Active Learning using Uncertainty Sampling and Query-by-Committee for Software Defect Prediction

Qu, Yubin; Chen, Xiang; Chen, Ruijie; Ju, Xiaolin; Jiang-feng, Guo

doi:10.23940/ijpe.19.10.p16.27012708

Cited by 5 publications

(3 citation statements)

References 13 publications

(17 reference statements)

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“…To select uncertain samples, expected error reduction based active learning, 23 clustering‐based active learning, 24,25 and informative and representative sample‐based active learning 1,26 are proposed. In addition, Qu et al 27 proposed a hybrid active learning query strategy using uncertainty sampling and query‐by‐committee for software defect prediction 28 . Li et al 29 found that a meta‐active learning query strategy could perform better than the commonly used query strategy when a little data was labeled.…”

Section: Related Workmentioning

confidence: 99%

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Cross‐version defect prediction using threshold‐based active learning

Mei

Liu

et al. 2023

J Software Evolu Process

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Because defects in software modules (e.g., classes) might lead to product failure and financial loss, software defect prediction enables us to better understand and control software quality. Software development is a dynamic evolutionary process that may result in data distributions (e.g., defect characteristics) varying from version to version. In this case, effective cross‐version defect prediction (CVDP) is not easy to achieve. In this paper, we aim to investigate whether the defect prediction method of the threshold‐based active learning (TAL) can tackle the problem of the different data distribution between successive versions. Our TAL method includes two stages. At the active learning stage, a committee of investigated metrics is constructed to vote on the unlabeled modules of the current version. We pick up the unlabeled module with the median of voting scores to domain experts. The domain experts test and label the selected unlabeled module. Then, we merge the selected labeled module and the remaining modules with pseudo‐labels from the current version into the labeled modules of the prior version to form enhanced training data. Based on the training data, we derive the metric thresholds used for the next iteration. At the defect prediction stage, the iterations stop when a predefined threshold is reached. Finally, we use the cutoff threshold of voting scores, that is, 50%, to predict the defect‐prone of the remaining unlabeled modules. We evaluate the TAL method on 31 versions of 10 projects with three prevalent performance indicators. The results show that TAL outperforms the baseline methods, including three variations methods, two common supervised methods, and the state‐of‐the‐art method Hybrid Active Learning and Kernel PCA (HALKP). The results indicate that TAL can effectively address the different data distribution between successive versions. Furthermore, to keep the cost of extensive testing low in practice, selecting 5% of candidate modules from the current version is sufficient for TAL to achieve a good performance of defect prediction.

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Section: Related Workmentioning

confidence: 99%

“…In addition, Qu et al 27 proposed a hybrid active learning query strategy using uncertainty sampling and query-by-committee for software defect prediction. 28 Li et al 29 found that a meta-active learning query strategy could perform better than the commonly used query strategy when a little data was labeled.…”

Section: Sample Selection Strategymentioning

confidence: 99%

Cross‐version defect prediction using threshold‐based active learning

Mei

Liu

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

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“…There are some strategies to implement uncertainty sampling, in which the most popular sampling strategy uses entropy as an uncertainty measure [45] shown in Equation (10). Using entropy as an uncertainty measure is investigated in our prior work [46]. The reason that Entropy can be used for uncertainty sampling in active learning or in our approach as shown in Equation (11) shows that this is a metric centered on the amount of information, which can represent the amount of information needed to "encode" a distribution.…”

Section: ( ) Log( ) T Tmentioning

confidence: 99%

Deep Learning-Based Self-Admitted Technical Debt Detection Empirical Research

Qu¹,

Qu²,

Meng³

et al. 2023

Journal of Internet Technology

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<p>Self-Admitted Technical Debt (SATD) is a workaround for current gains and subsequent software quality in software comments. Some studies have been conducted using NLP-based techniques or CNN-based classifiers. However, there exists a class imbalance problem in different software projects since the software code comments with SATD features are significantly less than those without Non-SATD. Therefore, to design a classification model with the ability of dealing with this class imbalance problem is necessary for SATD detection. We propose an improved loss function based on information entropy. Our proposed function is studied in a variety of application scenarios. Empirical research on 10 JAVA software projects is conducted to show the competitiveness of our new approach. We find our proposed approach can perform significantly better than state-of-the-art baselines. </p> <p> </p>

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