========================== First Call for Papers ==========================
The 9th International Conference on Predictive Models in Software
Oct 9, 2013, Baltimore, Maryland, USA
(Co-located with ESEM 2013)
Abstracts due: April 05, 2013
Submissions due: April 12, 2013
Author notification: June 10, 2013
Camera-ready copy due: June 28, 2013
Tom Zimmermann, Microsoft Research
PROMISE conference is an annual forum for researchers and practitioners to
present, discuss and exchange ideas, results, expertise and experiences in
construction and/or application of prediction models in software
engineering. Such models could be targeted at: planning, design,
implementation, testing, maintenance, quality assurance, evaluation, process
improvement, management, decision making, and risk assessment in software
and systems development.
PROMISE is distinguished from similar forums with its public data repository
and focus on methodological details, providing a unique interdisciplinary
venue for software engineering and machine learning communities, and seeking
for verifiable and repeatable prediction models that are useful in practice.
The special theme of PROMISE’13 is predictions across projects, contexts and
organizations, where the predictions employ approaches (e.g. transfer
learning, instance selection, data filtering) with an impact that is useful
in practice, in order to solve the problem of learning under concept drift
(across time and space).
TOPICS OF INTEREST:
* (Application oriented): Predicting for 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 in policy and decision making;
using predictive models in different settings, e.g. lean/agile, waterfall,
distributed, community-based software development.
* (Theory oriented): Interdisciplinary and novel approaches to predictive
modeling that contribute to the theoretical body of knowledge in software
engineering; verifying/refuting/challenging previous theory and results; the
effectiveness of human experts vs. automated models in predictions.
* (Data and model oriented): Data quality, sharing, and privacy; ethical
issues related to data collection; metrics; contributions to the repository;
model construction, evaluation, sharing and reusability; tools and
frameworks to support researchers and practitioners to collect data and
construct models to share/repeat experiments and results.
KINDS OF PAPERS:
We invite all kinds of empirical studies on the topics of interest (e.g.
case studies, meta-analysis, replications, experiments, simulations, surveys
etc.), as well as industrial experience reports detailing the application of
prediction technologies and their effectiveness in industrial settings. Both
positive and negative results are welcome, though negative results should
still be based on rigorous research and provide details on lessons learned.
Following the tradition, PROMISE'13 will give the highest priority to
empirical studies based on publicly available datasets. It is therefore
encouraged, but it is not mandatory, that conference attendees contribute
the data used in their analysis to the on-line PROMISE data repository.
We solicit both full and short papers. Short papers are intended to
disseminate new ideas, on-going work and preliminary results for early
feedback, and do not necessarily require complete results as in full papers.
The deadline for short papers is the same as full papers.
* Submissions must be original work, not published or under review
* Submissions must conform to the ACM SIG proceedings templates from
* Submissions must not exceed 10 (4) pages for full (short) papers including
* Papers should be submitted via Easychair (please choose either “full” or
“short” papers): http://www.easychair.org/conferences/?conf=promise2013.
* Accepted papers will be published in the ACM digital library.
The venue for the special issue is TBA. Previous PROMISE special issues have
appeared in IEEE Software, Empirical Software Engineering Journal,
Information and Software Technology Journal, and Automated Software
Stefan Wagner, University of Stuttgart (General Chair)
Burak Turhan, University of Oulu (PC Chair)
Ye Yang, Chinese Academy of Science (Publicity Chair)
Ayse Bener, Ryerson University (Local Org. Chair)
Lefteris Angelis, Aristotle University of Thessaloniki
Ayse Bener, Ryerson University
Christian Bird, Microsoft Research
David Bowes, University of Hertfordshire
Daniela Da Cruz, University of Minho
Massimiliano Di Penta, RCOST - University of Sannio
Harald Gall, University of Zurich
Vahid Garousi, University of Calgary
Tracy Hall, Brunel University
Mark Harman, University College London
Rachel Harrison, University of Oxford
Jacky Keung, The Hong Kong Polytechnic University
Sunghun Kim, The Univ. of Hong Kong Science and Tech.
Ekrem Kocaguneli, West Virginia University
Lech Madeyski, Wroclaw University of Technology
Kenichi Matsumoto, Nara Inst. of Science and Tech.
Emilia Mendes, Blekinge Institute of Technology
Tim Menzies, West Virginia University
Leandro Minku, The University of Birmingham
Thomas Ostrand, AT&T Labs - Research
Daryl Posnett, UC Davis
Rudolf Ramler, Software Competence Center Hagenberg GmbH
Daniel Rodriguez, The University of Alcalá
Guenther Ruhe, University of Calgary
Federica Sarro, University College London
Carolyn Seaman, UMBC
Martin Shepperd, Brunel University
Qinbao Song, Xi'an Jiaotong University
Ayse Tosun Misirli, University of Oulu
Burak Turhan, University of Oulu
Stefan Wagner, University of Stuttgart
Dietmar Winkler, Vienna University of Technology
Ye Yang, Chinese Academy of Sciences
Hongyu Zhang, Tsinghua University
Yuming Zhou, Nanjing University
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