Hybrid work has become firmly established as a standard practice in software development in the post-pandemic era. In agile environments, meetings are pivotal for collaboration, making thoughtful consideration of their formats essential. This study explores how a broad range of recurring meetings are organized and carried out in hybrid work settings within a large-scale agile environment. It also examines key meeting-related aspects such as camera usage, role-based differences in meeting load and overload, and multitasking behaviors. We conducted a case study at Ericsson, a multinational telecommunications company, combining semi-structured interviews with 27 participants and survey responses from 58 individuals across two units. Our results show that meeting formats should align with intent. Remote meetings were efficient for formal information sharing, while collaborative and socially oriented meetings benefited from in-person interaction. Office attendance policy models influenced format choices, with fixed office days allowing for fully in-person or remote meetings. The more flexible model necessitated hybrid meetings, which required well-equipped meeting spaces and reliable IT infrastructure for seamless collaboration. Camera use supported engagement in discussion-heavy virtual meetings, and perceptions of meeting overload varied by role and were not solely time-based. Frequent multitasking was generally limited and reflected role-specific demands rather than disengagement.
This paper addresses the security challenges of EOSIO smart contracts, which are deployed in WebAssembly bytecode, frequently targeted by attacks, and inherently difficult to analyze. Existing approaches typically suffer limitations that either rely on symbolic execution with handcrafted rules, which faces scalability and coverage limits, or emphasize purely dynamic fuzzing, which often lacks semantic precision. To this end, we propose a deep learning based detection approach that performs joint modeling and analysis of both static and dynamic characteristics. On the static side, we construct graph representations from multiple perspectives, including control-flow and data-dependency graphs, and semantic paths, to capture the structural and semantic properties of contracts. On the dynamic side, we design a multi-granularity and configurable instrumentation mechanism within the Wasm execution environment to collect runtime traces, system API invocations, and contextual signals related to permissions and notifications. Based on both structural and behavioral information, we build a multimodal learning model to detect four representative categories of EOSIO-specific vulnerabilities. Experimental results demonstrate that our method achieves superior detection performance, with an average precision of 84.7% and a Macro-F1 score of 85.3%, outperforming representative static and dynamic baselines in our evaluation.
For widespread deployment of autonomous driving systems (ADS), perception robustness plays a fundamental role. However, maintaining robust perception performance remains a significant challenge, especially under occlusion-rich conditions, known as blind-spot driving scenarios. In these scenarios, traffic participants are temporarily occluded by surrounding objects, making detection and tracking significantly more difficult. However, existing datasets lack sufficient coverage of such scenarios, hindering comprehensive evaluation and improvement of perception capabilities under such conditions. In light of this, we systematically model blind-spot scenarios for the first time and develop a corresponding framework, BHunter , for generating perception test cases. Specifically, this framework enables controllable synthesis of sensor data corresponding to these scenarios, such as generating data with specified occlusion levels, to effectively test perception models. With the help of BHunter , we construct a dataset of 2,000 blind-spot driving scenarios, covering five occlusion levels with a balanced distribution. Each scenario includes synchronized multimodal sensor data, accurate 3D annotations, and temporally consistent frames, and can be directly used for training and evaluating state-of-the-art perception models. To demonstrate the effectiveness of our benchmark, we evaluate widely used detection and tracking models across various sensor configurations. The results show that the models retrained on our benchmark achieve improvements of up to 30.74 and 36.96 percentage points in detection and tracking tasks, respectively, under high-occlusion conditions. These results confirm that our benchmark fills a critical gap in the perception testing landscape, offering a valuable foundation for improving ADS safety in blind-spot scenarios.
Large Language Models (LLMs) have advanced autonomous agents’ planning and decision-making, yet they struggle with complex tasks requiring diverse expertise and multi-step reasoning. Multi-Agent Debate (MAD) systems, introduced in NLP research, address this gap by enabling structured debates among LLM-based agents to refine solutions iteratively. MAD promotes divergent thinking through role-specific agents, dynamic interactions, and structured decision-making. Recognizing parallels between Software Engineering (SE) and collaborative human problem-solving, this study investigates MAD's effectiveness on four coding tasks in SE. We adapt a MAD framework from NLP, analyze agent interactions to assess consensus-building and iterative refinement, and propose two MAD variants that enhance agent debate for coding tasks by addressing the observed weaknesses. Our findings show that structured debate and collaboration improve problem-solving and yield strong performance in some cases, highlighting the collaborative debate synergy between the LLM-agents for coding tasks in SE while identifying areas for exploration.
IT professionals operate in a fast-paced, high-pressure environment that demands rapid delivery of quality solutions and constant adaptation to new technologies. In this context, well-being and job satisfaction are critical to both individual and organizational success, influencing their productivity, team dynamics, creativity, problem-solving capabilities, and software quality. This study explores how human values relate to job satisfaction and well-being in Thailand, focusing on their importance in personal life, relevance at work, and practical application. Using a mixed-methods approach, data were collected from 169 IT Thai professionals in Thailand through a 98-item survey and semi-structured interviews with 42 participants. Quantitative analysis revealed that Benevolence, Hedonism, and Self-direction are most valued personally, while Security, Hedonism, and Self-direction are prioritized at work. Among these, Hedonism at work had the strongest positive correlation with job satisfaction. However, a mismatch between the importance of Hedonism as a personal or professional value versus how it is actually applied at work, was significantly associated with lower job satisfaction. Interviews provided deeper insights into how participants interpret and apply these values, the challenges they face, and their perceived impact on well-being and satisfaction. The findings aim to empower Thai TI professionals and guide organizations to take informed steps to create healthier, more supportive work environments. This may help not only to empower these professionals to build more fulfilling careers, but can also helps organizations enhance performance, innovation, and talent retention in software development teams.
