, after the above two-step processing, some classical machine learning methods are applied to build the prediction model, and 12 project datasets in NASA and PROMISE are used for performance comparison. Experimental results prove that the proposed method has superior prediction performance over other mainstream CCDP methods.

Defect prediction model of static code features for cross-company and cross-project software

Software project metrics are seen needless in software industries but they are useful when some unacceptable situations come in the project (Satapathy et al., Proceedings of the 48th annual convention of CSI, vol 2, 2013). Mainly the focus of various defect prediction studies is to build prediction models using the regional data available within the company. So companies maintain a data repository where data of their past projects can be stored which can be used for defect prediction in the future. However, many companies do not follow this practice. In software engineering, the crucial task is Defect prediction. In this paper, a binary defect prediction model was built and examined if there is any conclusion or not. This paper presents the assets of cross-company and within-company data against software defect prediction. Neural network approach has been used to prepare the model for defect prediction. Further, this paper compares the results of with-in and cross-company defect prediction models. To analyse the results for with-in company two versions of Firefox (i.e. 2.0 and 3.0) were considered; for cross project one version of Mozila Sea Monkey (1.0.1); for cross-company validation one version of LICQ were considered. Main focus of the study is to analyse the behavior or role of software metrics for acceptable level of defect prediction.

Cross-company defect prediction via semi-supervised clustering-based data filtering and MSTrA-based transfer learning

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to a target company. Unfortunately, larger irrelevant cross-company (CC) data usually make it difficult to build a prediction model with high performance. On the other hand, brute force leveraging of CC data poorly related to within-company data may decrease the prediction model performance. To address such issues, we aim to provide an effective solution for CCDP. First, we propose a novel semi-supervised clustering-based data filtering method (i.e., SSDBSCAN filter) to filter out irrelevant CC data. Second, based on the filtered CC data, we for the first time introduce multi-source TrAdaBoost algorithm, an effective transfer learning method, into CCDP to import knowledge not from one but from multiple sources to avoid negative transfer. Experiments on 15 public datasets indicate that: (1) our proposed SSDBSCAN filter achieves better overall performance than compared data filtering methods; (2) our proposed CCDP approach achieves the best overall performance among all tested CCDP approaches; and (3) our proposed CCDP approach performs significantly better than with-company defect prediction models.

Improving Cross-Company Defect Prediction with Data Filtering

Defect prediction aims to estimate software reliability via learning from historical defect data. Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to the target company. Unfortunately, larger irrelevant cross-company (CC) data usually makes it difficult to build a CCDP model with high performance. To address such issues, this paper proposes a data filtering method based on agglomerative clustering (DFAC) for CCDP. First, DFAC combines within-company (WC) instances and CC instances and uses agglomerative clustering algorithm to group these instances. Second, DFAC selects subclusters which consist of at least one WC instance, and collects the CC instances in the selected subclusters into a new CC data. Compared with existing data filter methods, the experiment results from 15 public PROMISE datasets show that DFAC increases the pd value, reduces the pf value and achieves higher [Formula: see text]-measure value.

A feature matching and transfer approach for cross-company defect prediction

Abstract Software defect prediction has drawn much attention of researchers in software engineering. Traditional defect prediction methods aim to build the prediction model based on historical data. For a new project or a project with limited historical data, we cannot build a good prediction model. Therefore, researchers have proposed the cross-project defect prediction (CPDP) and cross-company defect prediction (CCDP) methods to share the historical data among different projects. However, the features of cross-company datasets are often heterogeneous, which may affect the feasibility of CCDP. To address the heterogeneous features of CCDP, this paper presents a feature matching and transfer (FMT) approach. First, we conduct feature selection for the source project and get the distribution curves of selected features. Similarly, we also get the distribution curves of all features in the target project. Second, according to the ‘distance’ of different distribution curves, we design a feature matching algorithm to convert the heterogeneous features into the matched features. Finally, we can achieve feature transfer from the source project to the target project. All experiments are conducted on 16 datasets from NASA and PROMISE, and the results show that FMT is effective for CCDP.

Balancing Privacy and Utility in Cross-Company Defect Prediction

Background: Cross-company defect prediction (CCDP) is a field of study where an organization lacking enough local data can use data from other organizations for building defect predictors. To support CCDP, data must be shared. Such shared data must be privatized, but that privatization could severely damage the utility of the data. Aim: To enable effective defect prediction from shared data while preserving privacy. Method: We explore privatization algorithms that maintain class boundaries in a dataset. CLIFF is an instance pruner that deletes irrelevant examples. MORPH is a data mutator that moves the data a random distance, taking care not to cross class boundaries. CLIFF+MORPH are tested in a CCDP study among 10 defect datasets from the PROMISE data repository. Results: We find: 1) The CLIFFed+MORPHed algorithms provide more privacy than the state-of-the-art privacy algorithms; 2) in terms of utility measured by defect prediction, we find that CLIFF+MORPH performs significantly better. Conclusions: For the OO defect data studied here, data can be privatized and shared without a significant degradation in utility. To the best of our knowledge, this is the first published result where privatization does not compromise defect prediction.

Transfer learning for cross-company software defect prediction

Context Software defect prediction studies usually built models using within-company data, but very few focused on the prediction models trained with cross-company data. It is difficult to employ these models which are built on the within-company data in practice, because of the lack of these local data repositories. Recently, transfer learning has attracted more and more attention for building classifier in target domain using the data from related source domain. It is very useful in cases when distributions of training and test instances differ, but is it appropriate for cross-company software defect prediction? Objective In this paper, we consider the cross-company defect prediction scenario where source and target data are drawn from different companies. In order to harness cross company data, we try to exploit the transfer learning method to build faster and highly effective prediction model. Method Unlike the prior works selecting training data which are similar from the test data, we proposed a novel algorithm called Transfer Naive Bayes (TNB), by using the information of all the proper features in training data. Our solution estimates the distribution of the test data, and transfers cross-company data information into the weights of the training data. On these weighted data, the defect prediction model is built. Results This article presents a theoretical analysis for the comparative methods, and shows the experiment results on the data sets from different organizations. It indicates that TNB is more accurate in terms of AUC (The area under the receiver operating characteristic curve), within less runtime than the state of the art methods. Conclusion It is concluded that when there are too few local training data to train good classifiers, the useful knowledge from different-distribution training data on feature level may help. We are optimistic that our transfer learning method can guide optimal resource allocation strategies, which may reduce software testing cost and increase effectiveness of software testing process.