In comparison to the lending data submitted by banks to credit bureaus under the traditional credit scoring paradigm, alternative credit data (such as social media activities and e-commerce consumption records) has increasingly demonstrated its significance in enhancing the accuracy of credit scores and addressing the issue of credit-invisible individuals in recent years. However, credit scoring model based on alternative credit data typically necessitates large-scale data circulation and may involve sensitive information, thereby raising concerns related to data security, user privacy, and data rights. Traditional cryptographic methods often encounter limitations in functionality, efficiency, flexibility, and traceability when addressing these issues. This article initially proposes a novel credit data sharing framework based on an alternative data cloud platform. Subsequently, based on this framework, a blockchain-based privacy-preserving alternative credit data sharing scheme is constructed. This scheme achieves efficient, privacy-preserving, and wildcard-supported attribute-based encryption (ABE) scheme through inner product operations, and implements a “two-level” access control by designing a keyword search mechanism in conjunction with the aforementioned scheme. Furthermore, a hybrid encryption mechanism is introduced to further enhance efficiency and security under high-frequency access scenarios. Security analysis and rigorous formal security reductions have been conducted to demonstrate the security of the proposed scheme. Comparative experimental results also indicate that the proposed scheme exhibits significant advantages in practicality compared with related schemes.

Developer departures in open source software (OSS) projects can seriously affect project sustainability, such as project delays and code quality degradation. However, analysis of developer departures in OSS projects faces challenges: limited analysis of departure prevalence, lack of standardized feature taxonomy, and limited research on large-scale datasets. In this paper, we investigate developer departures with a rigorously selected dataset. We empirically demonstrate a high prevalence of developer departure, and more than $80 \%$ of projects have departure rates between ${5 0 \%}$ and ${9 0 \%}$. Through systematic literature review, we construct the first taxonomy of departure reasons, which covers two main categories: personal reasons and organizational reasons, further divided into five and four subcategories, respectively. Through empirical research, we analyze seven individual factors and one organizational factor that influence developer departure. The result reveals: early joiners, less experienced developers, those with lower commit frequency, and non-core developers are significantly more likely to depart. Medium-sized projects face “scale disasters”, losing the flexibility of a small team without the institutionalized management advantages of a large team, making it easier for developers to depart. This study advances our understanding of developer departure prevalence, categorization, and influencing factors, and provides valuable insights for both future research and the management of open source communities.

Refactoring offers numerous advantages but also presents challenges such as high effort and limited automation. These challenges underscore the pressing need for an indepth analysis of past refactoring practices. However, existing empirical studies often focus on a limited set of refactoring types, narrow project scopes, or coarse-grained commit-level analysis, leaving several important questions underexplored. In particular, there is a lack of comprehensive understanding of how refactoring practices vary across ecosystems, evolve over time, and interact with external risks such as defect proneness and code instability. To address these gaps, this study presents an expanded empirical analysis of refactoring activities across diverse open-source projects. Leveraging an enhanced detection approach that captures nearly 100 types of refactorings, we conduct a multi-dimensional investigation into: the diversity of refactoring practices across ecosystems, their evolutionary patterns throughout the software lifecycle, and the association between bug proneness and post-refactoring volatility. Based on a comprehensive analysis of 807,651 refactoring operations, our findings provide a broader, lifecycle-aware and fine-grained perspective on how refactoring is applied in practice. We uncover consistent yet evolving usage patterns, reveal context-dependent risk profiles, and highlight the need for operation-level understanding of refactoring behavior. This study bridges key knowledge gaps in refactoring practices, offering an updated and richer understanding across three distinct dimensions and providing developers with empirical insights to optimize their refactoring strategies.

The application of computer vision-powered large language models (LLMs) for medical image diagnosis has significantly advanced healthcare systems. Recent progress in developing symmetrical architectures has greatly impacted various medical imaging tasks. While CNNs or RNNs have demonstrated excellent performance, these architectures often face notable limitations of substantial losses in detailed information, such as requiring to capture global semantic information effectively and relying heavily on deep encoders and aggressive downsampling. This paper introduces a novel LLM-based Hybrid-Transformer Network (HybridTransNet) designed to encode tokenized Big Data patches with the transformer mechanism, which elegantly embeds multimodal data of varying sizes as token sequence inputs of LLMS. Subsequently, the network performs both inter-scale and intra-scale self-attention, processing data features through a transformer-based symmetric architecture with a refining module, which facilitates accurately recovering both local and global context information. Additionally, the output is refined using a novel fuzzy selector. Compared to other existing methods on two distinct datasets, the experimental findings and formal assessment demonstrate that our LLM-based HybridTransNet provides superior performance for brain tumor diagnosis in healthcare informatics.

Display issues, often arising from design inconsistencies or software problems, can have a significant impact on both user experience and system functionality. This study focuses on three primary challenges in the field of display issues: the absence of a standardized classification system, the limitations of existing detection tools, and the inadequacy of available data. To systematically address these challenges, we introduce a comprehensive Display Issue Analysis Framework (DIS). Utilizing this framework, we construct a comprehensive and industry-validated taxonomy for display issues. When evaluating the capabilities of existing detection tools and the completeness of available data against this taxonomy, we find that current mainstream tools can identify only 77% of the cataloged display issues. This finding suggests that, although the field has received some attention from the industry, there is still room for further improvement and research. This study not only deepens our understanding of the classification of display issues and the capabilities of detection tools, but also provides valuable insights for future research and applications in this domain.

Web3, the next generation of the Internet, represents a decentralized and democratized web. Although it has garnered significant public interest and found numerous real-world applications, there is a limited understanding of people's perceptions and experiences with Web3. In this study, we conducted an empirical study to investigate the categories of Web3 applications and their popularity, as well as the potential challenges and opportunities within this emerging landscape. Our research was carried out in two phases. In the first phase, we analyzed 200 popular Web3 projects associated with 10 leading Web3 venture capital firms. In the second phase, we collected and examined code-related data from GitHub and market-related data from blockchain browsers (e.g., Etherscan) for these projects. Our analysis revealed that the Web3 ecosystem can be categorized into two groups, i.e., Web3 infrastructure and Web3 applications, with each consisting of several subcategories or subdomains. We also gained insights into the popularity of these Web3 projects at both the code and market levels and pointed out the challenges in the Web3 ecosystem at the system, developer, and user levels, as well as the opportunities it presents. Our findings contribute to a better understanding of Web3 for researchers and developers, which in turn promotes further exploration and advancement in this innovative field.

As increasingly prevalent technologies in autonomous driving, 5G and the Internet of Things (IoT), Internet of autonomous vehicle (IoAV) technology is recognized as a technique that is capable of disruptively changing the way people travel and greatly improving the travel experience. In the IoAV scenarios, information dissemination is inseparable from the interaction between autonomous vehicles and smart infrastructure. However, existing efforts rarely focus on the secrecy, authenticity of interactive data and flexible one-to-many communication between autonomous vehicles. In this paper, we propose a tamper-resistant broadcasting (TRBS) scheme for secure communication, which handles the inefficiencies and insecurity of existing identity-based broadcast signcryption solutions. Not only can our TRBS protect communication data from being illegally accessed, forged, or tampered with by malicious vehicles, but it can also enable efficient and flexible secure information dissemination between autonomous vehicles. We also exhibit strict security proofs and experimental evaluations to demonstrate our TRBS is secure and efficient for real-world applications.

Background: Bug dependencies refer to the link relationships between bugs and related issues, which are commonly observed in software evolution. It has been found that bugs with bug dependencies often take longer time to be resolved than other bugs without any dependencies. Despite the potential impact of bug dependencies on bug-fixing time, previous studies use traditional metrics without considering bug dependencies to build bug-fixing time prediction models. As a result, there is currently little empirical evidence to support the use of bug dependencies in improving prediction accuracy. Aims: We aim to conduct a comprehensive empirical study to investigate the value of considering bug dependencies for bug-fixing time prediction. Method: We define a set of bug dependency metrics based on bug dependencies. We first investigate the correlation between bug dependency metrics and bug-fixing time to investigate whether bugs with more complex dependencies are more time-consuming to be fixed. Next, we employ principal component analysis to study whether bug dependency metrics capture additional dimensions of a bug compared to traditional metrics. Finally, we build multivariate prediction models to explore whether considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Results: The experimental results suggest that: (1) bugs with more complex dependencies require more time to be fixed; (2) bug dependency metrics are complementary to traditional metrics; (3) considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Conclusions: These findings highlight the importance of considering bug dependencies in bug-fixing time prediction, and provide valuable insights into the potential impact of bug dependencies on software development processes.

Quality assurance (QA) tools are receiving more and more attention and are widely used by developers. Given the wide range of solutions for QA technology, it is still a question of evaluating QA tools. Most existing research is limited in the following ways: (i) They compare tools without considering scanning rules analysis. (ii) They disagree on the effectiveness of tools due to the study methodology and benchmark dataset. (iii) They do not separately analyze the role of the warnings. (iv) There is no large-scale study on the analysis of time performance. To address these problems, in the paper, we systematically select 6 free or open-source tools for a comprehensive study from a list of 148 existing Java QA tools. To carry out a comprehensive study and evaluate tools in multi-level dimensions, we first mapped the scanning rules to the CWE and analyze the coverage and granularity of the scanning rules. Then we conducted an experiment on 5 benchmarks, including 1,425 bugs, to investigate the effectiveness of these tools. Furthermore, we took substantial effort to investigate the effectiveness of warnings by comparing the real labeled bugs with the warnings and investigating their role in bug detection. Finally, we assessed these tools’ time performance on 1,049 projects. The useful findings based on our comprehensive study can help developers improve their tools and provide users with suggestions for selecting QA tools.

Abstract Mobile apps with tested Graphical User Interface (GUI) tend to have higher downloads in the apps store. In recent years, few efforts were made to analyse the research community and research status of the literature for GUI testing on mobile apps, which brings an obstacle to characterise and understand this field. In this study, the authors propose a systematic mapping study to gain insights into the field. First, the authors conduct an extensive search of relevant literature over seven popular digital libraries. From 4427 candidate studies, 114 primary studies published between January 2011 and September 2022 were selected. Next, the authors analyse these primary studies from the perspectives of bibliometric and qualitative analysis. For the bibliometric analysis, first, the authors analyse the popular research topics and their relationships. Second, the authors study the authors' community. For the qualitative analysis, the authors analyse the objectives, approaches and evaluation metrics employed in these primary studies. Their investigation reports several major findings: (1) there are relatively more studies on two topics, that is, test case generation and the automated test; (2) the most productive authors tend to collaborate and often have relatively broad research interests; (3) the functionality is the main objective of GUI testing; the model‐based approach is the most widely used.

Graphical User Interface (GUI) has become one of the most effective human-computer communication medium today. The quality of GUI is essential to the success of apps, especially for mobile apps. Developers not only have to understand the interaction of various components, but also follow the principles of design and implementation. It is helpful for developers to understand the challenges via analyzing the questions and answers (Q&A) on GUI development. However, there is no large-scale study on the GUI development posts on Stack Overflow. In this paper, we conduct an exploratory study on 23,741 posts related to GUI development on Stack Overflow. We first extract 20 topics related to GUI development using topic modeling. After manually classifying these GUI topics into 5 categories, we further quantitatively analyze the popularity and difficulty of GUI topics, the correlation between these two aspects, and qualitatively analyze the distribution of question types in posts. Finally, we have some interesting findings. These findings contain that the topic "tool selection" is the most popular topic, the topic "thread" has the highest percentage of unaccepted answers, and the topic "client/server" answer takes the longest time to be accepted. In addition, we discuss about possible inspirations of our research to GUI development stakeholders.

Graphical User Interface (GUI) is unavoidable in modern software apps. It facilitates the interactions between the users and the apps. As shown on the Google play store, some apps with higher downloads often have higher-quality, well-designed and tested GUI. GUI testing has become a necessary step in the app development process, and related research become a hot spot in recent years. However, there isn’t a review about GUI testing of mobile apps, which brings obstacles to new researchers. In this paper, we systematically review publications between 2010 and 2020, to gain an insight into GUI testing for mobile apps. Even though the earliest research was published around 1997 but we believe the considered years are likely to include the advances in the field. Specifically, the paper aims to identify (i) the main objectives of GUI testing, (ii) the approaches applied (iii) the evaluation metrics (iv) the challenges and future research directions. To cover all relevant literature, following a predefined systematic literature review procedure, involving both the automatic and manual search strategies, we found 75 primary studies. Four research questions are proposed to analyze them. We found that functionality is the main objective of GUI testing. Model-based testing is the most common approach. Metrics such as error detection, execution time, and code coverage are often used to evaluate the performance of GUI testing techniques. Finally, we outline some key challenges as well as possible research directions. We believe our work would provide a clue for new researchers as well as more research in GUI testing.

When implementing unfamiliar programming tasks, developers commonly search code examples and learn usage patterns of APIs from the code examples or reuse them by copy-pasting and modifying. For providing high-quality code examples, previous studies present several methods to recommend code snippets mainly based on information retrieval. In this paper, to provide better recommendation results, we propose ROSF, Recommending code Snippets with multi-aspect Features, a novel method combining both information retrieval and supervised learning. In our method, we recommend Top-K code snippets for a given free-form query based on two stages, i.e., coarse-grained searching and fine-grained re-ranking. First, we generate a code snippet candidate set by searching a code snippet corpus using an information retrieval method. Second, we predict probability values of the code snippets for different relevance scores in the candidate set by the learned prediction model from a training set, re-rank these candidate code snippets according to the probability values, and recommend the final results to developers. We conduct several experiments to evaluate our method in a large-scale corpus containing 921,713 real-world code snippets. The results show that ROSF is an effective method for code snippets recommendation and outperforms the-state-of-the-art methods by 20-41percent in Precision and 13-33 percent in NDCG.

As code search is a frequent developer activity in software development practices, improving the performance of code search is a critical task. In the text retrieval based search techniques employed in the code search, the term mismatch problem is a critical language issue for retrieval effectiveness. By reformulating the queries, query expansion provides effective ways to solve the term mismatch problem. In this paper, we propose Query Expansion based on Crowd Knowledge (QECK), a novel technique to improve the performance of code search algorithms. QECK identifies software-specific expansion words from the high quality pseudo relevance feedback question and answer pairs on Stack Overflow to automatically generate the expansion queries. Furthermore, we incorporate QECK in the classic Rocchio's model, and propose QECK based code search method QECKRocchio. We conduct three experiments to evaluate our QECK technique and investigate QECKRocchio in a large-scale corpus containing real-world code snippets and a question and answer pair collection. The results show that QECK improves the performance of three code search algorithms by up to 64 percent in Precision, and 35 percent in NDCG. Meanwhile, compared with the state-of-the-art query expansion method, the improvement of QECK Rocchio is 22 percent in Precision, and 16 percent in NDCG.

Existing approaches to image reconstruction in photoacoustic computed tomography (PACT) with acoustically heterogeneous media are limited to weakly varying media, are computationally burdensome, and/or cannot effectively mitigate the effects of measurement data incompleteness and noise. In this work, we develop and investigate a discrete imaging model for PACT that is based on the exact photoacoustic (PA) wave equation and facilitates the circumvention of these limitations. A key contribution of the work is the establishment of a procedure to implement a matched forward and backprojection operator pair associated with the discrete imaging model, which permits application of a wide-range of modern image reconstruction algorithms that can mitigate the effects of data incompleteness and noise. The forward and backprojection operators are based on the k-space pseudospectral method for computing numerical solutions to the PA wave equation in the time domain. The developed reconstruction methodology is investigated by use of both computer-simulated and experimental PACT measurement data.