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21st International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2025)

https://conf.researchr.org/home/promise-2025

June, 2025, Trondheim, Norway

Co-located with the International Conference on the Foundations of Software Engineering (FSE 2025)

Submit your papers by February 25th, 2025
https://conf.researchr.org/home/promise-2025#Call-for-Papers
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The International Conference on Predictive Models and Data Analytics in Software Engineering (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.

=== Important Dates ===

- Abstract submission: Feb 18th, 2025 AoE
- Paper submission: Feb 25th, 2025 AoE
- Author notification: Mar 24th, 2025 AoE
- Camera-ready: Apr 24th, 2025 AoE
- Conference Date: June, 2025

=== Types of Submissions ===

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

* Selected papers will be invited for journal first presentations at PROMISE. Details to follow.

=== 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.

=== Submissions ===

PROMISE 2025 submissions must meet the following criteria:

* be original work, not published or under review elsewhere while being considered;
* conform to the submission format requirements of the [FSE 2025 Companion proceedings](https://conf.researchr.org/track/fse-2025/fse-2025-how-to-submit#submission-format)
* 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 HotPRC and the submission link will be posted shortly.
* 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.

=== 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.

=== Evaluation ===

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.

=== 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.