GitHub actions (GHA), a built-in continuous integration and continuous delivery (CI/CD) service of GitHub, has been widely adopted by developers, streamlining the automation of software development workflows. Despite its popularity, failures frequently occur during GHA workflow executions. Fixing these failures often requires significant human effort, and unsuccessful workflow executions waste computing resources. Understanding the reasons behind workflow failures could provide valuable insights for troubleshooting the existing issues of CI/CD and further improving the development process. In this article, we present an empirical study to reveal the reasons behind GHA workflow failures. By manually analyzing 375 failed workflow executions across 260 open-source Java projects, we built a comprehensive taxonomy categorizing the common failure types. The taxonomy was further validated by surveying 151 developers. This study is the first empirical work to analyze GHA workflow failures, bringing valuable knowledge to the field of continuous integration in software engineering. Moreover, our taxonomy and survey results not only underscore the critical need for better tools and practices to mitigate these failures but also indicate the directions to enhance the efficiency and reliability of CI/CD pipelines.

Software logs provide a rich source of data for tracing, debugging, and detecting software bugs. However, the valuable data contained within logs attached to vulnerability reports remains largely unexplored. This study aims to bridge this gap by investigating the characteristics, rationales, and potential of logs for software vulnerability management. We conduct a comprehensive analysis of 1,118 Common Vulnerabilities and Exposures (CVEs) linked to issue reports, specifically focusing on the distribution and content of logs included in these reports. Our analysis reveals that exception logs are the most prevalent type across various vulnerability categories and life cycle phases. In addition, we further discover seven key rationales for attaching logs to vulnerability reports, highlighting the multifaceted role of logs in vulnerability reporting and analysis. Furthermore, we explore the feasibility of using logs to assist in vulnerability management, specifically for vulnerability location and security issue detection. Our experiments show that exception logs effectively target at least one vulnerable function in 65.6% of analyzed vulnerabilities. To support security issue detection, we apply three different approaches, i.e., heuristic rule-based, K-means++, and Latent Dirichlet Allocation. We evaluate the three approaches on a total of 158,730 issue reports from 72 projects hosted on GitHub and Bugzilla. The results show that heuristic rule-based and K-means++ approaches successfully identify true security issues, with a precision of 44.1% and 46.7% respectively. Overall, our findings highlight the significant potential of analyzing logs in vulnerability reports to strengthen software security practices and inspire future studies.

The mismatched pins inspection of the complex aviation connector is a critical process to ensure the correct wiring harness assembly, of which the existing manual operation is error-prone and time-consuming. Aiming to fill this gap, this article proposes an augmented reality (AR)-assisted deep learning-based approach to tackle three major challenges in the aviation connector inspection, including the small pins detection, multipins sequencing, and mismatched pins visualization. First, the proposed spatial-attention pyramid network approach extracts the image features in multilayers and searches for their spatial relationships among the images. Second, based on the cluster-generation sequencing algorithm, these detected pins are clustered into annuluses of expected layers and numbered according to their polar angles. Finally, the AR glass as the inspection visualization platform, highlights the mismatched pins in the augmented interface to warn the operators automatically. Compared with the other existing methodologies, the experimental result shows that the proposed approach can achieve better performance accuracy and support the operator's inspection process efficiently.

Assembly guided by paper documents is the most widespread type used in the process of aircraft cable assembly. This process is very complicated and requires assembly workers with high-level skills. The technologies of wearable Augmented Reality (AR) and portable visual inspection can be exploited to improve the efficiency and the quality of cable assembly. In this study, we propose a smart assistance system for cable assembly that combines wearable AR with portable visual inspection. Specifically, a portable visual device based on binocular vision and deep learning is developed to realize fast detection and recognition of cable brackets that are installed on aircraft airframes. A Convolutional Neural Network (CNN) is then developed to read the texts on cables after images are acquired from the camera of the wearable AR device. An authoring tool that was developed to create and manage the assembly process is proposed to realize visual guidance of the cable assembly process based on a wearable AR device. The system is applied to cable assembly on an aircraft bulkhead prototype. The results show that this system can recognize the number, types, and locations of brackets, and can correctly read the text of aircraft cables. The authoring tool can assist users who lack professional programming experience in establishing a process plan, i. e., assembly outline based on AR for cable assembly. The system can provide quick assembly guidance for aircraft cable with texts, images, and a 3D model. It is beneficial for reducing the dependency on paper documents, labor intensity, and the error rate.