A sentiment analysis scheme for image and text comments based on multimodal deep learning and spatiotemporal attention is proposed to address the issues of incomplete spatiotemporal considerations, incomplete implemen...
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Automated test generation is an area that has seen a lot of research and development, resulting in many test automation methods and tools for test design. However, practitioners often face challenges in adop...
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The design and implementation of a bird repeller based on the MLX90640 infrared sensor is proposed. The system can carry out real-time temperature measurement and accurate bird repellent. After the experimental verifi...
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Leadership in agile teams is a collective responsibility where team members share leadership work based on expertise and skills. However, the understanding of leadership in this context is limited. This study explores...
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ISBN:
(纸本)9783031783852;9783031783869
Leadership in agile teams is a collective responsibility where team members share leadership work based on expertise and skills. However, the understanding of leadership in this context is limited. This study explores the under-researched area of prototypical leadership, aiming to understand if and how leaders who are perceived as more representative of the team are more effective leaders. Qualitative interviews were conducted with eleven members of six agile software teams in five Swedish companies from various industries and sizes. In this study, the effectiveness of leadership was perceived as higher when it emerged from within the team or when leaders aligned with the group. In addition, leaders in managerial roles that align with the team's shared values and traits were perceived as more effective, contributing to overall team success.
As the present development process is more customer centric and demands quick delivery within a given budget. Agile software Development represents a drastic change in software development approaches focusing more on ...
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Dialogue policy trains an agent to select dialogue actions frequently implemented via deep reinforcement learning (DRL). The model-based reinforcement methods built a world model to generate simulated data to alleviat...
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The 7th edition of the Competition on software Testing (Test-Comp 2025) provides an overview and comparative evaluation of automatic test-suite generators for C programs. The experimental evaluation was performed on a...
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This paper proposes a deep learning model for predicting the next activities in a temporal sequence of activity-events associated with a single process instance. The proposed model is based on a mixture of experts (Mo...
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The proceedings contain 29 papers. The special focus in this conference is on Requirements engineering: Foundation for software Quality. The topics include: Towards Ethics-Driven Requirements engineering: Integrating ...
ISBN:
(纸本)9783031885303
The proceedings contain 29 papers. The special focus in this conference is on Requirements engineering: Foundation for software Quality. The topics include: Towards Ethics-Driven Requirements engineering: Integrating Critical Systems Heuristics and Ethical Guidelines for Autonomous Vehicles;refining and Validating Change Requests from a Crowd to Derive Requirements;do Users’ Explainability Needs in software Change with Mood?;exploring and Characterizing Ad-Hoc Requirements - A Case Study at a Large-Scale Systems Provider;feReRe: Feedback Requirements Relation Using Large Language Models;how Does Users’ App Knowledge Influence the Preferred Level of Detail and Format of software Explanations?;How Effectively Do LLMs Extract Feature-Sentiment Pairs from App Reviews?;an Interactive Tool for Goal Model Construction Using a Knowledge Graph;Generating Domain Models with LLMs Using Instruction Tuning: A Research Preview;A Systematic Literature Review of KAOS Extensions;LACE-HC: A Lightweight Attention-Based Classifier for Efficient Hierarchical Classification of software Requirements;requirements Representations in Machine Learning-Based Automotive Perception Systems Development for Multi-party Collaboration;automatic Prompt engineering: The Case of Requirements Classification;exploring Generative Pretrained Transformers to Support Sustainability Effect Identification - A Research Preview;prompt Me: Intelligent software Agent for Requirements engineering - A Vision Paper;detecting Redundancies Between User Stories with Graphs and Large Language Models;Leveraging Requirements Elicitation through software Requirement Patterns and LLMs;ReqRAG: Enhancing software Release Management through Retrieval-Augmented LLMs: An Industrial Study;the Potential of Citizen Platforms for Requirements engineering of Large Socio-Technical software Systems;end-User Requirements Modelling: An Experience Report from Digital Agriculture;requirements Elicitation Workshops Using the Six Thinking Hat
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to higher costs and delays in the softw...
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ISBN:
(纸本)9783031808883;9783031808890
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to higher costs and delays in the software release cycle due to their labor-intensive nature. Large Language Models (LLMs) present a promising solution to these challenges. However, their application generally demands multiple rounds of human-driven prompt engineering, which limits their practical deployment, particularly for industrial end-users who need reliable and efficient results. In this paper, we propose GoNoGo, an LLM agent system designed to streamline automotive software deployment while meeting both functional requirements and practical industrial constraints. Unlike previous systems, GoNoGo is specifically tailored to address domain-specific and risk-sensitive systems. We evaluate GoNoGo's performance across different task difficulties using zero-shot and few-shot examples taken from industrial practice. Our results show that GoNoGo achieves a 100% success rate for tasks up to Level 2 difficulty with 3-shot examples, and maintains high performance even for more complex tasks. We find that GoNoGo effectively automates decision-making for simpler tasks, significantly reducing the need for manual intervention. In summary, GoNoGo represents an efficient and user-friendly LLM-based solution currently employed in our industrial partner's company to assist with software release decision-making, supporting more informed and timely decisions in the release process for risk-sensitive vehicle systems.
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