🚨 6 days left! Submit your Initial Report to #ICSME2025 Registered Reports Track!
Why submit?
✅ Rigorous review of your methodology
✅ Commit to publish regardless of results
✅ Perfect for exploratory or confirmatory studies
✅ Improve study quality through early feedback
📍 More details on submission: https://conf.researchr.org/track/icsme-2025/icsme-2025-registered-reports
Don’t miss this chance to strengthen your research and contribute to more robust science!
#ICSME2025 #EmpiricalSE #RegisteredReports #SoftwareEngineeringResearch
ICSME 2025 - Registered Reports - ICSME 2025 - International Conference on Software Maintenance and Evolution
Call for Registrations
Empirical Software Engineering Journal (EMSE), in conjunction with the International Conference on Software Maintenance and Evolution (ICSME), is continuing the Registered Reports (RR) track.
The RR track of ICSME 2025 has two goals: (1) to prevent HARKing (hypothesizing after the results are known) for empirical studies and (2) to provide early feedback to authors in their initial study design. For papers submitted to the RR track, methods and proposed analyses are reviewed prior to execution. Pre-registered studies follow a two-step process:
Stage 1: A repor ...
📢 Call for Registered Reports
Are you tackling bold empirical research questions in software maintenance and evolution? Submit a Registered Report to ICSME 2025 and get feedback before you collect or analyze data.
✅ Rigorous review of your methodology
✅ Commit to publish regardless of results
📅 Initial Report Deadline: May 20, 2025
🔗For more details: https://conf.researchr.org/track/icsme-2025/icsme-2025-registered-reports
Let’s raise the bar for reproducible and impactful research! #ICSME2025 #RegisteredReports #EmpiricalSE
ICSME 2025 - Registered Reports - ICSME 2025 - International Conference on Software Maintenance and Evolution
Call for Registrations
Empirical Software Engineering Journal (EMSE), in conjunction with the International Conference on Software Maintenance and Evolution (ICSME), is continuing the Registered Reports (RR) track.
The RR track of ICSME 2025 has two goals: (1) to prevent HARKing (hypothesizing after the results are known) for empirical studies and (2) to provide early feedback to authors in their initial study design. For papers submitted to the RR track, methods and proposed analyses are reviewed prior to execution. Pre-registered studies follow a two-step process:
Stage 1: A repor ...
🚀 Starting the new year on a high note! 🎉 Our paper "Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study" has been accepted at #ICSE-SEIS2025!
We analyze 168 ML projects to uncover how developers adopt green architectural tactics for sustainable software. Huge thanks to @silveriomf & Fabio Palomba!
📄 Pre-Print here: https://arxiv.org/abs/2410.06708
#GreenAI #SoftwareEngineering #EmpiricalSE
Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study
As machine learning (ML) and artificial intelligence (AI) technologies become more widespread, concerns about their environmental impact are increasing due to the resource-intensive nature of training and inference processes. Green AI advocates for reducing computational demands while still maintaining accuracy. Although various strategies for creating sustainable ML systems have been identified, their real-world implementation is still underexplored. This paper addresses this gap by studying 168 open-source ML projects on GitHub. It employs a novel large language model (LLM)-based mining mechanism to identify and analyze green strategies. The findings reveal the adoption of established tactics that offer significant environmental benefits. This provides practical insights for developers and paves the way for future automation of sustainable practices in ML systems.
🚀 Thrilled to announce our paper "A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering" has been accepted at
#WSESE2025! 🎉
We introduce the PRIMES Framework to enhance dataset quality & reproducibility using LLMs. Big thanks to Joel Castaño, Fabio Palomba, Xavier Franch &
@silveriomf #SoftwareEngineering #LLMs #EmpiricalSE #PromptEngineering📄 For more details, you can read the full article here!
https://arxiv.org/abs/2411.09974
A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering
Context: The emergence of Large Language Models (LLMs) has significantly transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories. Objectives: Our objective is to establish a practical framework for future SE researchers needing to enhance the data collection and dataset while conducting software repository mining studies using LLMs. Method: This experience report shares insights from two previous repository mining studies, focusing on the methodologies used for creating, refining, and validating prompts that enhance the output of LLMs, particularly in the context of data collection in empirical studies. Results: Our research packages a framework, coined Prompt Refinement and Insights for Mining Empirical Software repositories (PRIMES), consisting of a checklist that can improve LLM usage performance, enhance output quality, and minimize errors through iterative processes and comparisons among different LLMs. We also emphasize the significance of reproducibility by implementing mechanisms for tracking model results. Conclusion: Our findings indicate that standardizing prompt engineering and using PRIMES can enhance the reliability and reproducibility of studies utilizing LLMs. Ultimately, this work calls for further research to address challenges like hallucinations, model biases, and cost-effectiveness in integrating LLMs into workflows.
Making research data (originally from a worksheet) available as a PDF should be punished with life-long ban from conference and journal submission!
#openscience #empiricalSE
A re- #introduction in this new instance:
I am an assistant professor at the Department of Computer Science and Operations Research of the Université de Montréal https://michalis.famelis.info/
I work to create formal but practical techniques and methods for #softwareEngineering. I specialize on modelling and managing #uncertainty and #variability. I draw from #formalVerification, #MDE, and #empiricalSE.
I recently became very interested in #scientificModels as software artifacts.
ICSME
Vincenzo De Martino
Marco Torchiano
Michális Famelis/Φαμέλης