IET Software最新文献

Effort-Aware Defect Prediction (EADP) methods sort software modules based on the defect density and guide the testing team to inspect the modules with high defect density first. Previous studies indicated that some feature selection methods could improve the performance of Classification-Based Defect Prediction (CBDP) models, and the Correlation-based feature subset selection method with the Best First strategy (CorBF) performed the best. However, the practical benefits of feature selection methods on EADP performance are still unknown, and blindly employing the best-performing CorBF method in CBDP to pre-process the defect datasets may not improve the performance of EADP models but possibly result in performance degradation. To assess the impact of the feature selection techniques on EADP, a total of 24 feature selection methods with 10 classifiers embedded in a state-of-the-art EADP model (CBS+) on the 41 PROMISE defect datasets were examined. We employ six evaluation metrics to assess the performance of EADP models comprehensively. The results show that (1) The impact of the feature selection methods varies in classifiers and datasets. (2) The four wrapper-based feature subset selection methods with forwards search, that is, AdaBoost with Forwards Search, Deep Forest with Forwards Search, Random Forest with Forwards Search, and XGBoost with Forwards Search (XGBF) are better than other methods across the studied classifiers and the used datasets. And XGBF with XGBoost as the embedded classifier in CBS+ performs the best on the datasets. (3) The best-performing CorBF method in CBDP does not perform well on the EADP task. (4) The selected features vary with different feature selection methods and different datasets, and the features noc (number of children), ic (inheritance coupling), cbo (coupling between object classes), and cbm (coupling between methods) are frequently selected by the four wrapper-based feature subset selection methods with forwards search. (5) Using AdaBoost, deep forest, random forest, and XGBoost as the base classifiers embedded in CBS+ can achieve the best performance. In summary, we recommend the software testing team should employ XGBF with XGBoost as the embedded classifier in CBS+ to enhance the EADP performance.

Retraction: [Shuo Cheng, Yao Lu, Intelligent design of rural residential environment guided by blockchain under the concept of green low carbon, IET Software 2023 (https://doi.org/10.1049/sfw2.12119)].

The above article from IET Software, published online on 5 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Retraction: [Tao Cheng, Lianjiang Li, Optimization of E-commerce platform marketing method and comment recognition model based on deep learning and intelligent blockchain, IET Software 2023 (https://doi.org/10.1049/sfw2.12117)].

The above article from IET Software, published online on 3 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Retraction: [Liping Cai, Qiaona Gong, Feng Jiang, Mingzhan Yuan, Zhiyong Xiao, Shuai Zhang, Chengcheng Zheng, Yue Wu, The use of the intelligent Bayesian network method combined with blockchain technology in the optimisation of tunnel construction quality control, IET Software 2023 (https://doi.org/10.1049/sfw2.12109)].

The above article from IET Software, published online on 2 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Retraction: [Abdulwahid Al Abdulwahid, Software solution for text summarisation using machine learning based Bidirectional Encoder Representations from Transformers algorithm, IET Software 2023 (https://doi.org/10.1049/sfw2.12098)].

The above article from IET Software, published online on 2 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Retraction: [Tingting Zheng, Intelligent distribution system of university student dormitory based on data fusion optimisation algorithm, IET Software 2023 (https://doi.org/10.1049/sfw2.12100)].

The above article from IET Software, published online on 1 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Deep-Learning-based Fault Localisation (DLFL) leverages deep neural networks to learn the relationship between statement behaviour and program failures, showing promising results. However, since DLFL uses program failures as labels to conduct supervised learning, a labelled dataset is a requisite of applying DLFL. A failure is detected by comparing program output with a test oracle which is the standard answer for the given input. The problem is, test oracles are often difficult, or even impossible to acquire in real life, and that has severely restricted the application of DLFL since we have only unlabelled datasets in most cases. Thus, MetaFL: Metamorphic Fault Localisation Using Weakly Supervised Deep Learning is proposed, to provide a weakly supervised learning solution for DLFL. Instead of using test oracles, MetaFL uses metamorphic relations to prescribe expected behaviour of a program, and defines labels of metamorphic testing groups by verifying integrity in each group of test cases. Hence, a coarse-grained labelled dataset can be built from the originally unlabelled one, with which DLFL can work now, utilising a weakly supervised learning paradigm. The experiments show that MetaFL yields a performance comparable to plain DLFL under ideal condition (i.e. the labels of datasets are available). MetaFL successfully extends the methodology of DLFL from supervised learning to weakly supervised learning, and a fully labelled dataset is no longer mandatory for applying DLFL.

Retraction: [Inas Ismael Imran, Shymaa Mohammed Jameel, Refed Adnan Jaleel, Smart optimal path with blockchain modelling in internet of things using enhanced K-means clustering and advance encryption standard, IET Software 2023 (https://doi.org/10.1049/sfw2.12095)].

The above article from IET Software, published online on 28 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.