IET Software最新文献

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article from IET Software, published online on 2 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd.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article from IET Software, published online on 2 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd.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.

Retraction: [Aryan Marwaha, Rami Qays Malik, Shehab Mohamed Beram, Ali Rizwan, Hari Kishore Kakarla, Deepak Thakur, Tanya Gera, Mohammad Shabaz, Visualisation-based binary classification of android malware using vgg16, IET Software 2023 (https://doi.org/10.1049/sfw2.12094)].

The above article from IET Software, published online on 23 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.

The rapid development of Open-Source Software (OSS) has resulted in a significant demand for code changes to maintain OSS. Symptoms of poor design and implementation choices in code changes often occur, thus heavily hindering code reviewers to verify correctness and soundness of code changes. Researchers have investigated how to learn meaningful code changes to assist developers in anticipating changes that code reviewers may suggest for the submitted code. However, there are two main limitations to be addressed, including the limitation of long-range dependencies of the source code and the missing syntactic structural information of the source code. To solve these limitations, a novel method is proposed, named Graph Transformer for learning meaningful Code Transformations (GTCT), to provide developers with preliminary and quick feedback when developers submit code changes, which can improve the quality of code changes and improve the efficiency of code review. GTCT comprises two components: code graph embedding and code transformation learning. To address the missing syntactic structural information of the source code limitation, the code graph embedding component captures the types and patterns of code changes by encoding the source code into a code graph structure from the lexical and syntactic representations of the source code. Subsequently, the code transformation learning component uses the multi-head attention mechanism and positional encoding mechanism to address the long-range dependencies limitation. Extensive experiments are conducted to evaluate the performance of GTCT by both quantitative and qualitative analyses. For the quantitative analysis, GTCT relatively outperforms the baseline on six datasets by 210%, 342.86%, 135%, 29.41%, 109.09%, and 91.67% in terms of perfect prediction. Meanwhile, the qualitative analysis shows that each type of code change by GTCT outperforms that of the baseline method in terms of bug fixed, refactoring code and others' taxonomy of code changes.