IET Software最新文献

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.

Smart contracts have gained immense popularity in recent years as self-executing programs that operate on a blockchain. However, they are not immune to security flaws, which can result in significant financial losses. These flaws can be detected using dynamic analysis methods that extract various aspects from smart contract bytecode. Methods currently used for identifying vulnerabilities in smart contracts mostly rely on static analysis methods that search for predefined vulnerability patterns. However, these patterns often fail to capture complex vulnerabilities, leading to a high rate of false negatives. To overcome this limitation, researchers have explored machine learning-based methods. However, the accurate interpretation of complex logic and structural information in smart contract code remains a challenge. In this study, we present a technique that combines real-time runtime batch normalization and data augmentation for data preprocessing, along with n-grams and one-hot encoding for feature extraction of opcode sequence information from the bytecode. We then combined bidirectional long short-term memory (BiLSTM), convolutional neural network, and the attention mechanism for vulnerability detection and classification. Additionally, our model includes a gated recurrent units memory module that enhances efficiency using historical execution data from the contract. Our results demonstrate that our proposed model effectively identifies smart contract vulnerabilities.

In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user-related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.

Mobile applications are continuously increasing in prevalence. One of the main challenges in mobile application development is creating cross-platform applications. To facilitate developing cross-platform applications, the software engineering community created several solutions, one of which is React Native (RN), which is a popular cross-platform framework. The software engineering literature demonstrated the effectiveness of Stack Overflow (SO) in providing real-world perspectives on a variety of technical subjects. Therefore, this study aims to gain a better understanding of the stance of RN on SO. We identified and analyzed 131,620 SO RN-related questions. Moreover, we observed how the interest toward RN on SO evolves over time. Additionally, we utilized Latent Dirichlet Allocation (LDA) to identify RN-related topics that are discussed within the questions. Afterward, we utilized a number of proxy measures to estimate the popularity and difficulty of these topics. The results revealed that interest toward RN on SO was generally increasing. Moreover, RN-related questions revolve around six topics, with the topics of layout and navigation being the most popular and the topic of iOS issues being the most difficult. Software engineering researchers, practitioners, educators, and RN contributors may find the results of this study beneficial in guiding their future RN efforts.

With the rapid development of artificial intelligence and digital media technology, modern animation technology has greatly improved the creative efficiency of creators through computer-generated graphics, electronic manual painting, and other means, and its number has also experienced explosive growth. The intelligent completion of emotional expression identification within animation works holds immense significance for both animation production learners and the creation of intelligent animation works. Consequently, emotion recognition has emerged as a focal point of research attention. This paper focuses on the analysis of emotional states in animation works. First, by analyzing the characteristics of emotional expression in animation, the model data foundation for using sound and video information is determined. Subsequently, we perform individual feature extraction for these two types of information using gated recurrent unit (GRU). Finally, we employ a multiattention mechanism to fuse the multimodal information derived from audio and video sources. The experimental outcomes demonstrate that the proposed method framework attains a recognition accuracy exceeding 90% for the three distinct emotional categories. Remarkably, the recognition rate for negative emotions reaches an impressive 94.7%, significantly surpassing the performance of single-modal approaches and other feature fusion methods. This research presents invaluable insights for the training of multimedia animation production professionals, empowering them to better grasp the nuances of emotion transfer within animation and, thereby, realize productions of elevated quality, which will greatly improve the market operational efficiency of animation industry.

The performance of software defect prediction (SDP) models determines the priority of test resource allocation. Researchers also use interpretability techniques to gain empirical knowledge about software quality from SDP models. However, SDP methods designed in the past research rarely consider the impact of data transformation methods, simple but commonly used preprocessing techniques, on the performance and interpretability of SDP models. Therefore, in this paper, we investigate the impact of three data transformation methods (Log, Minmax, and Z-score) on the performance and interpretability of SDP models. Through empirical research on (i) six classification techniques (random forest, decision tree, logistic regression, Naive Bayes, K-nearest neighbors, and multilayer perceptron), (ii) six performance evaluation indicators (Accuracy, Precision, Recall, F1, MCC, and AUC), (iii) two interpretable methods (permutation and SHAP), (iv) two feature importance measures (Top-k feature rank overlap and difference), and (v) three datasets (Promise, Relink, and AEEEM), our results show that the data transformation methods can significantly improve the performance of the SDP models and greatly affect the variation of the most important features. Specifically, the impact of data transformation methods on the performance and interpretability of SDP models depends on the classification techniques and evaluation indicators. We observe that log transformation improves NB model performance by 7%–61% on the other five indicators with a 5% drop in Precision. Minmax and Z-score transformation improves NB model performance by 2%–9% across all indicators. However, all three transformation methods lead to substantial changes in the Top-5 important feature ranks, with differences exceeding 2 in 40%–80% of cases (detailed results available in the main content). Based on our findings, we recommend that (1) considering the impact of data transformation methods on model performance and interpretability when designing SDP approaches as transformations can improve model accuracy, and potentially obscure important features, which lead to challenges in interpretation, (2) conducting comparative experiments with and without the transformations to validate the effectiveness of proposed methods which are designed to improve the prediction performance, and (3) tracking changes in the most important features before and after applying data transformation methods to ensure precise and traceable interpretability conclusions to gain insights. Our study reminds researchers and practitioners of the need for comprehensive considerations even when using other similar simple data processing methods.