Within-project Defect Prediction Research Articles

An Empirical Study of the Impact of Class Overlap on the Performance and Interpretability of Cross-Version Defect Prediction

The class overlap problem refers to instances from different categories heavily overlapping in the feature space. This issue is one of the challenges in improving the performance of software defect prediction (SDP). Currently, the studies on the impact of class overlap on SDP mainly focused on within-project defect prediction and cross-project defect prediction. Moreover, the existing class overlap instances cleaning methods are not suitable for cross-version defect prediction. In this paper, we propose a class overlap instances cleaning method based on the Ratio of K-nearest neighbors with the Same Label (RKSL). This method removes instances with the abnormal neighbor ratio in the training set. Based on the RKSL method, we investigate the impact of class overlap on the performance and interpretability of the cross-version defect prediction model. The experiment results show that class overlap can affect the performance of cross-version defect prediction models significantly. The RKSL method can handle the class overlap problem in defect datasets, but it may impact the interpretability of models. Through the analysis of feature changes, we consider that class overlap instances cleaning can assist models in identifying more important features.

Cross-Project Defect Prediction Using Transfer Learning with Long Short-Term Memory Networks

With the increasing number of software projects, within-project defect prediction (WPDP) has already been unable to meet the demand, and cross-project defect prediction (CPDP) is playing an increasingly significant role in the area of software engineering. The classic CPDP methods mainly concentrated on applying metric features to predict defects. However, these approaches failed to consider the rich semantic information, which usually contains the relationship between software defects and context. Since traditional methods are unable to exploit this characteristic, their performance is often unsatisfactory. In this paper, a transfer long short-term memory (TLSTM) network model is first proposed. Transfer semantic features are extracted by adding a transfer learning algorithm to the long short-term memory (LSTM) network. Then, the traditional metric features and semantic features are combined for CPDP. First, the abstract syntax trees (AST) are generated based on the source codes. Second, the AST node contents are converted into integer vectors as inputs to the TLSTM model. Then, the semantic features of the program can be extracted by TLSTM. On the other hand, transferable metric features are extracted by transfer component analysis (TCA). Finally, the semantic features and metric features are combined and input into the logical regression (LR) classifier for training. The presented TLSTM model performs better on the f-measure indicator than other machine and deep learning models, according to the outcomes of several open-source projects of the PROMISE repository. The TLSTM model built with a single feature achieves 0.7% and 2.1% improvement on Log4j-1.2 and Xalan-2.7, respectively. When using combined features to train the prediction model, we call this model a transfer long short-term memory for defect prediction (DPTLSTM). DPTLSTM achieves a 2.9% and 5% improvement on Synapse-1.2 and Xerces-1.4.4, respectively. Both prove the superiority of the proposed model on the CPDP task. This is because LSTM capture long-term dependencies in sequence data and extract features that contain source code structure and context information. It can be concluded that: (1) the TLSTM model has the advantage of preserving information, which can better retain the semantic features related to software defects; (2) compared with the CPDP model trained with traditional metric features, the performance of the model can validly enhance by combining semantic features and metric features.

Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction

Heterogeneous defect prediction (HDP) is a significant research topic in cross-project defect prediction (CPDP), due to the inconsistency of metrics used between source and target projects. While most HDP methods aim to improve the performance of models trained on data from one source project, few studies have investigated how the number of source projects affects predictive performance. In this paper, we propose a new multi-source heterogeneous kernel mapping (MSHKM) algorithm to analyze the effects of different numbers of source projects on prediction results. First, we introduce two strategies based on MSHKM for multi-source HDP. To determine the impact of the number of source projects on the predictive performance of the model, we regularly vary the number of source projects in each strategy. Then, we compare the proposed MSHKM with state-of-the-art HDP methods and within-project defect prediction (WPDP) methods, in terms of three common performance measures, using 28 data sets from five widely used projects. Our results demonstrate that, (1) in the multi-source HDP scenario, strategy 2 outperforms strategy 1; (2) for MSHKM, a lower number of source projects leads to better results and performance under strategy 1, while n = 4 is the optimal number under strategy 2; (3) MSHKM performs better than related state-of-the-art HDP methods; and (4) MSHKM outperforms WPDP. In summary, our proposed MSHKM algorithm provides a promising solution for heterogeneous cross-project defect prediction, and our findings suggest that the number of source projects should be carefully selected to achieve optimal predictive performance.

Addressing Noise and Class Imbalance Problems in Heterogeneous Cross-Project Defect Prediction

When a software project either lacks adequate historical data to build a defect prediction (DP) model or is in the initial phases of development, the DP model based on related source project's defect data might be used. This kind of SDP is categorized as heterogeneous cross-project defect prediction (HCPDP). According to a comprehensive literature review, no research has been done in the field of CPDP to deal with noise and class imbalance problem (CIP) at the same time. In this paper, the impact of noise and imbalanced data on the efficiency of the HCPDP and with-in project defect prediction (WPDP) model is examined empirically and conceptually using four different classification algorithms. In addition, CIP is handled using a novel technique known as chunk balancing algorithm (CBA). Ten prediction combinations from three open-source projects are used in the experimental investigation. The findings show that noise in an imbalanced dataset has a significant impact on defect prediction accuracy.

A Hybrid Multiple Models Transfer Approach for Cross-Project Software Defect Prediction

For a new project, it is impossible to get a reliable prediction model because of the lack of sufficient training data. To solve the problem, researchers proposed cross-project defect prediction (CPDP). For CPDP, most researchers focus on how to reduce the distribution difference between training data and test data, and ignore the impact of class imbalance on prediction performance. This paper proposes a hybrid multiple models transfer approach (HMMTA) for cross-project software defect prediction. First, several instances that are most similar to each target project instance are selected from all source projects to form the training data. Second, the same number of instances as that of the defected class are randomly selected from all the non-defect class in each iteration. Next, instances selected from the non-defect classes and all defected class instances are combined to form the training data. Third, the transfer learning method called ETrAdaBoost is used to iteratively construct multiple prediction models. Finally, the prediction models obtained from multiple iterations are integrated by the ensemble learning method to obtain the final prediction model. We evaluate our approach on 53 projects from AEEEM, PROMISE, SOFTLAB and ReLink four defect repositories, and compare it with 10 baseline CPDP approaches. The experimental results show that the prediction performance of our approach significantly outperforms the state-of-the-art CPDP methods. Besides, we also find that our approach has the comparable prediction performance as within-project defect prediction (WPDP) approaches. These experimental results demonstrate the effectiveness of HMMTA approach for CPDP.

An empirical study of Aging Related Bug prediction using Cross Project in Cloud Oriented Software

Cloud oriented applications enable users to increase the scalability of computing infrastructure by using shared computer resources. These applications include the characteristics such as NoSQL database, BigData Analytics, distributed file system and MapReduce architecture which may face issues like software aging due to which ongoing system's performance decreases and failure rate increases. Aging Related Bugs (ARB) are bugs that are caused due to memory leakage, null pointer exception, resource depletion etc. in the ongoing system whose impact can be dangerous, so it’s better to predict them before releasing the software. Manual extraction of ARB reports are common but finding ARBs within thousand of bug reports is challenging. This is the first paper that presents the empirical study to automatically search aging related bug reports through SEARCH_KEYWORD algorithm and implement the ARB prediction in cross project for cloud oriented applications/softwares. To compare the efficacy of the prediction results, With-in Project Defect Prediction (WPDP) of ARBs is also performed. The work is divided in three phases: 1. ARB reports are extracted from the summary/description of bug in bug repository through automatic process. 2. Cross project bug prediction (CPDP) is performed to predict ARB due to limited availability of training data which is not implemented yet in cloud oriented softwares to the best of our knowledge. 3. Machine learning techniques are applied for ARB prediction to build fault prediction models. There is an imbalanced proportion between ARB-prone and ARB-free files, therefore Recall, FPR(False Positive Rate), Balance are used as major performance measures to predict ARBs. Kruskal Wallis Test and Friedman Test, are applied on the prediction results and it is proved that Naive Bayes performed significantly better than other classifiers. The results suggested that CPDP performed better than WPDP of ARBs using machine learning classifiers in cloud oriented datasets.

Towards building a pragmatic cross-project defect prediction model combining non-effort based and effort-based performance measures for a balanced evaluation

• We propose a two-phase transfer boosting-based cross-project prediction model. • The proposed model is assessed using non-effort based and effort-based measures. • Training data weights are based on feature importance and inter-project similarity. • We validate the effectiveness of the proposed model on a large corpus of data. Recent years have witnessed the growing trend in cross-project defect prediction (CPDP), where the training and the testing data come from different projects having different data distributions. Several CPDP methods have been presented in the literature to overcome differences in their distributions, but the majority of the existing approaches have been evaluated considering the availability of unlimited inspection effort, which is practically impossible, thus leading to fallacious conclusions. Further, they focused more on improving Recall over Precision leading to a high probability of false alarm (PF), causing significant wastage of developer's efforts and time. Addressing these issues, we propose a Two-Phase Transfer Boosting (TPTB) model, which aims at improving the performance not only in terms of non-effort based measures (NEBMs) (making a balance between Recall and PF) but also in terms of effort based measures (EBMs), considering the availability of limited inspection effort. To mitigate the distribution differences, the first phase assigns initial weights to the training modules based on the feature distribution and feature importance. The second phase applies the Dynamic Transfer AdaBoost algorithm to build an ensemble classifier to lessen the impact of contradictory training modules. In addition, a sorting strategy is designed to prioritize the modules for further inspection. Statistical results on 62 datasets revealed a better-balanced performance of our TPTB model holistically over NN-filter, ManualDown, EASC, and Cruz model with performance comparable to WPDP (Within-project defect prediction) considering NEBMs. Besides, when considering EBMs together, TPTB showed statistically and practically more balanced performance as compared to ManualUP and Cruz with overall performance comparable to EASC. Our results demonstrate the efficacy of the TPTB model in a practical setting empowering the quality assurance team to predict and prioritize the defective modules allocating limited inspection effort by optimally focusing on highly defective modules.

A three-stage transfer learning framework for multi-source cross-project software defect prediction

Transfer learning techniques have been proved to be effective in the field of Cross-project defect prediction (CPDP). However, some questions still remain. First, the conditional distribution difference between source and target projects has not been considered. Second, facing multiple source projects, most studies only rarely consider the issues of source selection and multi-source data utilization; instead, they use all available projects and merge multi-source data together to obtain one final dataset. To address these issues, in this paper, we propose a three-stage weighting framework for multi-source transfer learning (3SW-MSTL) in CPDP. In stage 1, a source selection strategy is needed to select a suitable number of source projects from all available projects. In stage 2, a transfer technique is applied to minimize marginal differences. In stage 3, a multi-source data utilization scheme that uses conditional distribution information is needed to help guide researchers in the use of multi-source transferred data. First, we have designed five source selection strategies and four multi-source utilization schemes and chosen the best one to be used in stage 1 and 3 in 3SW-MSTL by comparing their influences on prediction performance. Second, to validate the performance of 3SW-MSTL, we compared it with four multi-source and six single-source CPDP methods, a baseline within-project defect prediction (WPDP) method, and two unsupervised methods on the data from 30 widely used open-source projects. Through experiments, bellwether and weighted vote are separately chosen as a source selection strategy and a multi-source utilization scheme used in 3SW-MSTL. And, our results indicate that 3SW-MSTL outperforms four multi-source, six single-source CPDP methods and two unsupervised methods. And, 3SW-MSTL is comparable to the WPDP method. The proposed 3SW-MSTL model is more effective for considering the two issues mentioned before.

An optimized approach for class imbalance problem in heterogeneous cross project defect prediction

Background: In recent studies, Cross Project Defect Prediction (CPDP) has proven to be feasible in software defect prediction. When both the source as well as the target projects have the same metric sets, it is termed as a homogeneous CPDP. Current CPDP strategies are difficult to implement through projects with a variety of different metric sets. Aside from that, training data often has a problem with class imbalance. The number of defective/bug-ridden and non-defective/clean instances of the source class is usually unbalanced. To address this issue, we propose a heterogeneous cross-project defect prediction framework that can predict defects across projects with different metric sets. Methods: To construct a prediction framework between projects with heterogeneous metric sets, our heterogeneous cross project defect prediction approach uses metric selection, metric matching, class imbalance (CIB) learning followed by ensemble modelling. For our study, we have considered six open-source object-oriented projects. Results: The proposed model resolved the class imbalance issue and records the highest recall value of 7.5 with f-score value as 7.4 in comparison with other baseline models. The highest AUC (area under curve) value of 0.86 has also been recorded. K fold cross validation was performed to evaluate the training accuracy of the model. The proposed optimized model was validated using the Wilcoxon signed rank test (WSR) with a significance level of 5% (i.e., P-value=0.05). Conclusions: Our empirical research on these six projects shows that predictions based on our methodology outperform or are statistically comparable to Within-Project Defect Prediction (WPDP) and other heterogeneous CPDP baseline models.

Siamese networks with an online reweighted example for imbalanced data learning

One key challenging problem in data mining and decision-making is to establish a decision support system based on unbalanced datasets. In this study, we propose a novel algorithm to handle unbalanced learning problems that integrates the advantages of Siamese convolutional neural networks (SCNN) and the online reweighted example (ORE) algorithm into a unified method. First, the SCNN model is established for learning and extracting deep feature features at different levels. Second, the ORE algorithm is used to address the problem of data with a class-imbalanced distribution. Compared with baseline approaches, the experimental results show that our proposed method substantially enhances the performance of both within-project defect prediction and cross-project defect prediction.

Just-in-time defect prediction based on AST change embedding

Just-in-time (JIT) defect prediction can help developers quickly identify whether a change is defective or not. The features extracted from changes play an essential role in building an accurate prediction model. In recent years, it has been considered effective to extract the semantic features of software code files by using code representation technology. However, how to extract semantic information from broken changing code snippets is still a challenging problem. We propose a new feature to represent code semantics based on Abstract Syntax Trees (ASTs), called ACE (AST Change Embedding), by comparing the abstract syntax tree of source code before and after a change and extracting AST change sequences, and then mapping them into numeric vectors by using word embedding technology. At the same time, we utilize the gated mechanism to build a gated hierarchical model, called GH-ACE, to combine the traditional manual features and semantic features. We conduct experiments on within-project and cross-project defect prediction tasks and evaluate the effectiveness of our proposed model in non-effort-aware scenarios and effort-aware scenarios. The results show that, on average, our model is 4.0 percent higher than the best baseline method for within-project defect prediction and 2.4 percent higher than the best baseline method for cross-project defect prediction.

Software defect prediction based on nested-stacking and heterogeneous feature selection

Software testing guarantees the delivery of high-quality software products, and software defect prediction (SDP) has become an important part of software testing. Software defect prediction is divided into traditional software defect prediction and just-in-time software defect prediction (JIT-SDP). However, most of the existing software defect prediction frameworks are relatively simplified, which makes it extremely difficult to provide developers with more detailed reference information. To improve the effectiveness of software defect prediction and realize effective software testing resource allocation, this paper proposes a software defect prediction framework based on Nested-Stacking and heterogeneous feature selection. The framework includes three stages: data set preprocessing and feature selection, Nested-Stacking classifier, and model classification performance evaluation. The novel heterogeneous feature selection and nested custom classifiers in the framework can effectively improve the accuracy of software defect prediction. This paper conducts experiments on two software defect data sets (Kamei, PROMISE), and demonstrates the classification performance of the model through two comprehensive evaluation indicators, AUC, and F1-score. The experiment carried out large-scale within-project defect prediction (WPDP) and cross-project defect prediction (CPDP). The results show that the framework proposed in this paper has an excellent classification performance on the two types of software defect data sets, and has been greatly improved compared with the baseline models.

An Empirical Study of Training Data Selection Methods for Ranking-Oriented Cross-Project Defect Prediction.

Ranking-oriented cross-project defect prediction (ROCPDP), which ranks software modules of a new target industrial project based on the predicted defect number or density, has been suggested in the literature. A major concern of ROCPDP is the distribution difference between the source project (aka. within-project) data and target project (aka. cross-project) data, which evidently degrades prediction performance. To investigate the impacts of training data selection methods on the performances of ROCPDP models, we examined the practical effects of nine training data selection methods, including a global filter, which does not filter out any cross-project data. Additionally, the prediction performances of ROCPDP models trained on the filtered cross-project data using the training data selection methods were compared with those of ranking-oriented within-project defect prediction (ROWPDP) models trained on sufficient and limited within-project data. Eleven available defect datasets from the industrial projects were considered and evaluated using two ranking performance measures, i.e., FPA and Norm(Popt). The results showed no statistically significant differences among these nine training data selection methods in terms of FPA and Norm(Popt). The performances of ROCPDP models trained on filtered cross-project data were not comparable with those of ROWPDP models trained on sufficient historical within-project data. However, ROCPDP models trained on filtered cross-project data achieved better performance values than ROWPDP models trained on limited historical within-project data. Therefore, we recommended that software quality teams exploit other project datasets to perform ROCPDP when there is no or limited within-project data.

Analysis and modeling conditional mutual dependency of metrics in software defect prediction using latent variables

Software defect prediction constitutes an important discipline in software development life-cycle. Among the techniques employed in this domain, Naive Bayes (NB) classifier is cited by a large number of researchers for its simple structure and remarkable classification performance notwithstanding the concern of whether it is theoretically justified or not. More concisely, NB is fundamentally built on the strong assumption of conditional independence of attributes, and the major question here is the compliance of software metrics with this assumption. To address this question, we propose a novel framework “MLMNB-SDP” equipped with a statistical hypothesis testing method to detect those software metrics with a significant conditional dependency. MLMNB-SDP is designed to handle conditional dependencies via a single latent variable in a predefined structure which is responsible for preserving the connection between pairs of software metrics when the class variables are instantiated. We evaluate the effectiveness of our approach based on its capability to measure conditional dependency of software metrics and defect prediction performance. For the former one, we employ Conditional Mutual Information (CMI), and for the later one we use three settings for defect prediction; (1) Within-Project Defect-Prediction (WPDP), (2) Cross-Project Defect-Prediction (CPDP), and (3) stratified k-fold cross-validation. Our metrics dependency analysis results indicate that traditional file-level software metrics demonstrate a significant conditional mutual dependency and the application of naive Bayes classifier in this domain is not theoretically acceptable. Our results based on the three settings indicate that MLMNB-SDP improves naive Bayes classifier 5.45% to 75.86% and outperforms well-known benchmark classifiers, i.e., Random Forest and Logistic Regression, regarding a significant increase in Precision, Recall, and F1 Score, Mathew’s Correlation Coefficient (MCC), and area under the ROC curve (AUC) values.

An Empirical Study of Heterogeneous Cross-Project Defect Prediction Using Various Statistical Techniques

Cross-project defect prediction (CPDP) forecasts flaws in a target project through defect prediction models (DPM) trained by defect data of another project. However, CPDP has a prevalent problem (i.e., distinct projects must have identical features to describe themselves). This article emphasizes on heterogeneous CPDP (HCPDP) modeling that does not require same metric set between two applications and builds DPM based on metrics showing comparable distribution in their values for a given pair of datasets. This paper evaluates empirically and theoretically HCPDP modeling, which comprises of three main phases: feature ranking and feature selection, metric matching, and finally, predicting defects in the target application. The research work has been experimented on 13 benchmarked datasets of three open source projects. Results show that performance of HCPDP is very much comparable to baseline within project defect prediction (WPDP) and XG boosting classification model gives best results when used in conjunction with Kendall's method of correlation as compared to other set of classifiers.

Cross-project software defect prediction based on domain adaptation learning and optimization

Software defect prediction (SDP) is very helpful for optimizing the resource allocation of software testing and improving the quality of software products. The cross-project defect prediction (CPDP) model based on machine learning is first learned through the existing training data with sufficient number and defect labels on one project, and then used to predict the defect labels of another new project with insufficient number and fewer labeled data. However, its prediction performance has a large gap compared with the within-project defect prediction (WPDP) model. The main reason is that there are usually differences between the distributions of training data in different software projects, and it has a greater impact on the prediction performance of the CPDP model. To solve this problem, the kernel twin support vector machines (KTSVMs) is used to implement domain adaptation (DA) to match the distributions of training data for different projects. Moreover, KTSVMs with DA function (called DA-KTSVM) is further used as the CPDP model in this paper. Since the parameters of DA-KTSVM have an impact on its predictive performance, these parameters are optimized by an improved quantum particle swarm optimization algorithm (IQPSO), and the optimized DA-KTSVM is called as DA-KTSVMO. In order to confirm the effectiveness of DA-KTSVMO, some experiments are implemented on 17 open source software projects. Experimental results and analysis show that DA-KTSVMO can not only achieve better prediction performance than other CPDP models compared, but also achieve almost the same or better compared performance than WPDP models when the training sample data is sufficient. In addition, DA-KTSVMO can make better use of existing sufficient data knowledge and realize the reuse of defective data to improve the prediction performance of DA-KTSVMO.

Estimation of Target Defect Prediction Coverage in Heterogeneous Cross Software Projects

Heterogeneous cross-project defect prediction (HCPDP) is an evolving area under quality assurance domain which aims to predict defects in a target project that has restricted historical defect data as well as completely non-uniform software metrics from other projects using a model built on another source project. The article discusses a particular source project group's problem of defect prediction coverage (DPC) and also proposes a novel two phase model for addressing this issue in HCPDP. The study has evaluated DPC on 13 benchmarked datasets in three open source software projects. One hundred percent of DPC is achieved with higher defect prediction accuracy for two project group pairs. The issue of partial DPC is found in third prediction pairs and a new strategy is proposed in the research study to overcome this issue. Furthermore, this paper compares HCPDP modeling with reference to with-in project defect prediction (WPDP), both empirically and theoretically, and it is found that the performance of WPDP is highly comparable to HCPDP and gradient boosting method performs best among all three classifiers.

MPT‐embedding: An unsupervised representation learning of code for software defect prediction

AbstractSoftware project defect prediction can help developers allocate debugging resources. Existing software defect prediction models are usually based on machine learning methods, especially deep learning. Deep learning‐based methods tend to build end‐to‐end models that directly use source code‐based abstract syntax trees (ASTs) as input. They do not pay enough attention to the front‐end data representation. In this paper, we propose a new framework to represent source code called multiperspective tree embedding (MPT‐embedding), which is an unsupervised representation learning method. MPT‐embedding parses the nodes of ASTs from multiple perspectives and encodes the structural information of a tree into a vector sequence. Experiments on both cross‐project defect prediction (CPDP) and within‐project defect prediction (WPDP) show that, on average, MPT‐embedding provides improvements over the state‐of‐the‐art method.

Deep Semantic Feature Learning for Software Defect Prediction

Software defect prediction, which predicts defective code regions, can assist developers in finding bugs and prioritizing their testing efforts. Traditional defect prediction features often fail to capture the semantic differences between different programs. This degrades the performance of the prediction models built on these traditional features. Thus, the capability to capture the semantics in programs is required to build accurate prediction models. To bridge the gap between semantics and defect prediction features, we propose leveraging a powerful representation-learning algorithm, deep learning, to learn the semantic representations of programs automatically from source code files and code changes. Specifically, we leverage a deep belief network (DBN) to automatically learn semantic features using token vectors extracted from the programs' abstract syntax trees (AST) (for file-level defect prediction models) and source code changes (for change-level defect prediction models). We examine the effectiveness of our approach on two file-level defect prediction tasks (i.e., file-level within-project defect prediction and file-level cross-project defect prediction) and two change-level defect prediction tasks (i.e., change-level within-project defect prediction and change-level cross-project defect prediction). Our experimental results indicate that the DBN-based semantic features can significantly improve the examined defect prediction tasks. Specifically, the improvements of semantic features against existing traditional features (in F1) range from 2.1 to 41.9 percentage points for file-level within-project defect prediction, from 1.5 to 13.4 percentage points for file-level cross-project defect prediction, from 1.0 to 8.6 percentage points for change-level within-project defect prediction, and from 0.6 to 9.9 percentage points for change-level cross-project defect prediction.

CFPS: Collaborative filtering based source projects selection for cross-project defect prediction

Software defect prediction aims at helping developers allocate existing resources by predicting defect-prone modules prior to the testing phase. In the past decade, cross-project defect prediction (CPDP) have gained more attention than within-project defect prediction (WPDP) as WPDP is usually inefficient with the scarcity of training data due to the absence of historical defect data. Currently most CPDP studies focus on selecting appropriate training instances for improving the performance of defect prediction while few studies pay attention to the selection of appropriate source projects. However, in practice, source projects selection is the basis and prerequisite of training instances selection as an increasing number of open source software defect data are now available. In present study, we propose a Collaborative Filtering based source Projects Selection (CFPS) method for cross-project defect prediction. For a given new project, the similarity between it and each historical project is firstly calculated and thus the corresponding similarity repository could be obtained. Then CFPS mines the applicability among historical projects for constructing an applicability repository. Finally, with the aforementioned applicability and similarity repository, the popular user-based collaborative filtering algorithm is employed to recommend the appropriate source projects for the given new project. In the experiment, we have empirically validated the importance and necessity of selecting appropriate source projects. Furthermore, the experimental results also demonstrate that the proposed CFPS method is feasible and effective.