相关论文: A Path Less Traveled: Reimagining Software Enginee…
Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software…
The rapid rise of Artificial Intelligence (AI) is reshaping Software Engineering (SE), creating new opportunities while introducing human-centered challenges. Although prior work notes behavioral and other non-technical factors in AI…
Cybersecurity demands both rapid pattern recognition and deliberative reasoning, yet purely neural or purely symbolic approaches each address only one side of this duality. Neuro-Symbolic (NeSy) AI bridges this gap by integrating learning…
We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a neurosymbolic…
This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LLMs to technical problems faced by software engineers. LLMs'…
This paper envisions a transformative paradigm in software engineering, where Artificial Intelligence, embodied in fully autonomous agents, becomes the primary driver of the core software development activities. We introduce a new class of…
Generative AI enables rapid ``vibe coding," where natural language prompts yield working software systems. While this lowers barriers to software creation, it also collapses the boundary between prototypes and engineered software, leading…
The recent proliferation of generative artificial intelligence (AI) technologies such as pre-trained large language models (LLMs) has opened up new frontiers in computational law. An exciting area of development is the use of AI to automate…
Engineering models created in Model-Based Systems Engineering (MBSE) environments contain detailed information about system structure and behavior. However, they typically lack symbolic planning semantics such as preconditions, effects, and…
Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.g. safety or structural constraints. As such, it represents one of the most promising avenues for reliable and…
Model-Driven Engineering (MDE) places models at the core of system and data engineering processes. In the context of research data, these models are typically expressed as schemas that define the structure and semantics of datasets.…
This manuscript signals a new era in the integration of artificial intelligence with software engineering, placing machines at the pinnacle of coding capability. We present a formalized, iterative methodology proving that AI can fully…
General logical reasoning, defined as the ability to reason deductively on domain-agnostic tasks, continues to be a challenge for large language models (LLMs). Current LLMs fail to reason deterministically and are not interpretable. As…
Automated algorithm design is entering a new phase: Large Language Models can now generate full optimisation (meta)heuristics, explore vast design spaces and adapt through iterative feedback. Yet this rapid progress is largely…
Software development automation is a long-term goal in software engineering. With the development of artificial intelligence (AI), more and more researchers are exploring approaches to software automation. They view AI systems as tools or…
Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper systematically reviews the emerging paradigm of LLM-based…
The rapid advancement of Large Language Models (LLMs) is reshaping software engineering by profoundly influencing coding, documentation, and system maintenance practices. As these tools become deeply embedded in developers' daily workflows,…
In this paper, we present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations, operating collaboratively with users or autonomously. Existing…
One of the goals of software design is to model a system in such a way that it is easily understandable. Nowadays the tendency for software development is changing from manual coding to automatic code generation; it is becoming model-based.…
The design of complex engineering systems is an often long and articulated process that highly relies on engineers' expertise and professional judgment. As such, the typical pitfalls of activities involving the human factor often manifest…