Cross-version defect prediction via hybrid active learning with kernel principal component analysis

Zhou, Xu; Liu, Jin; Luo, Xiapu; Zhang, Tao

doi:10.1109/saner.2018.8330210

Cited by 50 publications

(64 citation statements)

References 59 publications

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“…Based on the above analysis, we find previous two studies [29], [31] only focused on the within-project defect prediction scenario. The latest two studies [32], [49] only focused on the cross-version defect prediction scenario, which has the smaller distribution difference when compared to the cross-project defect prediction scenario focused in our study [22]. In this article, we are the first to combine active learning and TrAdaBoost to solve a more challenge problem (i.e., CPDP problem) and to our best knowledge, this kind of method has not been investigated in the previous CPDP studies.…”

Section: B Active Learning and Its Application To Software Defect Prmentioning

confidence: 94%

“…They considered both active learning and feature compression techniques (i.e., feature selection techniques and dimensionality reduction techniques). For the cross-version defect prediction, Xu et al [49] also proposed a method HALKP, which combines hybrid active learning with kernel principal component analysis. In particular, this method first selects some informative and representative unlabeled modules in the current version, and then utilizes a non-linear mapping method (i.e., kernel principal component analysis), which can embed the original data from two different versions into a high-dimension space.…”

Section: B Active Learning and Its Application To Software Defect Prmentioning

confidence: 99%

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ALTRA: Cross-Project Software Defect Prediction via Active Learning and Tradaboost

et al. 2020

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Cross-project defect prediction (CPDP) methods can be used when the target project is a new project or lacks enough labeled program modules. In these new target projects, we can easily extract and then measure these modules with software measurement tools. However, labeling these program modules is time-consuming, error-prone and requires professional domain knowledge. Moreover, directly using labeled modules in the other projects (i.e., the source projects) can not achieve satisfactory performance due to the large data distribution difference in most cases. In this article, to our best knowledge, we are the first to propose a novel method ALTRA, which can utilize both active learning and TrAdaBoost to alleviate this issue. In particular, we firstly use Burak filter to select similar labeled modules from the source project after analyzing the unlabeled modules in the target project. Then we use active learning to choose representative unlabeled modules from the target project and ask experts to label the type (i.e., defective or non-defective) of these modules. Later, we use TrAdaBoost to determine the weights of labeled modules in the source project and the target project, and then construct the model via weighted support vector machine. After selecting a small number of modules (i.e., only 5% modules) in the target project, we terminate the method ALTRA and return the final constructed model. To show the effectiveness of our proposed method ALTRA, we choose 10 large-scale open-source projects from different application domains. In terms of both F1 and AUC performance indicators, we find ALTRA can perform significantly better than seven state-of-the-art CPDP baselines. Moreover, we also show that the usage of Burak filter, the uncertainty active learning strategy, the class imbalanced learning method and TrAdaBoost are competitive in our proposed method ALTRA.

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Section: B Active Learning and Its Application To Software Defect Prmentioning

confidence: 94%

Section: B Active Learning and Its Application To Software Defect Prmentioning

confidence: 99%

ALTRA: Cross-Project Software Defect Prediction via Active Learning and Tradaboost

et al. 2020

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“…Lu et al [46] proposed an approach which took uncertain information as the strategy to select special instances of the current version iteratively, and then determine their classes manually and merged them into the training set. Xu et al [47] proposed an active learning method based on uncertainty information and information density to select special instances from the current version.…”

Section: B Defect Prediction Of Evolving Projectsmentioning

confidence: 99%

Which Process Metrics Are Significantly Important to Change of Defects in Evolving Projects: An Empirical Study

Jiang

Gong

et al. 2020

IEEE Access

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Process metrics can reflect the software development process and the code changes which are the main causes of defects. So, recently, the researches have put more emphasis on process metrics in the field of software defect prediction. For evolving projects, it is more meaningful to study whether the software module introduces or eliminates defects or not, not whether the software module is defective or defect-free. However, no such work is available in the literature focusing on the change of defect state. Discovering the factors that influence the change of defect state in the process of software development can help us to understand the causes of software defects and improve the quality of subsequent software versions. Therefore, this paper presents an extensive empirical study on which process metrics are significantly important to change of defects in evolving projects. Five process metrics of 37 versions in 12 software projects are collected. We not only analyze the class correlation values and the classification performance values among five process metrics, but also perform statistical analysis to verify whether the experimental results are of practical value. The experimental results indicate that Number of Distinct Committers plays a significantly important role in the change of defect state, especially for elimination of defects, and Number of Revisions is the second, whereas Degree of Code Modification is the last. In addition, Average Number of Modified Lines is superior to Number of Modified Lines. Based on the experimental results, some suggestions for software development and software defect prediction are also discussed.

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“…They found that models using cross-company data can only be "useful in extreme cases such as mission-critical projects, where the cost of false alarms can be afforded" and suggested using within-company data if available. While some recent studies reported advances in cross-project defect prediction (Xia et al, 2016;Zhang et al, 2016;Xu et al, 2018), it is still considered as a challenging task.…”

Section: Related Workmentioning

confidence: 99%

Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk

Niedermayr

Röhm

Wagner

2019

PeerJ Computer Science

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BackgroundTest resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios.AimsWe take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being “trivial”. We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions.MethodWe compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk (LFR). We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level.ResultsOur results show that inverse defect prediction can identify approx. 32–44% of the methods of a project to have a LFR; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even 11 times less likely to contain a fault.ConclusionsInverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.

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Cross-version defect prediction via hybrid active learning with kernel principal component analysis

Cited by 50 publications

References 59 publications

ALTRA: Cross-Project Software Defect Prediction via Active Learning and Tradaboost

ALTRA: Cross-Project Software Defect Prediction via Active Learning and Tradaboost

Which Process Metrics Are Significantly Important to Change of Defects in Evolving Projects: An Empirical Study

Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk

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