Dayi Lin

Agentic Software Engineering (SE 3.0) represents a new era where intelligent agents are tasked not with simple code generation, but with achieving complex, goal-oriented SE objectives. To harness these new capabilities while ensuring trustworthiness, we must recognize a fundamental duality within the SE field in the Agentic SE era, comprising two symbiotic modalities: SE for Humans and SE for Agents. This duality demands a radical reimagining of the foundational pillars of SE (actors, processes, tools, and artifacts), which manifest differently in each modality. This new vision of SE requires two distinct, purpose-built workbenches (aka tools) for these two collaborative modalities: the Agent Command Environment (ACE), a command center where humans orchestrate, mentor, and oversee agent teams while managing an inbox of agent-generated events like Merge-Readiness Packs (MRPs) and Consultation Request Packs (CRPs); and the Agent Execution Environment (AEE), a digital workbench where agents not only execute tasks but can proactively invoke human expertise when facing complex trade-offs or ambiguity. This bidirectional partnership, which supports agent-initiated human callbacks and handovers, gives rise to new, structured engineering activities (i.e., processes) that redefine human-AI collaboration, elevating the practice from agentic coding to true agentic software engineering. This paper presents the Structured Agentic Software Engineering (SASE) vision, outlining several of the foundational pillars for the future of SE. The paper culminates in a research roadmap that identifies a few key challenges and opportunities while briefly discussing the resulting impact of this future on SE education. Our goal is not to offer a definitive solution, but to provide a conceptual scaffold with structured vocabulary to catalyze a community-wide dialogue, pushing the SE community to think beyond its classic, human-centric tenets toward a disciplined, scalable, and trustworthy agentic future.

As Large Language Models (LLMs) achieve breakthroughs in complex reasoning, Codeforces-based Elo ratings have emerged as a prominent metric for evaluating competitive programming capabilities in industry. However, these ratings are often reported without critical experimental details, leading to significant discrepancies-illustrated by recent reports where the same model version’s score fluctuated by nearly 500 points (out of 2,029). This paper presents a systematic empirical study on the hidden factors biasing Elo evaluations: (1) the temporal ordering of submissions, (2) contest difficulty selection, and (3) run-to-run stochastic variability of LLMs. Utilizing a controlled benchmark of 37 recent Codeforces contests and 13,691 generated test cases, we demonstrate that Elo scores are highly sensitive to these parameters. Our findings reveal that varying submission orders can shift scores by 394 points, while contest selection can cause differences of up to 1,122 points for the same model. Run-to-run performance exhibits substantial instability, with a maximum difference of 349 points in mean scores observed when evaluating identical contests. We conclude that direct Elo comparisons are unreliable and potentially misleading without strict standardization and transparent reporting of experimental settings.

The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware, software systems that integrate FM(s) as core components. While building demonstration-level FMware is relatively straightforward, transitioning to production-ready systems presents numerous challenges, including reliability, high implementation costs, scalability, and compliance with privacy regulations. Our paper conducts a semi-structured thematic synthesis to identify key challenges in productionizing FMware across diverse data sources, including our industry experience developing FMArts, a FMware lifecycle engineering platform, and its integration into Huawei Cloud; grey literature; academic publications; hands-on involvement in the Open Platform for Enterprise AI (OPEA); organizing the AIware conference and bootcamp; and co-leading the ISO SPDX SBOM working group on AI and datasets. We identify critical issues in FM(s) selection, data and model alignment, prompt engineering, agent orchestration, system testing, and deployment, alongside cross-cutting concerns such as memory management, observability, and feedback integration. We discuss necessary technologies and strategies to address these challenges and offer guidance to enable the transition from demonstration systems to scalable, production-ready FMware solutions. Our findings underscore the importance of continued research and multi-industry collaboration to advance the development of production-ready FMware.

“Vibe coding” represents a paradigm shift where non-technical users, or “software makers”, create applications by describing the desired outcome, focusing entirely on the final output rather than the underlying code. This movement, propelled by hyper-growth platforms like Lovable, Bubble, and Glide, actualizes the vision of “AIware”: software for all, by all. However, this remarkable progress is currently confined to applications of limited complexity and introduces significant risks of unreliability and failure. This 90-minute technical briefing introduces Vibe Engineering, a structured framework for AI-assisted software creation that goes beyond one-shot prompts. The framework operates by defining roles, specifying artifacts, and orchestrating a repeatable process. It is built on two foundational pillars: (1) Intent Engineering, which focuses on deeply understanding user needs through multi-agent dialogues, Theory of Mind (ToM) principles, and test-driven requirement refinement; and (2) Realization Engineering, a specialized Agent Execution Environment (AEE) where multi-agent teams autonomously build, test, and verify the software based on the user’s validated intent. This briefing provides an insider’s view of the technical challenges in this domain and surfaces concrete software engineering research opportunities.

The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered coding assistants, has shown promise in improving developer productivity. However, it has also exposed inherent limitations, such as cognitive overload on developers and inefficiencies. We propose a shift towards Software Engineering 3.0 (SE 3.0), an AI-native approach characterized by intent-centric, conversation-oriented development between human developers and AI teammates. SE 3.0 envisions AI systems evolving beyond task-driven copilots into intelligent collaborators, capable of deeply understanding and reasoning about software engineering principles and intents. We outline the key components of the SE 3.0 technology stack, which includes Teammate.next for adaptive and personalized AI partnership, IDE.next for intent-centric conversation-oriented development, Compiler.next for multi-objective code synthesis, and Runtime.next for SLA-aware execution with edge-computing support. Our vision addresses the inefficiencies and cognitive strain of SE 2.0 by fostering a symbiotic relationship between human developers and AI, maximizing their complementary strengths. We also present a roadmap of challenges that must be overcome to realize our vision of SE 3.0. This paper lays the foundation for future discussions on the role of AI in the next era of software engineering.

Large Language Models (LLMs) achieve strong performance across many tasks but suffer from high inference latency due to autoregressive decoding. The issue is exacerbated in Large Reasoning Models (LRMs), which generate lengthy chains of thought. While speculative decoding accelerates inference by drafting and verifying multiple tokens in parallel, existing methods operate at the token level and ignore semantic equivalence (i.e., different token sequences expressing the same meaning), leading to inefficient rejections. We propose SemanticSpec, a semantic-aware speculative decoding framework that verifies entire semantic sequences instead of tokens. SemanticSpec introduces a semantic probability estimation mechanism that probes the model's internal hidden states to assess the likelihood of generating sequences with specific meanings. Experiments on four benchmarks show that SemanticSpec achieves up to 2.7x speedup on DeepSeekR1-32B and 2.1x on QwQ-32B, consistently outperforming token-level and sequence-level baselines in both efficiency and effectiveness.