PROMISE'23
Conference Dec 8

CFP

PROMISE'23 CALL FOR PAPERS

https://www.facebook.com/promiseConf

https://twitter.com/promise_conf

PROMISE is an annual forum for researchers and practitioners to present, discuss and exchange ideas, results, expertise and experiences in construction and/or application of predictive models, artificial intelligence, and data analytics in software engineering. PROMISE encourages researchers to publicly share their data in order to provide interdisciplinary research between the software engineering and data mining communities, and seek for verifiable and repeatable experiments that are useful in practice.

Please see FSE 2023 website for venue, registration, and visa information

Keynote by Dr. Foutse Khomh, Polytechnique Montréal, Canada

Harnessing Predictive Modeling and Software Analytics in the Age of LLM-Powered Software Development

Abstract: In the rapidly evolving landscape of software development, Large Language Models (LLM) have emerged as powerful tools that can significantly impact the way software code is written, reviewed, and optimized, making them invaluable resources for programmers. They offer developers the ability to leverage pre-trained knowledge and tap into vast code repositories, enabling faster development cycles and reducing the time spent on repetitive or mundane coding tasks. However, while these models offer substantial benefits, their adoption also presents multiple challenges. For example, they might generate code snippets that are syntactically correct but functionally flawed, requiring human review and validation. Moreover, the ethical considerations surrounding these models, such as biases in the training data, should be carefully addressed to ensure fair and inclusive software development practices. This talk will provide an overview and reflection on some of these challenges, present some preliminary solutions, and discuss opportunities for predictive models and data analytics.
Biography: Foutse Khomh is a Full Professor of Software Engineering at Polytechnique Montréal, a Canada CIFAR AI Chair on Trustworthy Machine Learning Software Systems, and an FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications. He received a Ph.D. in Software Engineering from the University of Montreal in 2011, with the Award of Excellence. He also received a CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize for 2019. His research interests include software maintenance and evolution, machine learning systems engineering, cloud engineering, and dependable and trustworthy ML/AI. His work has received four ten-year Most Influential Paper (MIP) Awards, and six Best/Distinguished Paper Awards. He also served on the steering committee of SANER (chair), MSR, PROMISE, ICPC (chair), and ICSME (vice-chair). He initiated and co-organized the Software Engineering for Machine Learning Applications (SEMLA) symposium and the RELENG (Release Engineering) workshop series. He is co-founder of the NSERC CREATE SE4AI: A Training Program on the Development, Deployment, and Servicing of Artificial Intelligence-based Software Systems and one of the Principal Investigators of the DEpendable Explainable Learning (DEEL) project. He is also a co-founder of Quebec's initiative on Trustworthy AI (Confiance IA Quebec). He is on the editorial board of multiple international software engineering journals (e.g., IEEE Software, EMSE, JSEP) and is a Senior Member of IEEE.

Accepted Papers

  • Ayberk Yaşa, Ege Ergül, Eray Tuzun, Hakan Erdogmus
    Do Developers Fix Continuous Integration Smells?
  • Umutcan Karakas, Ayse Tosun
    Automated Fairness Testing with Representative Sampling
  • David Reid, Kristiina Rahkema, James Walden
    Large Scale Study of Orphan Vulnerabilities in the Software Supply Chain
  • Sivajeet Chand, Sushant Kumar Pandey, Jennifer Horkoff, Miroslaw Staron, Miroslaw Ochodek, Darko Durisic
    Comparing Word-based and AST-based Models for Design Pattern Recognition
  • Norbert Tihanyi, Tamas Bisztray, Ridhi Jain, Mohamed Amine Ferrag, Lucas C. Cordeiro, Vasileios Mavroeidis
    The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification
  • Pragya Bhandari, Gema Rodríguez-Pérez
    BuggIn: Automatic Intrinsic Bugs Classification Model using NLP and ML
  • Tim Menzies
    Model Review: A PROMISEing Opportunity
  • Sousuke Amasaki
    On Effectiveness of Further Pre-training on BERT models for Story Point Estimation

Topics of Interest

PROMISE papers can explore any of the following topics (or more).

Application-oriented papers:

  • prediction of cost, effort, quality, defects, business value;
  • quantification and prediction of other intermediate or final properties of interest in software development regarding people, process or product aspects;
  • using predictive models and data analytics in different settings, e.g. lean/agile, waterfall, distributed, community-based software development;
  • dealing with changing environments in software engineering tasks;
  • dealing with multiple-objectives in software engineering tasks;
  • using predictive models and software data analytics in policy and decision-making.

Ethically-aligned papers:

  • Can we apply and adjust our AI-for-SE tools (including predictive models) to handle ethical non-functional requirements such as inclusiveness, transparency, oversight and accountability, privacy, security, reliability, safety, diversity and fairness?

Theory-oriented papers:

  • model construction, evaluation, sharing and reusability;
  • interdisciplinary and novel approaches to predictive modelling and data analytics that contribute to the theoretical body of knowledge in software engineering;
  • verifying/refuting/challenging previous theory and results;
  • combinations of predictive models and search-based software engineering;
  • the effectiveness of human experts vs. automated models in predictions.

Data-oriented papers:

  • data quality, sharing, and privacy;
  • curated data sets made available for the community to use;
  • ethical issues related to data collection and sharing;
  • metrics;
  • tools and frameworks to support researchers and practitioners to collect data and construct models to share/repeat experiments and results.

Validity-oriented papers:

  • replication and repeatability of previous work using predictive modelling and data analytics in software engineering;
  • assessment of measurement metrics for reporting the performance of predictive models;
  • evaluation of predictive models with industrial collaborators.

 

Important Dates

  • Abstracts due: June 30th, 2023
  • Submissions due: July 7th, 2023
  • Author notification: July 28th, 2023
  • Camera ready: August 24th, 2023
  • Conference Date: December 8th, 2022

 

Journal Special Section

Following the conference, the authors of the best papers will be invited to submit extended versions of their papers for consideration in a special section in the journal Empirical Software Engineering (EMSE).

 

Call for papers

Technical papers: (10 pages) PROMISE accepts a wide range of papers where AI tools have been applied to SE such as predictive modeling and other AI methods. Both positive and negative results are welcome, though negative results should still be based on rigorous research and provide details on lessons learned.

Industrial papers: (2-4 pages) Results, challenges, lessons learned from industrial applications of software analytics.

New idea papers: (2-4 pages) Novel insights or ideas that may yet to be fully tested.

Journal First: (*new this year*) Selected papers will be invited for journal first presentations at PROMISE. Details to follow.

Publication and Attendance

Accepted papers will be published in the ACM Digital Library within its International Conference Proceedings Series and will be available electronically via ACM Digital Library.

Each accepted paper needs to have one registration at the full conference rate and be presented in person at the conference.

Green Open Access

Similar to other leading SE conferences, PROMISE supports and encourages Green Open Access, i.e., self-archiving. Authors can archive their papers on their personal home page, an institutional repository of their employer, or at an e-print server such as arXiv (preferred). Also, given that PROMISE papers heavily rely on software data, we would like to draw authors that leverage data scraped from GitHub of GitHub's Terms of Service, which require that "publications resulting from that research are open access".

We also strongly encourage authors to submit their tools and data to Zenodo, which adheres to FAIR (findable, accessible, interoperable and re-usable) principles and provides DOI versioning.

 

Submissions

PROMISE 2023 submissions must meet the following criteria:
  • be original work, not published or under review elsewhere while being considered;
  • conform to the ACM SIG proceedings template;
  • not exceed 10 (4) pages for technical (industrial, new-ideas) papers including references;
  • be written in English;
  • be prepared for double blind review
    • Exception: for data-oriented papers, authors may elect not to use double blind by placing a footnote on page 1 saying "Offered for single-blind review".
  • be submitted via HotCRP;
  • on submission, please choose the paper category appropriately, i.e., technical (main track, 10 pages max); industrial (4 pages max); and new idea papers (4 pages max).
To satisfy the double blind requirement submissions must meet the following criteria:
  • no author names and affiliations in the body and metadata of the submitted paper;
  • self-citations are written in the third person;
  • no references to the authors personal, lab, or university website;
  • no references to personal accounts on GitHub, bitbucket, Google Drive, etc.
Submissions will be peer reviewed by at least three experts from the international program committee. Submissions will be evaluated on the basis of their originality, importance of contribution, soundness, evaluation, quality, and consistency of presentation, and appropriate comparison to related work.

Programme Committee

  • Hirohisa Aman, Ehime University
  • Gábor Antal, University of Szeged
  • Tapajit Dey, Carnegie Mellon University - Software Engineering Institute
  • Tracy Hall, Lancaster University
  • Maxime Lamothe, Polytechnique Montréal
  • Yepang Liu, Southern University of Science and Technology
  • Leandro Minku, University of Birmingham, UK
  • Csaba Nagy, Software Institute - USI, Lugano
  • Luca Pascarella, ETH Zurich
  • Fabiano Pecorelli, Jheronimus Academy of Data Science & Eindhoven University of Technology, Netherlands
  • Gregorio Robles, Universidad Rey Juan Carlos
  • Miroslaw Staron, Chalmers | University of Gothenburg
  • Yiming Tang, Concordia University
  • Alexander Trautsch, University of Passau
  • Lili Wei, McGill
  • Xiaoyuan Xie, Wuhan University

Steering Committee

General Chair

PC Co-Chairs

Publicity Co-Chairs