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 the FSE 2023 website for venue, registration, and visa information.

The website for PROMISE 23 was previously hosted here.

Plenary
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Fri 8 Dec

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09:00 - 10:30
Opening and KeynotePROMISE 2023 at Foothill G
Chair(s): Steffen Herbold University of Passau
09:00
30m
Day opening
Opening Session and Best Paper Award
PROMISE 2023
Steffen Herbold University of Passau
09:30
60m
Keynote
Harnessing Predictive Modeling and Software Analytics in the Age of LLM-Powered Software Development
PROMISE 2023
K: Foutse Khomh Polytechnique Montréal
DOI
10:30 - 11:00
11:00 - 12:30
Bugs, smells, and vulnerabilitiesPROMISE 2023 at Foothill G
Chair(s): Miroslaw Staron University of Gothenburg
11:00
30m
Paper
BuggIn: Automatic Intrinsic Bugs Classification Model using NLP and ML
PROMISE 2023
Pragya Bhandari University of British Columbia, Gema Rodríguez-Pérez University of British Columbia (UBC)
DOI
11:30
30m
Paper
Do Developers Fix Continuous Integration Smells?
PROMISE 2023
Ayberk Yaşa Bilkent University, Ege Ergül Bilkent University, Eray Tüzün Bilkent University, Hakan Erdogmus Carnegie Mellon University
DOI
12:00
30m
Paper
Large Scale Study of Orphan Vulnerabilities in the Software Supply Chain
PROMISE 2023
David Reid University of Tennessee, Kristiina Rahkema University of Tartu, James Walden Northern Kentucky University
DOI
14:00 - 15:30
Language ModelsPROMISE 2023 at Foothill G
Chair(s): Csaba Nagy Software Institute - USI, Lugano
14:00
30m
Paper
The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification
PROMISE 2023
Norbert Tihanyi Technology Innovation Institute, Tamas Bisztray University of Oslo, Ridhi Jain Technology Innovation Institute (TII), Abu Dhabi, UAE, Mohamed Amine Ferrag Technology Innovation Institute, Lucas C. Cordeiro The University of Manchester, UK, Vasileios Mavroeidis University of Oslo
DOI
14:30
30m
Paper
Comparing Word-based and AST-based Models for Design Pattern Recognition
PROMISE 2023
Sivajeet Chand Dept. of CSE Chalmers | University of Gothenburg, Sweden, Sushant Kumar Pandey Chalmers and University of Gothenburg, Jennifer Horkoff Chalmers and the University of Gothenburg, Miroslaw Staron University of Gothenburg, Miroslaw Ochodek Poznan University of Technology, Darko Durisic R&D, Volvo Cars, Gothenburg, Sweden
DOI
15:00
30m
Paper
On Effectiveness of Further Pre-training on BERT models for Story Point Estimation
PROMISE 2023
Sousuke Amasaki Okayama Prefectural University
DOI
15:30 - 16:00
16:00 - 17:00
Fairness and PrivacyPROMISE 2023 at Foothill G
16:00
30m
Paper
Automated Fairness Testing with Representative Sampling
PROMISE 2023
Umutcan Karakaş Istanbul Technical University, Ayse Tosun Istanbul Technical University
DOI
16:30
30m
Paper
Model Review: A PROMISEing Opportunity
PROMISE 2023
Tim Menzies North Carolina State University
DOI Pre-print
17:00 - 17:10
17:00
10m
Day closing
Closing Session
PROMISE 2023
Steffen Herbold University of Passau

Not scheduled yet

Not scheduled yet
Coffee break
Coffee break 1
PROMISE 2023

Not scheduled yet
Coffee break
Coffee break 2
PROMISE 2023

Not scheduled yet
Lunch
Lunch
PROMISE 2023

Call for Papers

The International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE) welcomes four 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 (new this year)

  • 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 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.

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.

Important Dates

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

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.

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).

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.