How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction

How Far We Have Progressed in the Journey?An Examination of Cross-Project Defect Prediction YUMING ZHOU,YIBIAO YANG,HONGMIN LU,LIN CHEN,YANHUI LI,and YANGYANG ZHAO,Nanjing University JUNYAN QIAN,Guilin University of Electronic Technology BAOWEN XU,Nanjing University Background.Recent years have seen an increasing interest in cross-project defect prediction(CPDP),which aims to apply defect prediction models built on source projects to a target project.Currently,a variety of (complex)CPDP models have been proposed with a promising prediction performance. Problem.Most,if not all,of the existing CPDP models are not compared against those simple module size models that are easy to implement and have shown a good performance in defect prediction in the literature. Objective.We aim to investigate how far we have really progressed in the journey by comparing the perfor- mance in defect prediction between the existing CPDP models and simple module size models. Method.We first use module size in the target project to build two simple defect prediction models,Manual- Down and ManualUp,which do not require any training data from source projects.ManualDown considers a larger module as more defect-prone,while ManualUp considers a smaller module as more defect-prone. Then,we take the following measures to ensure a fair comparison on the performance in defect prediction between the existing CPDP models and the simple module size models:using the same publicly available data sets,using the same performance indicators,and using the prediction performance reported in the original cross-project defect prediction studies. Result.The simple module size models have a prediction performance comparable or even superior to most of the existing CPDP models in the literature,including many newly proposed models. Conclusion.The results caution us that,if the prediction performance is the goal,the real progress in CPDP is not being achieved as it might have been envisaged.We hence recommend that future studies should include ManualDown/ManualUp as the baseline models for comparison when developing new CPDP models to predict defects in a complete target project. CCS Concepts:.Software and its engineering-Software evolution;Maintaining software; Additional Key Words and Phrases:Defect prediction,cross-project,supervised,unsupervised,model This work is partially supported by the National Key Basic Research and Development Program of China(2014CB340702) and the National Natural Science Foundation of China (61432001,61772259,61472175,61472178,61702256,61562015, 61403187). Authors'addresses:Y.Zhou (corresponding author),Y.Yang.H.Lu,L.Chen,Y.Li,Y.Zhao,and B.Xu(corresponding author), State Key Laboratory for Novel Software Technology,Nanjing University,No.163,Xianlin Road,Nanjing.210023,Jiangsu Province,P.R.China;emails:(zhouyuming.yangyibiao,hmlu,Ichen,yanhuili,bwxu@nju.edu.cn,csurjzhyy@163.com;Y. Qian,Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,541004,Guangxi Province,P.R.China;email:qjy2000@gmail.com. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.Copyrights for components of this work owned by others than ACM must be honored Abstracting with credit is permitted.To copy otherwise,or republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.Request permissions from permissions@acm.org. ©2018ACM1049-331X/2018/04-ART1$15.00 https:/∥doi.org/10.1145/3183339 ACM Transactions on Software Engineering and Methodology,Vol.27.No.1,Article 1.Pub.date:April 2018

1 How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction YUMING ZHOU, YIBIAO YANG, HONGMIN LU, LIN CHEN, YANHUI LI, and YANGYANG ZHAO, Nanjing University JUNYAN QIAN, Guilin University of Electronic Technology BAOWEN XU, Nanjing University Background. Recent years have seen an increasing interest in cross-project defect prediction (CPDP), which aims to apply defect prediction models built on source projects to a target project. Currently, a variety of (complex) CPDP models have been proposed with a promising prediction performance. Problem. Most, if not all, of the existing CPDP models are not compared against those simple module size models that are easy to implement and have shown a good performance in defect prediction in the literature. Objective. We aim to investigate how far we have really progressed in the journey by comparing the perfor￾mance in defect prediction between the existing CPDP models and simple module size models. Method. We first use module size in the target project to build two simple defect prediction models, Manual￾Down and ManualUp, which do not require any training data from source projects. ManualDown considers a larger module as more defect-prone, while ManualUp considers a smaller module as more defect-prone. Then, we take the following measures to ensure a fair comparison on the performance in defect prediction between the existing CPDP models and the simple module size models: using the same publicly available data sets, using the same performance indicators, and using the prediction performance reported in the original cross-project defect prediction studies. Result. The simple module size models have a prediction performance comparable or even superior to most of the existing CPDP models in the literature, including many newly proposed models. Conclusion. The results caution us that, if the prediction performance is the goal, the real progress in CPDP is not being achieved as it might have been envisaged. We hence recommend that future studies should include ManualDown/ManualUp as the baseline models for comparison when developing new CPDP models to predict defects in a complete target project. CCS Concepts: • Software and its engineering → Software evolution; Maintaining software; Additional Key Words and Phrases: Defect prediction, cross-project, supervised, unsupervised, model This work is partially supported by the National Key Basic Research and Development Program of China (2014CB340702) and the National Natural Science Foundation of China (61432001, 61772259, 61472175, 61472178, 61702256, 61562015, 61403187). Authors’ addresses: Y. Zhou (corresponding author), Y. Yang, H. Lu, L. Chen, Y. Li, Y. Zhao, and B. Xu (corresponding author), State Key Laboratory for Novel Software Technology, Nanjing University, No. 163, Xianlin Road, Nanjing, 210023, Jiangsu Province, P.R. China; emails: {zhouyuming, yangyibiao, hmlu, lchen, yanhuili, bwxu}@nju.edu.cn, csurjzhyy@163.com; Y. Qian, Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, Guangxi Province, P.R. China; email: qjy2000@gmail.com. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2018 ACM 1049-331X/2018/04-ART1 $15.00 https://doi.org/10.1145/3183339 ACM Transactions on Software Engineering and Methodology, Vol. 27, No. 1, Article 1. Pub. date: April 2018

1:2 Y.Zhou et al. ACM Reference format: Yuming Zhou,Yibiao Yang,Hongmin Lu,Lin Chen,Yanhui Li,Yangyang Zhao,Junyan Qian,and Baowen Xu.2018.How Far We Have Progressed in the Journey?An Examination of Cross-Project Defect Prediction. ACM Trans.Softw.Eng.Methodol 27,1,Article 1(April 2018),51 pages. https:/doi.org/10.1145/3183339 INTRODUCTION A defect prediction model can predict the defect-proneness of the modules(e.g.,files,classes,or functions)in a software project.Given the prediction result,a project manager can(1)classify the modules into two categories,high defect-prone or low defect-prone,or(2)rank the modules from the highest to lowest defect-proneness.In both scenarios,the project manager could allocate more resources to inspect or test those high defect-prone modules.This will help to find more defects in the project if the model can accurately predict defect-proneness.For a given project, it is common to use the historical project data (e.g.,module-level complexity metric data and defect data in the previous releases of the project)to train the model.Prior studies have shown that the model predicts defects well in the test data if it is trained using a sufficiently large amount of data [23].However,in practice,it might be difficult to obtain sufficient training data [129].This is especially true for a new type of projects or projects with little historical data collected. One way to deal with the shortage of training data is to leverage the data from other projects (i.e.,source projects)to build the model and apply it to predict the defects in the current project (i.e.,the target project)[9].However,in practice,it is challenging to achieve accurate cross-project defect prediction(CPDP)[129].The main reason is that the source and target project data usually exhibit significantly different distributions,which violates the similar distribution assumption of the training and test data required by most modeling techniques [61,80,123].Furthermore,in many cases,the source and target project data even consist of different metric sets,which makes it difficult to use the regular modeling techniques to build and apply the prediction model [17,27, 41,79].In recent years,various techniques have been proposed to address these challenges and a large number of CPDP models have hence been developed(see Section 2.4).In particular,it has been reported that these CPDP models produce a promising prediction performance [27,41,61, 79,80] Yet,most,if not all,of the existing CPDP models are not compared against those simple module size models that are easy to implement and have shown a good performance in defect prediction in the literature.In the past decades,many studies have reported that simple models based on the module size(e.g.,SLOC,source lines of code)in a project can in general predict the defects in the project well [2,49-51,65,102,127,128].In the context of CPDP,we can use module size in a target project to build two simple module size models,ManualDown and ManualUp [49-51,65]. The former considers a larger module as more defect-prone,while the latter considers a smaller module as more defect-prone.Since ManualDown and ManualUp do not require any data from the source projects to build the models,they are free of the challenges on different data distributions/ metric sets of the source and target project data.In particular,they have a low computation cost and are easy to implement.In contrast,due to the use of complex modeling techniques,many existing CPDP models not only have a high computation cost but also involve a large number of parameters needed to be carefully tuned.This imposes substantial barriers to apply them in practice,especially for large projects.Furthermore,previous studies show that module size has a strong confounding effect on the associations between code metrics and defect-proneness [20, 128].Emam et al.even reported that their associations disappeared after controlling for module ACM Transactions on Software Engineering and Methodology,Vol.27,No.1,Article 1.Pub.date:April 2018

1:2 Y. Zhou et al. ACM Reference format: Yuming Zhou, Yibiao Yang, Hongmin Lu, Lin Chen, Yanhui Li, Yangyang Zhao, Junyan Qian, and Baowen Xu. 2018. How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction. ACM Trans. Softw. Eng. Methodol. 27, 1, Article 1 (April 2018), 51 pages. https://doi.org/10.1145/3183339 1 INTRODUCTION A defect prediction model can predict the defect-proneness of the modules (e.g., files, classes, or functions) in a software project. Given the prediction result, a project manager can (1) classify the modules into two categories, high defect-prone or low defect-prone, or (2) rank the modules from the highest to lowest defect-proneness. In both scenarios, the project manager could allocate more resources to inspect or test those high defect-prone modules. This will help to find more defects in the project if the model can accurately predict defect-proneness. For a given project, it is common to use the historical project data (e.g., module-level complexity metric data and defect data in the previous releases of the project) to train the model. Prior studies have shown that the model predicts defects well in the test data if it is trained using a sufficiently large amount of data [23]. However, in practice, it might be difficult to obtain sufficient training data [129]. This is especially true for a new type of projects or projects with little historical data collected. One way to deal with the shortage of training data is to leverage the data from other projects (i.e., source projects) to build the model and apply it to predict the defects in the current project (i.e., the target project) [9]. However, in practice, it is challenging to achieve accurate cross-project defect prediction (CPDP) [129]. The main reason is that the source and target project data usually exhibit significantly different distributions, which violates the similar distribution assumption of the training and test data required by most modeling techniques [61, 80, 123]. Furthermore, in many cases, the source and target project data even consist of different metric sets, which makes it difficult to use the regular modeling techniques to build and apply the prediction model [17, 27, 41, 79]. In recent years, various techniques have been proposed to address these challenges and a large number of CPDP models have hence been developed (see Section 2.4). In particular, it has been reported that these CPDP models produce a promising prediction performance [27, 41, 61, 79, 80]. Yet, most, if not all, of the existing CPDP models are not compared against those simple module size models that are easy to implement and have shown a good performance in defect prediction in the literature. In the past decades, many studies have reported that simple models based on the module size (e.g., SLOC, source lines of code) in a project can in general predict the defects in the project well [2, 49–51, 65, 102, 127, 128]. In the context of CPDP, we can use module size in a target project to build two simple module size models, ManualDown and ManualUp [49–51, 65]. The former considers a larger module as more defect-prone, while the latter considers a smaller module as more defect-prone. Since ManualDown and ManualUp do not require any data from the source projects to build the models, they are free of the challenges on different data distributions/ metric sets of the source and target project data. In particular, they have a low computation cost and are easy to implement. In contrast, due to the use of complex modeling techniques, many existing CPDP models not only have a high computation cost but also involve a large number of parameters needed to be carefully tuned. This imposes substantial barriers to apply them in practice, especially for large projects. Furthermore, previous studies show that module size has a strong confounding effect on the associations between code metrics and defect-proneness [20, 128]. Emam et al. even reported that their associations disappeared after controlling for module ACM Transactions on Software Engineering and Methodology, Vol. 27, No. 1, Article 1. Pub. date: April 2018

How Far We Have Progressed in the Journey?An Examination of CPDP 1:3 size [20].This indicates that module size may have a capability in defect prediction similar to code metrics,the most commonly used predictors in the existing CPDP models.An interesting question hence arises:"If the prediction performance is the goal,then how do the existing CPDP models perform compared with ManualDown and ManualUp?"This question is important for both practitioners and researchers.For practitioners,it will help to determine whether it is worth to apply the existing CPDP models in practice.If simple module size models perform similarly or even better,then there is no practical reason to apply complex CPDP models.For researchers,if simple module size models perform similarly or even better,then there is a strong need to improve the prediction power of the existing CPDP models.Otherwise,the motivation of applying those CPDP models could not be well justified.To the best of our knowledge,of the existing CPDP studies,only two studies compared the proposed CPDP models against simple module size models [9,12].In other words,for most of the existing CPDP models,it remains unclear whether they have a superior prediction performance compared with simple module size models. In this article,we attempt to investigate how far we have really progressed in the journey by conducting a comparison in defect prediction performance between the existing CPDP models and simple module size models.We want to know not only whether the difference in defect prediction performance is statistically significant but also whether it is of practical importance. In our study,we take the following two measures to ensure a fair comparison.On the one hand, we use the same publicly available datasets and the same performance indicators to collect the performance values for the simple module size models as those used in the existing CPDP studies. On the other hand,we do not attempt to re-implement the CPDP models to collect their prediction performance values.In contrast,we use the prediction performance values reported in the original CPDP studies to conduct the comparison.Therefore,the implementation bias can be avoided. These measures ensure that we can draw a reliable conclusion on the benefits of the existing CPDP models w.r.t.simple module size models in defect prediction performance in practice. Under the above experimental settings,we perform an extensive comparison between the existing CPDP models and simple module size models for defect prediction.Surprisingly,our experimental results show that simple module size models have a prediction performance comparable or even superior to most of the existing CPDP models in the literature,including many newly proposed models.Consequently,for practitioners,it would be better to apply simple module size models rather than the existing CPDP models to predict defects in a target project. This is especially true when taking into account the application cost(including metrics collection cost and model building cost).The results reveal that,if the prediction performance is the goal, the current progress in kjjc zCPDP studies is not being achieved as it might have been envisaged We hence strongly recommend that future CPDP studies should consider simple module size models as the baseline models to be compared against.The benefits of using a baseline model are two-fold [112].On the one hand,this would ensure that the predictive power of a newly proposed CPDP model could be adequately compared and assessed.On the other hand,"the ongoing use of a baseline model in the literature would give a single point of comparison".This will allow a meaningful assessment of any new CPDP model against previous CPDP models. The rest of this article is organized as follows.Section 2 introduces the background on cross- project defect prediction,including the problem studied,the general framework,the performance evaluation indicators,and the state of progress.Section 3 describes the experimental design,in- cluding the simple module size model,the research questions,the datasets,and the data analysis method.Section 4 presents in detail the experimental results.Section 5 compares the simple module size models with related work.Section 6 discusses the results and implications.Section 7 analyzes the threats to the validity of our study.Section 8 concludes the article and outlines directions for future work. ACM Transactions on Software Engineering and Methodology.Vol.27.No.1,Article 1.Pub.date:April 2018

How Far We Have Progressed in the Journey? An Examination of CPDP 1:3 size [20]. This indicates that module size may have a capability in defect prediction similar to code metrics, the most commonly used predictors in the existing CPDP models. An interesting question hence arises: “If the prediction performance is the goal, then how do the existing CPDP models perform compared with ManualDown and ManualUp?” This question is important for both practitioners and researchers. For practitioners, it will help to determine whether it is worth to apply the existing CPDP models in practice. If simple module size models perform similarly or even better, then there is no practical reason to apply complex CPDP models. For researchers, if simple module size models perform similarly or even better, then there is a strong need to improve the prediction power of the existing CPDP models. Otherwise, the motivation of applying those CPDP models could not be well justified. To the best of our knowledge, of the existing CPDP studies, only two studies compared the proposed CPDP models against simple module size models [9, 12]. In other words, for most of the existing CPDP models, it remains unclear whether they have a superior prediction performance compared with simple module size models. In this article, we attempt to investigate how far we have really progressed in the journey by conducting a comparison in defect prediction performance between the existing CPDP models and simple module size models. We want to know not only whether the difference in defect prediction performance is statistically significant but also whether it is of practical importance. In our study, we take the following two measures to ensure a fair comparison. On the one hand, we use the same publicly available datasets and the same performance indicators to collect the performance values for the simple module size models as those used in the existing CPDP studies. On the other hand, we do not attempt to re-implement the CPDP models to collect their prediction performance values. In contrast, we use the prediction performance values reported in the original CPDP studies to conduct the comparison. Therefore, the implementation bias can be avoided. These measures ensure that we can draw a reliable conclusion on the benefits of the existing CPDP models w.r.t. simple module size models in defect prediction performance in practice. Under the above experimental settings, we perform an extensive comparison between the existing CPDP models and simple module size models for defect prediction. Surprisingly, our experimental results show that simple module size models have a prediction performance comparable or even superior to most of the existing CPDP models in the literature, including many newly proposed models. Consequently, for practitioners, it would be better to apply simple module size models rather than the existing CPDP models to predict defects in a target project. This is especially true when taking into account the application cost (including metrics collection cost and model building cost). The results reveal that, if the prediction performance is the goal, the current progress in kjjc zCPDP studies is not being achieved as it might have been envisaged. We hence strongly recommend that future CPDP studies should consider simple module size models as the baseline models to be compared against. The benefits of using a baseline model are two-fold [112]. On the one hand, this would ensure that the predictive power of a newly proposed CPDP model could be adequately compared and assessed. On the other hand, “the ongoing use of a baseline model in the literature would give a single point of comparison”. This will allow a meaningful assessment of any new CPDP model against previous CPDP models. The rest of this article is organized as follows. Section 2 introduces the background on cross￾project defect prediction, including the problem studied, the general framework, the performance evaluation indicators, and the state of progress. Section 3 describes the experimental design, in￾cluding the simple module size model, the research questions, the datasets, and the data analysis method. Section 4 presents in detail the experimental results. Section 5 compares the simple module size models with related work. Section 6 discusses the results and implications. Section 7 analyzes the threats to the validity of our study. Section 8 concludes the article and outlines directions for future work. ACM Transactions on Software Engineering and Methodology, Vol. 27, No. 1, Article 1. Pub. date: April 2018

1:4 Y.Zhou et al. 2 CROSS-PROJECT DEFECT PREDICTION In this section,we first describe the problem that cross-project defect prediction aims to address. Then,we present a general framework that depicts the key components in a supervised cross- project defect prediction.After that,we introduce the commonly used performance evaluation indicators involved in the existing CPDP studies.Finally,we give a literature overview of current developments in this area and provide a comparison of the main CPDP studies under the general framework. 2.1 Problem Statement The purpose of cross-project defect prediction is to address the shortage problem of training data that are used for building a model to predict defects in a target release of a target project.To predict defects in the target release,it is common to use a two-phase process(i.e.,model building and ap- plication).At the model-building phase,the metric data and the defect data are first collected from the modules in historical releases(i.e.,the releases before the target release in the target project) Then,based on the collected data,a specific modeling technique is used to train a model to cap- ture the relationships between the metrics and defect-proneness.At the model application phase, the same metrics are first collected from the modules in the target release.Then,the predicted defect-proneness of each module in the target release is obtained by substituting the correspond- ing metric data into the model.After that,the predicted performance is evaluated by comparing the predicted defect-proneness against the actual defect information in the target release.It has been shown that,if there is a sufficiently large amount of training data at the model-building phase,the resulting model in general predicts defects well at the model application phase [23].In practice, however,sufficiently large training data are often unavailable,especially for those projects with few or even no historical releases.To mitigate this problem,researchers propose to use the training data from other projects(i.e.,source projects)to build the model for the target project,which is called cross-project defect prediction [9,106,129]. In the literature,it has been highlighted that there are two major challenges that cross-project defect prediction has to deal with.The first challenge is that the source and target project data usually exhibit significantly different distributions [13,41,42,61,80,123].Since the source and target projects might be from different corporations,there might have a large difference between their development environments.Consequently,the same metrics in the source and target project data might have a large difference between the data distributions.However,regular modeling techniques are based on the assumption that the training and test data are drawn from the same distribution.Therefore,it is difficult to achieve a good prediction performance by directly applying regular modeling techniques to build the prediction models.The second challenge is that the source and target project data might have no common metrics [27,41,79].In practice,since the source and target projects might be from different corporations,it is likely that the source and target project data consist of completely different metric sets.However,regular modeling techniques are based on the assumption that the training and test data have common metric sets.Consequently, it is difficult,if not impossible,to predict defects in the target project using the models built with regular modeling techniques on the source project data.In recent years,much effort has been devoted to dealing with these challenges. 2.2 General Framework In the field of cross-project defect prediction,supervised models are the mainstream models. Figure 1 presents a general framework that applies supervised techniques to cross-project defect prediction.As can be seen,the target project consists of k release,among which the kth release is ACM Transactions on Software Engineering and Methodology,Vol.27,No.1,Article 1.Pub.date:April 2018

1:4 Y. Zhou et al. 2 CROSS-PROJECT DEFECT PREDICTION In this section, we first describe the problem that cross-project defect prediction aims to address. Then, we present a general framework that depicts the key components in a supervised cross￾project defect prediction. After that, we introduce the commonly used performance evaluation indicators involved in the existing CPDP studies. Finally, we give a literature overview of current developments in this area and provide a comparison of the main CPDP studies under the general framework. 2.1 Problem Statement The purpose of cross-project defect prediction is to address the shortage problem of training data that are used for building a model to predict defects in a target release of a target project. To predict defects in the target release, it is common to use a two-phase process (i.e., model building and ap￾plication). At the model-building phase, the metric data and the defect data are first collected from the modules in historical releases (i.e., the releases before the target release in the target project). Then, based on the collected data, a specific modeling technique is used to train a model to cap￾ture the relationships between the metrics and defect-proneness. At the model application phase, the same metrics are first collected from the modules in the target release. Then, the predicted defect-proneness of each module in the target release is obtained by substituting the correspond￾ing metric data into the model. After that, the predicted performance is evaluated by comparing the predicted defect-proneness against the actual defect information in the target release. It has been shown that, if there is a sufficiently large amount of training data at the model-building phase, the resulting model in general predicts defects well at the model application phase [23]. In practice, however, sufficiently large training data are often unavailable, especially for those projects with few or even no historical releases. To mitigate this problem, researchers propose to use the training data from other projects (i.e., source projects) to build the model for the target project, which is called cross-project defect prediction [9, 106, 129]. In the literature, it has been highlighted that there are two major challenges that cross-project defect prediction has to deal with. The first challenge is that the source and target project data usually exhibit significantly different distributions [13, 41, 42, 61, 80, 123]. Since the source and target projects might be from different corporations, there might have a large difference between their development environments. Consequently, the same metrics in the source and target project data might have a large difference between the data distributions. However, regular modeling techniques are based on the assumption that the training and test data are drawn from the same distribution. Therefore, it is difficult to achieve a good prediction performance by directly applying regular modeling techniques to build the prediction models. The second challenge is that the source and target project data might have no common metrics [27, 41, 79]. In practice, since the source and target projects might be from different corporations, it is likely that the source and target project data consist of completely different metric sets. However, regular modeling techniques are based on the assumption that the training and test data have common metric sets. Consequently, it is difficult, if not impossible, to predict defects in the target project using the models built with regular modeling techniques on the source project data. In recent years, much effort has been devoted to dealing with these challenges. 2.2 General Framework In the field of cross-project defect prediction, supervised models are the mainstream models. Figure 1 presents a general framework that applies supervised techniques to cross-project defect prediction. As can be seen, the target project consists of k release, among which the kth release is ACM Transactions on Software Engineering and Methodology, Vol. 27, No. 1, Article 1. Pub. date: April 2018

How Far We Have Progressed in the Journey?An Examination of CPDP 15 ◆ ]1a[S'9 - ACM Transactions on Software Engineering and Methodology.Vol.27.No.1,Article 1.Pub.date:April 2018

How Far We Have Progressed in the Journey? An Examination of CPDP 1:5 Fig. 1. The general framework that applies supervised modeling techniques to cross-project defect prediction. ACM Transactions on Software Engineering and Methodology, Vol. 27, No. 1, Article 1. Pub. date: April 2018