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 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 2022 website for venue, registration, and visa information
Topics of Interest
PROMISE papers can explore any of the following topics (or more).
- 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.
- 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?
- 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 quality, sharing, and privacy;
- curated data sets made available for the community to use;
- ethical issues related to data collection and sharing;
- tools and frameworks to support researchers and practitioners to collect data and construct models to share/repeat experiments and results.
- 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.
- Abstracts due: June 27th, 2022
- Submissions due: July 1st, 2022
- Author notification: July 29th, 2022
- Camera ready: August 12th, 2022
- Conference Date: TBD
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).
EMSE encourages open science and reproducible research for this special issue. Please see our Open Science Initiative for further information.
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.
Tutorials/Technical Briefing: (2+1 pages) (*new this year*) Tutorials and short technical briefings on trending topics related to software engineering (duration: 60/90/120 minutes). The proposal should be no longer than 2 pages plus one page for brief speaker information and biographies. The tutorial Call for Papers and submission guidelines can be can be found here.
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.
SubmissionsPROMISE 2022 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).
- 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.
- Hirohisa Aman, Ehime University
- Sousuke Amasaki, Okayama Prefectural University
- Gemma Catolino, Tilburg University - Jheronimus Academy of Data Science
- Jinfu Chen, Huawei Technologies Canada
- Zadia Codabux, University of Saskatchewan
- Eleni Constantinou, Eindhoven University of Technology
- Carmine Gravino, University of Salerno
- Tracy Hall, Lancaster University
- Steffen Herbold, TU Clausthal, Germany
- Yasutaka Kamei, Kyushu University
- Maxime Lamothe, Polytechnique Montreal
- Gregorio Robles, Universidad Rey Juan Carlos
- Mohammed Sayagh, ETS - Quebec University
- Martin Shepperd, Gothenburg University/Brunel University
- Yiming Tang, Concordia University
- Melina Vidoni, ustralian National University, CECS School of Computing
- Zhiyuan Wan, Zhejiang University
- Lili Wei, The Hong Kong University of Science and Technology
- Xiaoyuan Xie, Wuhan University
- Ahmed Zerouali, Vrije Universiteit Brussels
- Hongyu Zhang, The University of Newcastle
- Foutse Khomh, Ecole Polytechnique de Montreal
- Ayse Tosun, Istanbul Technical University
- David Bowes, University of Central Lancashire
- Giuseppe Destefanis, Brunel University
- Tim Menzies, North Carolina State University
- Meiyappan Nagappan, University of Wateroo
- Jean Petric, Lancaster U.
- Shane McIntosh, University of Waterloo