PROMISE 2017
The 13th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE'17)
Nov. 8, 2017. Toronto, Canada.
http://promisedata.org/2017
Twitter: @promise_conf
Facebook: https://www.facebook.com/promiseConf
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Important dates:
Abstracts: June 6, 2017
Paper submission: June 12, 2017
Notification date: July 21, 2017
Conference date: November 8, 2017
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Call for Submissions to PROMISE 2017
(Co-located with ESEM 2017)
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. Such models and analyses 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 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.
Topics of interest include, but are not limited to:
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 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.
Theory oriented:
-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:
-data quality, sharing, and privacy;
-ethical issues related to data collection;
-metrics;
-tools and frameworks to support researchers and practitioners to collect data and construct models to share/repeat experiments and results.
Validity oriented:
-replication and repeatability of previous work using predictive modelling in software engineering;
-assessment of measurement metrics for reporting the performance of predictive models;
-evaluation of predictive models with industrial collaborators;
*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 predictive modelling and data analytics 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. It is encouraged, but not mandatory, that conference attendees contribute the data used in their analysis on-line. Submissions can be of the following kinds:
-Full papers (oral presentation): papers with novel and complete results.
-Short papers (oral presentation): papers to disseminate on-going work and preliminary results for early feedback, or vision papers about the future of predictive modelling and data analytics in software engineering.
*Submissions
PROMISE 2017 submissions must meet the following criteria:
-be original work, not published or under review elsewhere;
-conform to the ACM SIG proceedings templates from https://www.acm.org/publications/proceedings-template
-not exceed 10 (4) pages for full (short) papers including references;
-follow a structured abstract with the headings: Background, Aims, Method, Results, and Conclusions.
-written in English;
-papers should be submitted via EasyChair (please choose the paper category appropriately): https://easychair.org/conferences/?conf=promise2017
Submissions will be peer reviewed by at least three experts from the international program committee. 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.
*Journal Special Section
Following the conference, the authors of the best papers will be invited for consideration in a special issue of the Empirical Software Engineering (EMSE) journal by Springer.
Note about GitHub research. 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". 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).
——————PROMISE General Chair——————
Burak Turhan, University of Oulu
——————PROMISE Program Co-Chairs——————
David Bowes, University of Hertfordshire
Emad Shihab, Concordia University
——————Publications Chair——————
David Bowes, University of Hertfordshire
——————Publicity and Social Media Chair——————
Federica Sarro, University College London
——————Local Organization Chair——————
Andriy Miranskyy, Ryerson University
——————PROMISE Steering Committee——————
Leandro Minku, University of Leicester
Andriy Miranskyy, Ryerson University
Massimiliano Di Penta, University of Sannio
Burak Turhan, University of Oulu
Hongyu Zhang, University of Newcastle
--
Dr. Federica Sarro
Senior Research Associate
CREST, Department of Computer Science
University College London
Malet Place, London, WC1E 6BT, UK
http://www0.cs.ucl.ac.uk/staff/F.Sarro/
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