会议信息

ESEM 2026: International Symposium on Empirical Software Engineering and Measurement

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截稿日期:
2026-04-22 已截稿
通知日期:
2026-06-30
会议日期:
2026-10-04
会议地点:
Munchen, Germany
届数:
20
CCF: B   ICORE: A   QUALIS: A2   浏览: 154663   关注: 64   参加: 5
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会伴指数 (CP-I)

72.7 / 100
全站第 292 名 / 共 5,705 个会议 · 前 6%

电气与电子工程 第 8 / 512 软件工程 第 37 / 247

学术认可 (35%)
92
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
82
社区关注 (10%)
66
资料公开度 (15%)
50

用到的输入: 收录等级：CCF B, ICORE A, QUALIS A2 · 有据可查的届次：20 · 在会伴关注它的研究者：64 人 · 过去 24 个月打开过本页的研究者：29 人

公开资料里还缺: 历年录用率 (+4.5) · 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 80% —— 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 1.2 · 算于 2026-10-16

征稿

ESEM 2026 (International Symposium on Empirical Software Engineering and Measurement) is a CCF B / ICORE A / QUALIS A2 conference held in Munchen, Germany on 2026-10-04. The paper submission deadline is 2026-04-22. Acceptance notifications are sent on 2026-06-30.

The International Symposium on Empirical Software Engineering and Measurement (ESEM) technical papers (main) track features submissions that describe original, unpublished work in software engineering and software measurement, with a strong empirical foundation. Papers in this track should communicate fully developed research and results. Strong emphasis should be given to the methodological aspects of the research and the assessment of the validity of the contributions. Please note: Open Science policy: ESEM is open by default. Submissions must comply with the Open Science Policy and provide the artifacts needed to understand and reproduce the analysis, unless legal/ethical barriers exist (see details on Data Availability below). Double-anonymous review format: ESEM 2026 uses double blind reviewing. Submissions must not reveal authors’ identities. General scope of submissions Submissions must not be under consideration for publication or presentation elsewhere. In addition to the specific scope of this track, submissions may address any aspect of software engineering but must tackle the problem from an empirical perspective and using a rigorous empirical method, including (but not limited to): Empirical studies using qualitative, quantitative, and mixed methods Cross- and multi-disciplinary methods and studies Controlled experiments and quasi-experiments Case studies, action research, ethnography, and field studies Survey research Simulation studies Artifact studies Data mining using statistical, machine learning, and AI-based approaches Meta-studies and evidence syntheses, including: Systematic literature reviews and rapid reviews with a strong synthesis component and a clear contribution beyond overview summarization Meta-analyses, and qualitative, quantitative, or structured syntheses of prior studies that generate clear new insights Replication studies Papers should be positioned in terms of research methodology and contribution in relation to established frameworks for empirical software engineering and measurement. Negative results are also welcome as long as the research is based on well-motivated hypotheses in line with the state-of-the-art, relevant scientific knowledge that is then well-documented, analysed in depth, and provides clear value to the community. Topics of interest (illustrative, not exhaustive) Topics in scope for ESEM 2026 and commonly addressed using an empirical approach include, but are not limited to: Evaluation and comparison of software models, tools, techniques, and practices Modeling, measuring, and assessing product or process quality and productivity Continuous software engineering Software verification and validation, including analysis and testing Engineering of software systems that include machine learning components and data dependencies Applications of software engineering to different types of systems and domains (e.g., IoT, cyber-physical systems, Industry 4.0) Human factors, teamwork, and behavioral aspects of software engineering We also welcome submissions that relate to meta-topics around empirical software engineering research, including for example: Development, evaluation, and comparison of empirical approaches and methods Infrastructure, techniques, and tools for conducting and supporting empirical studies
由 Dou Sun 最后更新于

最佳论文

年份最佳论文
2018A Longitudinal Cohort Study on the Retainment of Test-driven Development
2018Relationship Between Geographical Location and Evaluation of Developer Contributions in Github
2018What if a Bug has a Different Origin? Making Sense of Bugs Without an Explicit Bug Introducing Change
2018Automatic Topic Classification of Test Cases Using Text Mining at an Android Smartphone Vendor
2018Prediction of relatedness in stack overflow: deep learning vs. SVM: a reproducibility study
2018Comparing Techniques for Aggregating Interrelated Replications in Software Engineering
2017Quantifying the Transition from Python 2 to 3: An Empirical Study of Python Applications
2017An empirical analysis of FLOSS repositories to compare One-Time Contributors to Core and Periphery Developers
2017REACT: An Approach for Capturing Rationale in Chat Messages
2017Early Phase Cost Models for Agile Software Processes in the US DoD
2016Experiences from Measuring Learning Potential and Performance in Large-Scale Distributed Software Development
2016Using Forward Snowballing to update Systematic Reviews in Software Engineering
2016An External Replication on the Effects of Test-driven Development Using Blind Analysis
2015An Exploratory Study on the Evolution of C Programming in the Unix Operating System
2015How to Make Best Use of Cross-Company Data for Web Effort Estimation?
2015Don't Call Us, We'll Call You: Characterizing Callbacks in JavaScript
2014Evaluating strategies for study selection in systematic literature studies
2014Networking in a Large-Scale Distributed Agile Project
2014Towards a Framework to Support Large Scale Sampling in Software Engineering Surveys
2014Discovering Buffer Overflow Vulnerabilities In The Wild: An Empirical Study
2013Towards a Metric Suite Proposal to Quantify Confirmation Biases of Developers
2013Evaluating software product metrics with synthetic defect data
2013Benchmarking Usability and Performance of Multicore Languages
2012Handling Categorical Variables in Effort Estimation
2012Experimental Assessment of Software Metrics using Automated Refactoring
2011One Technique is Not Enough: A Comparison of Vulnerability Discovery Techniques
2011An Empirical Investigation of Systematic Reviews in Software Engineering
2011End-User Programmers and their Communities: An Artifact-Based Analysis
2011Design of an Empirical Study for Comparing the Usability of Concurrent Programming Languages
2011An Empirical Study on the Use of Team Building Criteria in Software Projects
2011Scrum + Engineering Practices: Experiences of Three Microsoft Teams
2010Transition from a plan-driven process to Scrum: a longitudinal case study on software quality
2010Trust dynamics in global software engineering
2010Are developers complying with the process: an XP study
2009Using differences among replications of software engineering experiments to gain knowledge
2008Socio-technical congruence: a framework for assessing the impact of technical and work dependencies on software development productivity
2007The Effects of Over and Under Sampling on Fault-prone Module Detection
2007Toward Reducing Fault Fix Time: Understanding Developer Behavior for the Design of Automated Fault Detection Tools

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相关期刊

CCF全称影响因子出版商ISSN
Measurement5.2Elsevier0263-2241
BEmpirical Software Engineering3.6Springer1382-3256
Measurement: SensorsElsevier2665-9174
Measurement and Control1.300SAGE0020-2940
BSoftware & Systems Modeling3.2Springer1619-1366
BPattern Recognition7.6Elsevier0031-3203
BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
BInformation Sciences6.0Elsevier0020-0255
BComputer Networks4.7Elsevier1389-1286
BComputers & Security5.4Elsevier0167-4048

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