IET Software最新文献

Machine learning-based software defect prediction (SDP) approaches have been commonly proposed to help to deliver high-quality software. Unfortunately, all the previous research conducted without effective feature reduction suffers from high-dimensional data, leading to unsatisfactory prediction performance measures. Moreover, without proper feature reduction, the interpretability and generalization ability of machine learning models in SDP may be compromised, hindering their practical utility in diverse software development environments. In this paper, an SDP approach using deep Q-learning network (DQN)-based feature extraction is proposed to eliminate irrelevant, redundant, and noisy features and improve the classification performance. In the data preprocessing phase, the undersampling method of BalanceCascade is applied to divide the original datasets. As the first step of feature extraction, the weight ranking of all the metric elements is calculated according to the expected cross-entropy. Then, the relation matrix is constructed by applying random matrix theory. After that, the reward principle is defined for computing the Q value of Q-learning based on weight ranking, relation matrix, and the number of errors, according to which a convolutional neural network model is trained on datasets until the sequences of metric pairs are generated for all datasets acting as the revised feature set. Various experiments have been conducted on 11 NASA and 11 PROMISE repository datasets. Sensitive analysis experiments show that binary classification algorithms based on SDP approaches using the DQN-based feature extraction outperform those without using it. We also conducted experiments to compare our approach with four state-of-the-art approaches on common datasets, which show that our approach is superior to these methods in precision, F-measure, area under receiver operating characteristics curve, and Matthews correlation coefficient values.

Cross-project defect prediction (CPDP) is an attractive research area in software testing. It identifies defects in projects with limited labeled data (target projects) by utilizing predictive models from data-rich projects (source projects). Existing CPDP methods based on transfer learning mainly rely on the assumption of a unimodal distribution and consider the case where the feature distribution has one obvious peak. However, in actual situations, the feature distribution of project samples often exhibits multiple peaks that cannot be ignored. It manifests as a multimodal distribution, making it challenging to align distributions between different projects. To address this issue, we propose a balanced adversarial tight-matching model for CPDP. Specifically, this method employs multilinear conditioning to obtain the cross-covariance of both features and classifier predictions, capturing the multimodal distribution of the feature. When reducing the captured multimodal distribution differences, pseudo-labels are needed, but pseudo-labels have uncertainty. Therefore, we additionally add an auxiliary classifier and attempt to generate pseudo-labels using a pseudo-label strategy with less uncertainty. Finally, the feature generator and two classifiers undergo adversarial training to align the multimodal distributions of different projects. This method outperforms the state-of-the-art CPDP model used on the benchmark dataset.

The role of focal packages in packaging ecosystems is crucial for the development of the entire ecosystem, as they are the packages on which other packages depend. However, the evolution of dependency groups in packaging ecosystems has not been systematically investigated. In this study, we examine the downstream dependency package groups (DDGs) in three typical packaging ecosystems—Cargo for Rust, Comprehensive Perl Archive Network for Perl, and RubyGems for Ruby—to identify their features and evolution. We also identify and analyze a special type of DDG, the collaborative downstream dependency package group (CDDG), which requires shared contributors. Our findings show that the overall development of DDGs, particularly CDDGs, is consistent with the status of the whole ecosystem, and the size of DDGs and CDDGs follows a power law distribution. Furthermore, the interaction mechanisms between focal packages and downstream packages differ between ecosystems, but focal packages always play a leading role in the development of DDGs and CDDGs. Finally, we investigate predictive models for the development of CDDGs in the next stage based on their features, and our results show that random forest and Gradient Boosting Regression Tree achieve acceptable prediction accuracy. We provide the raw data and scripts used for our analysis at https://github.com/onion616/DDG.

Test case prioritization techniques improve the fault detection rate by adjusting the execution sequence of test cases. For static black-box test case prioritization techniques, existing methods generally improve the fault detection rate by increasing the early diversity of execution sequences based on string distance differences. However, such methods have a high time overhead and are less stable. This paper proposes a novel test case prioritization method (DC-TCP) based on density-based spatial clustering of applications with noise (DBSCAN) and combination policies. By introducing a combination strategy to model the inputs to generate a mapping model, the test inputs are mapped to consistent types to improve generality. The DBSCAN method is then used to refine the classification of test cases further, and finally, the Firefly search strategy is introduced to improve the effectiveness of sequence merging. Extensive experimental results demonstrate that the proposed DC-TCP method outperforms other methods in terms of the average percentage of faults detected and exhibits advantages in terms of time efficiency when compared to several existing static black-box sorting methods.

With the continuous advancement of autonomous driving technology, visual analysis techniques have emerged as a prominent research topic. The data generated by autonomous driving is large-scale and time-varying, yet more than existing visual analytics methods are required to deal with such complex data effectively. Time-varying diagrams can be used to model and visualize the dynamic relationships in various complex systems and can visually describe the data trends in autonomous driving systems. To this end, this paper introduces a time-varying graph-based method for visual analysis in autonomous driving. The proposed method employs a graph structure to represent the relative positional relationships between the target and obstacle interferences. By incorporating the time dimension, a time-varying graph model is constructed. The method explores the characteristic changes of nodes in the graph at different time instances, establishing feature expressions that differentiate target and obstacle motion patterns. The analysis demonstrates that the feature vector centrality in the time-varying graph effectively captures the distinctions in motion patterns between targets and obstacles. These features can be utilized for accurate target and obstacle recognition, achieving high recognition accuracy. To evaluate the proposed time-varying graph-based visual analytic autopilot method, a comparative study is conducted against traditional visual analytic methods such as the frame differencing method and advanced visual analytic methods like visual lidar odometry and mapping. Robustness, accuracy, and resource consumption experiments are performed using the publicly available KITTI dataset to analyze and compare the three methods. The experimental results show that the proposed time-varying graph-based method exhibits superior accuracy and robustness. This study offers valuable insights and solution ideas for developing deep integration between intelligent networked vehicles and intelligent transportation. It provides a reference for advancing intelligent transportation systems and their integration with autonomous driving technologies.

With the increasing number of software projects, within-project defect prediction (WPDP) has already been unable to meet the demand, and cross-project defect prediction (CPDP) is playing an increasingly significant role in the area of software engineering. The classic CPDP methods mainly concentrated on applying metric features to predict defects. However, these approaches failed to consider the rich semantic information, which usually contains the relationship between software defects and context. Since traditional methods are unable to exploit this characteristic, their performance is often unsatisfactory. In this paper, a transfer long short-term memory (TLSTM) network model is first proposed. Transfer semantic features are extracted by adding a transfer learning algorithm to the long short-term memory (LSTM) network. Then, the traditional metric features and semantic features are combined for CPDP. First, the abstract syntax trees (AST) are generated based on the source codes. Second, the AST node contents are converted into integer vectors as inputs to the TLSTM model. Then, the semantic features of the program can be extracted by TLSTM. On the other hand, transferable metric features are extracted by transfer component analysis (TCA). Finally, the semantic features and metric features are combined and input into the logical regression (LR) classifier for training. The presented TLSTM model performs better on the f-measure indicator than other machine and deep learning models, according to the outcomes of several open-source projects of the PROMISE repository. The TLSTM model built with a single feature achieves 0.7% and 2.1% improvement on Log4j-1.2 and Xalan-2.7, respectively. When using combined features to train the prediction model, we call this model a transfer long short-term memory for defect prediction (DPTLSTM). DPTLSTM achieves a 2.9% and 5% improvement on Synapse-1.2 and Xerces-1.4.4, respectively. Both prove the superiority of the proposed model on the CPDP task. This is because LSTM capture long-term dependencies in sequence data and extract features that contain source code structure and context information. It can be concluded that: (1) the TLSTM model has the advantage of preserving information, which can better retain the semantic features related to software defects; (2) compared with the CPDP model trained with traditional metric features, the performance of the model can validly enhance by combining semantic features and metric features.

In the rapidly evolving landscape of social media, the demand for precise sentiment analysis (SA) on multimodal data has become increasingly pivotal. This paper introduces a sophisticated data lake architecture tailored for efficient multimodal emotion feature extraction, addressing the challenges posed by diverse data types. The proposed framework encompasses a robust storage solution and an innovative SA model, multilevel spatial attention fusion (MLSAF), adept at handling text and visual data concurrently. The data lake architecture comprises five layers, facilitating real-time and offline data collection, storage, processing, standardized interface services, and data mining analysis. The MLSAF model, integrated into the data lake architecture, utilizes a novel approach to SA. It employs a text-guided spatial attention mechanism, fusing textual and visual features to discern subtle emotional interplays. The model’s end-to-end learning approach and attention modules contribute to its efficacy in capturing nuanced sentiment expressions. Empirical evaluations on established multimodal sentiment datasets, MVSA-Single and MVSA-Multi, validate the proposed methodology’s effectiveness. Comparative analyses with state-of-the-art models showcase the superior performance of our approach, with an accuracy improvement of 6% on MVSA-Single and 1.6% on MVSA-Multi. This research significantly contributes to optimizing SA in social media data by offering a versatile and potent framework for data management and analysis. The integration of MLSAF with a scalable data lake architecture presents a strategic innovation poised to navigate the evolving complexities of social media data analytics.

Developer question and answering communities rely on experts to provide helpful answers. However, these communities face a shortage of experts. To cultivate more experts, the community needs to quantify and analyze the rules of the influence of extrinsic motivations on the ongoing contributions of those developers who can become experts in the future (potential experts). Currently, there is a lack of potential expert-centred research on community incentives. To address this gap, we propose a motivational impact model with self-determination theory-based hypotheses to explore the impact of five extrinsic motivations (badge, status, learning, reputation, and reciprocity) for potential experts. We develop a status-based timeline partitioning method to count information on the sustained contributions of potential experts from Stack Overflow data and propose a multifactor assessment model to examine the motivational impact model to determine the relationship between potential experts’ extrinsic motivations and sustained contributions. Our results show that (i) badge and reciprocity promote the continuous contributions of potential experts while reputation and status reduce their contributions; (ii) status significantly affects the impact of reciprocity on potential experts’ contributions; (iii) the difference in the influence of extrinsic motivations on potential experts and active developers lies in the influence of reputation, learning, and status and its moderating effect. Based on these findings, we recommend that community managers identify potential experts early and optimize reputation and status incentives to incubate more experts.

Uncertainty exists widely in various fields, especially in industrial manufacturing. From traditional manufacturing to intelligent manufacturing, uncertainty always exists in the manufacturing process. With the integration of rapidly developing intelligent technology, the complexity of manufacturing scenarios is increasing, and the postdecision method cannot fully meet the needs of the high reliability of the process. It is necessary to research the pre-elimination of uncertainty to ensure the reliability of process execution. Here, we analyze the sources and characteristics of uncertainty in manufacturing scenarios and propose a meta-model architecture and uncertainty quantification (UQ) framework for uncertainty modeling. On the one hand, our approach involves the creation of a meta-model structure that incorporates various strategies for uncertainty elimination (UE). On the other hand, we develop a comprehensive UQ framework that utilizes quantified metrics and outcomes to bolster the UE process. Finally, a deterministic model is constructed to guide and drive the process execution, which can achieve the purpose of controlling the uncertainty in advance and ensuring the reliability of the process. In addition, two typical manufacturing process scenarios are modeled, and quantitative experiments are conducted on a simulated production line and open-source data sets, respectively, to illustrate the idea and feasibility of the proposed approach. The proposed UE approach, which innovatively combines the domain modeling from the software engineering field and the probability-based UQ method, can be used as a general tool to guide the reliable execution of the process.