This paper reports early findings from a study investigating how explainability requirements are elicited, specified, and validated using established RE techniques in an industrial context.

This paper compares four requirements elicitation approaches using AI-supported collaboration and evaluates their impact on requirements artifact quality and stakeholder perceptions.

The paper introduces MAAD, a multi-agent framework that autonomously transforms software requirements into comprehensive, multi-view architectural blueprints, significantly improving completeness and…

This paper proposes a new paradigm for human-machine cooperation in software engineering, where machines amplify engineers' reasoning through causation.

The paper introduces GAIATrace, a comprehensive token-level dataset, and Vidur-Agent, a simulator, to enable reproducible and detailed system-level characterization of complex multi-model agentic AI s…

This paper analyzes the performance of agentic LLM systems in complex binary reverse engineering, identifying key limitations such as handling obfuscation and token constraints, and proposing future d…

This paper studies AI development frameworks for software engineering and proposes a six-dimension process taxonomy.

The paper introduces an ontology-driven framework, From Prompts to Context, to explicitly model and structure the often-opaque context of human-Generative AI collaborations, thereby improving traceabi…

The paper proposes a theoretical framework, called constraint-coupled reasoning, to make AI models less susceptible to knowledge distillation by coupling high-level capabilities to internal stability…

This paper presents an approach for turning exploratory large language model prototypes into auditable applications with traceable, auditable architecture.

The authors conducted a user study to assess the effectiveness of their interactive visual query tracer and builder tools for Nemo, a Datalog reasoner, in helping students learn Datalog.

This paper documents and analyzes the failure process of strategies used to address conceptual drift in long-horizon LLM collaboration and introduces the concept of 'Index Sickness' and the 'Pang Prin…

This paper introduces PromptMN, a domain-specific language for annotating natural language prompts to clarify roles, goals, and constraints for AI models, reducing context ambiguities and repair cycle…

The paper proposes TraceDev, a multi-agent framework for automated software development grounded in use cases, achieving higher success rates than baseline approaches in repository-level code generati…

The paper proposes Faithful Agentic XAI (FAX), a verification framework that explicitly checks LLM-generated explanations against model behavior, significantly improving explanation faithfulness on a…

This paper compares the quality and coverage of epic-organized LLM-generated Gherkin acceptance criteria with requirement-aligned generation, using four requirements documents from the PURE dataset.

This tutorial explores advances and challenges in deploying large language model-based agentic systems across industries, with a focus on reasoning and planning, multi-agent coordination, and evaluati…

The paper argues that current 'on-the-fly' AI agent design lacks necessary software engineering rigor and proposes an 'AI Workflow Store' to provide hardened, reusable, and reliable agent workflows.