... interesting specialized event co-located with FSE; the CFP explcitly
welcomes negative results, if we sth in our fundus ;-)
Best,
Timo
-------- Weitergeleitete Nachricht --------
Betreff: [SEWORLD] CfP: PROMISE 2025 - 21st International Conference on
Predictive Models and Data Analytics in Software Engineering
Weitersenden-Datum: Mon, 13 Jan 2025 03:53:20 +0000
Weitersenden-Von: SEWORLD Moderator <seworld-moderator(a)sigsoft.org>
Weitersenden-An: seworld(a)sigsoft.org
Datum: Fri, 10 Jan 2025 22:50:25 +0000
Von: Yiming Tang <seworld(a)volunteer.acm.org>
An: SEWORLD(a)SIGSOFT.ORG <SEWORLD(a)SIGSOFT.ORG>
Kopie (CC): wshang(a)uwaterloo.ca <wshang(a)uwaterloo.ca>ca>, Lili Wei, Prof.
<lili.wei(a)mcgill.ca>
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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
===================================================================
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-s…
* 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.
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