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FASE 2025: Fundamental Approaches to Software Engineering

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FASE
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截稿日期:
2024-10-10
通知日期:
2024-12-20
会议日期:
2025-05-03
会议地点:
Hamilton, Canada
届数:
28
ICORE: B   QUALIS: A2   浏览: 47170   关注: 20   参加: 1

会伴指数 (CP-I)

83.3 / 100
全站第 154 名 / 共 5,687 个会议 · 前 3%

软件工程 第 20 / 247

学术认可 (35%)
92
投稿选择性 (20%)
84
会议传承 (20%)
91
社区关注 (10%)
41
资料公开度 (15%)
80

用到的输入: 收录等级：ICORE B, QUALIS A2 · 录用率：25.3%（有记录的 5 届的均值） · 有据可查的届次：28 · 在会伴关注它的研究者：20 人 · 过去 24 个月打开过本页的研究者：4 人

公开资料里还缺: 历届信息 (+3.0)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-28

征稿

FASE 2025 (Fundamental Approaches to Software Engineering) is a ICORE B / QUALIS A2 conference held in Hamilton, Canada on 2025-05-03. The paper submission deadline is 2024-10-10. Acceptance notifications are sent on 2024-12-20.

General Information FASE is concerned with the foundations on which software engineering is built. Submissions should make novel contributions to making software engineering a more mature and soundly-based discipline. Contributions should be supported by appropriate arguments and validation. Contributions that combine the development of conceptual and methodological advances with their formal foundations and tool support are particularly encouraged. We welcome contributions to all such fundamental approaches, including: applications of AI to software engineering, including search-based, learning-based approaches, and advances in code generation and program synthesis, automated testing and debugging, software documentation and program understanding, intelligent code review and refactoring, DevOps and AIOps, and applications of large language models (LLMs). software engineering foundations for AI-based systems, for example with focus on: explainability, transparency and trust, data management, testing and verification, lifecycle management, system architecture; software quality and testing: validation and verification of functional and non-functional software properties ( including security and data privacy) using techniques such as theorem proving, model checking, testing, analysis, simulation, refinement methods, metrics or visualization techniques; software engineering as an engineering discipline, including its interaction with and impact on society and economics; ethical and responsible software engineering: prioritizing fairness, transparency, and accountability in AI and software systems while addressing societal concerns like inequality and bias; requirements engineering: capture, consistency, and change management of software requirements, natural language processing for requirement elicitation; software architectures: description and analysis of the architecture, e.g., SOA, microservice architectures, cloud-native architectures, event-driven architectures, model-driven architectures, or software product lines; specification, design, and implementation of particular classes of systems: (self-)adaptive, autonomous, collaborative, intelligent, embedded, distributed, mobile, pervasive, cyber-physical, data-centric, service-oriented applications, multi-agent systems, safety-critical, real-time, blockchain-based, mixed-reality systems, human-in-the-loop systems or digital twins; model-driven engineering: model transformation, meta-modelling, design and semantics of domain-specific languages, consistency and synchronization of models, generative architectures, low-code and no-code development; software processes: support for iterative, agile, and open source development; software evolution: refactoring, reverse and re-engineering, configuration management and architectural change, mining software repositories.
由 Dou Sun 最后更新于

录用率

平均录用率: 25.9% 14 年间 (1998–2013).

年份提交数录用数录用率(%)
20131122623.2%
20121343324.6%
2011992929.3%
2010962425%
20091243024.2%
20071413021.3%
20061662716.3%
2005992222.2%
2004912224.2%
2003892022.5%
2002602135%
2001742229.7%
2000602135%
1998591830.5%

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