Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM

Yu, Xiao; Liu, Jin; Keung, Jacky; Li, Qing; Bennin, Kwabena Ebo; Zhou, Xu; Wang, Jun Ping; Cui, Xiaohui

doi:10.1109/tr.2019.2931559

Cited by 50 publications

(8 citation statements)

References 80 publications

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“…In our empirical study, we use the three threshold-dependent evaluation metrics (Precision, Recall, and F-measure (F1)) and one threshold-independent evaluation metric (Matthews correlation coefficient, MCC) to evaluate the performance of CSD models. The metrics are widely used in both software engineering studies [64][65][66][67][68][69][70][71] and artificial intelligence researches. [72][73][74][75] In the binary classification problem, these four evaluation metrics can be calculated according to a confusion matrix, as shown in Table 4.…”

Section: Performance Measuresmentioning

confidence: 99%

On the relative value of imbalanced learning for code smell detection

Zou

Keung

et al. 2023

Softw Pract Exp

Self Cite

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SummaryMachine learning‐based code smell detection (CSD) has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for CSD, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques for machine learning‐based CSD, we examine 31 imbalanced learning techniques with seven classifiers to build CSD models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed‐rank test and Cliff's . The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over‐sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best‐performing imbalanced learning techniques and the top‐3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.

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Section: Performance Measuresmentioning

confidence: 99%

On the relative value of imbalanced learning for code smell detection

Zou

Keung

et al. 2023

Softw Pract Exp

Self Cite

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“…Consequently, ranking modules in Project D can be more helpful than predicting whether or not a module is defective in the absence of testing resources. Since this CPDP approach employs the learning to rank technique to build models, we call it ranking-oriented cross-project defect prediction (ROCPDP) [26].…”

Section: Rocpdpmentioning

confidence: 99%

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

Luo

Dai²,

Peng

et al. 2021

Sensors

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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.

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“…According to the analysis of practical scientific computing concepts, there are many calculation methods available for content recommendation, but due to the differences in specific schemes and concerns, it gradually presents a mixed development state in the development of practice. This paper mainly studies the common dimensions of content recommendation algorithm from a coarse-grained perspective, as shown in Figure 4 below [15]: Combined with the above structure and the analysis of the calculation process with TF-IDF as the core, it can be seen that: Firstly, N targets waiting for recommendation should be formed into an overall vector form, as shown below:…”

Section: Cascading Hybrid Algorithmmentioning

confidence: 99%

Research on the Design of Mass Recommendation System Based on Lambda Architecture

Chen

Zhu

et al. 2021

JWE

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In the era of big data, in order to increasing the information data for conforms to the personalized needs of content, research scholars put forward based on the Lambda mass recommendation system architecture design, it can not only to the recessive and dominant behavior of users of the system data collection storage and research analysis, can also be based on the analysis of cascading hybrid algorithm to explore how to carry out real-time recommendation. Therefore, on the basis of understanding the research and development achievements of recommender systems at home and abroad in recent years, and based on the understanding and analysis of Lambda architecture and cascading hybrid algorithm, this paper aims at how to design a massive recommender system in line with users’ behavior, and makes clear the recommendation effect by combining with system testing.

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Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM

Cited by 50 publications

References 80 publications

On the relative value of imbalanced learning for code smell detection

On the relative value of imbalanced learning for code smell detection

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

Research on the Design of Mass Recommendation System Based on Lambda Architecture

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