ACM SIGSOFT SEN Empirical Software Engineering: Introducing Our New Regular Column

• URL: http://arxiv.org/abs/2510.02007v2
• Date: Wed, 15 Oct 2025 11:17:59 GMT
• Title: ACM SIGSOFT SEN Empirical Software Engineering: Introducing Our New Regular Column
• Authors: Justus Bogner, Roberto Verdecchia,
• Abstract summary: ESE research still needs to keep evolving as new impediments, shortcoming, and technologies emerge.<n>With this new regular ACM SIGSOFT SENSEN-ESE column, we introduce a venue for discussing meta-aspects of ESE research.<n>Our aim for the column is to be a place where we can regularly spark conversations on ESE topics that might not often be touched upon or are left implicit.
• Score: 7.447436322111677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: From its early foundations in the 1970s, empirical software engineering (ESE) has evolved into a mature research discipline that embraces a plethora of different topics, methodologies, and industrial practices. Despite its remarkable progress, the ESE research field still needs to keep evolving, as new impediments, shortcoming, and technologies emerge. Research reproducibility, limited external validity, subjectivity of reviews, and porting research results to industrial practices are just some examples of the drivers for improvements to ESE research. Additionally, several facets of ESE research are not documented very explicitly, which makes it difficult for newcomers to pick them up. With this new regular ACM SIGSOFT SEN column (SEN-ESE), we introduce a venue for discussing meta-aspects of ESE research, ranging from general topics such as the nature and best practices for replication packages, to more nuanced themes such as statistical methods, interview transcription tools, and publishing interdisciplinary research. Our aim for the column is to be a place where we can regularly spark conversations on ESE topics that might not often be touched upon or are left implicit. Contributions to this column will be grounded in expert interviews, focus groups, surveys, and position pieces, with the goal of encouraging reflection and improvement in how we conduct, communicate, teach, and ultimately improve ESE research. Finally, we invite feedback from the ESE community on challenging, controversial, or underexplored topics, as well as suggestions for voices you would like to hear from. While we cannot promise to act on every idea, we aim to shape this column around the community interests and are grateful for all contributions.

Related papers

• Deep Research: A Systematic Survey [118.82795024422722]
  Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
  arXiv  Detail & Related papers  (2025-11-24T15:28:28Z)
• Twenty-Five Years of MIR Research: Achievements, Practices, Evaluations, and Future Challenges [68.49490211993141]
  We trace the evolution of Music Information Retrieval (MIR) over the past 25 years.<n>MIR gathers all kinds of research related to music informatics.<n>We review a set of successful practices that fuel the rapid development of MIR research.
  arXiv  Detail & Related papers  (2025-11-10T15:32:23Z)
• LIR: The First Workshop on Late Interaction and Multi Vector Retrieval @ ECIR 2026 [17.097147033209037]
  Late interaction retrieval methods, pioneered by ColBERT, have emerged as a powerful alternative to single-vector neural IR.<n>They have been demonstrated to deliver strong generalisation and robustness, particularly in out-of-domain settings.<n>They have recently been shown to be particularly well-suited for novel use cases, such as reasoning-based or cross-modality retrieval.
  arXiv  Detail & Related papers  (2025-11-01T08:21:33Z)
• The Budget AI Researcher and the Power of RAG Chains [4.797627592793464]
  Current approaches to supporting research idea generation often rely on generic large language models (LLMs)<n>Our framework, The Budget AI Researcher, uses retrieval-augmented generation chains, vector databases, and topic-guided pairing to recombine concepts from hundreds of machine learning papers.<n>The system ingests papers from nine major AI conferences, which collectively span the vast subfields of machine learning, and organizes them into a hierarchical topic tree.
  arXiv  Detail & Related papers  (2025-06-14T02:40:35Z)
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Not real or too soft? On the challenges of publishing interdisciplinary software engineering research [4.597329752530121]
  Discipline of software engineering combines social and technological dimensions.<n>Interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics.
  arXiv  Detail & Related papers  (2025-01-11T12:18:46Z)
• Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers [1.4841630983274845]
  Lack of transparency, data or code, poor adherence to standards, and sensitivity of ML training mean that many papers are not even reproducible in principle.<n>Experiments have found worryingly low degrees of similarity with original results.<n>Poor integrity threatens trust in and integrity of research results.
  arXiv  Detail & Related papers  (2024-06-20T13:56:42Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
  We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI)<n>We identify benefits, harms, and future research directions for the four main themes.<n>We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• Parsing Objects at a Finer Granularity: A Survey [54.72819146263311]
  Fine-grained visual parsing is important in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms. We conduct an in-depth study of the advanced work from a new perspective of learning the part relationship.
  arXiv  Detail & Related papers  (2022-12-28T04:20:10Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.