Submissions 20 Jun

CFP (pdf)

The 12th International Conference on Predictive Models and Data Analytics in Software Engineering

September 7, 2016, Ciudad Real, Spain


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 is distinguished from similar forums with its public data repository and focus on methodological details, providing a unique interdisciplinary venue for software engineering and data mining communities, and seeking for verifiable and repeatable experiments that are useful in practice.

Keynote: "Use and Misuse of the term experiment in the software repositories research"

by Prof. Natalia Juristo, Technical University of Madrid (UPM), Spain

Abstract: Today empiricism is everywhere in SE research. But this does not imply that SE is empirically mature. Conducting empirical studies does not mean they are carried out and used properly. In this talk I focus on a methodological issue regarding research on mining software repositories (MSR). MSR is an extremely active area of research these days, but a young one that I believe still lacks rigor. I have observed that the term experiment is misused very often in MSR works. We have conducted a small-scale literature review to understand the level of misuse and it is broad. The results of such review are shown in the talk. I will discuss about the essential features that make an experiment an experiment and allows discovering causality. Most MSR works lack the manipulation required to an empirical study to be an experiment. To me most MSR studies are observational studies. (Although there are some type of experiments that can be conducted with repositories). To get reliable results it is critical that the researchers understand the type of study they are conducting as well as the type of evidence that every type of study generates. I see MSR research as epidemiologic research in medicine. If properly conducted, epidemiologic studies can catch a glimpse of causality. Epidemiology has developed types of empirical studies that make evidence stronger (as control-case studies or cohort studies). MSR could learn from them and apply strategies, as random selection of data from the repository, that makes decrease bias in results.

Bio: Dr. Natalia Juristo is full professor of software engineering with the Computing School at the Technical University of Madrid (UPM) since 1997 and holds a FiDiPro (Finland Distinguish Professor) research grant since 2013. She was the Director of the UPM MSc in Software Engineering from 1992 to 2002 and the coordinator of the Erasmus Mundus European Master on SE (whith the participation of the University of Bolzano, the University of Kaiserslautern and the University of Blekinge) from 2007 to 2012. Natalia has served in several Program Committees ICSE, RE, REFSQ, ESEM, ISESE and others. She has been Program Chair EASE13, ISESE04 and SEKE97 and General Chair for ESEM07, SNPD02 and SEKE01. She has been member of several Editorial Boards, including Transactions on SE, Journal of Empirial Software Engineering and Software magazine. Dr. Juristo has been Guest Editor of special issues in several journals, including Journal of Empirical Software Engineering, IEEE Software, Journal of Software and Systems, Data and Knowledge Engineering and the International Journal of Software Engineering and Knowledge Engineering.


Keynote: Natalia Juristo: "Use and Misuse of the term experiment in the software repositories research"
Jil Klünder, Oliver Karras, Fabian Kortum and Kurt Schneider. Forecasting Communication Behavior in Student Software Projects
Morning Break
Simone Porru, Alessandro Murgia, Serge Demeyer, Michele Marchesi and Roberto Tonelli. Estimating Story Points from Issue Reports
Seyedrebvar Hosseini, Burak Turhan and Mika Mäntylä. Search Based Training Data Selection For Cross Project Defect Prediction
Leandro Minku. On the Terms Within- and Cross-Company in Software Effort Estimation
Lunch Break
Qing Mi, Jacky Keung and Yang Yu. Measuring the Stylistic Inconsistency in Software Projects using Hierarchical Agglomerative Clustering
Luigi Lavazza and Sandro Morasca. An Empirical Evaluation of Distribution-based Thresholds for Internal Software Measures
Gernot Liebchen and Martin Shepperd. Data Sets and Data Quality in Software Engineering: Eight Years On
Verena Honsel, Steffen Herbold and Jens Grabowski. Hidden Markov Models for the Prediction of Developer Involvement Dynamics and Workload
Afternoon Break
Hudson Borges, Andre Hora and Marco Tulio Valente. Predicting the Popularity of GitHub Repositories
István Kádár, Péter Hegedűs, Rudolf Ferenc and Tibor Gyimóthy. A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability
Closing Discussion and Q&A

Topics of Interest

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:

  • contributions to the repository;
  • 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.


Keynote Speaker


Important Dates

  • Abstract submission: 10 17 June 2016
  • Paper submission: 17 20 June 2016
  • Notification of acceptance: 10 July 2016
  • Camera-ready papers: 27 July 2016
  • Conference date: 07 September 2016


Journal Special Section

  • Following the conference, the authors of two of the best  manuscripts will be invited to extend their papers into full journal papers, for a Special Section of the Information and Software Technology journal.

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.

Following the tradition, PROMISE 2016 will give the highest priority to studies based on publicly available datasets. It is therefore encouraged, but not mandatory, that conference attendees contribute the data used in their analysis to the on-line PROMISE data repository ( We also encourage authors to submit their source codes to the repository.

Submissions can be of the following kinds:

  • Full papers (oral presentation, 10 pages): papers with novel and complete results.
  • Short papers (oral presentation, 4 pages): 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.
  • Short papers (poster competition, 4 pages): papers to disseminate on-going work and preliminary results for early feedback.

Poster Competition

We will also have a poster competition. The poster is not mandatory, but is strongly encouraged. Posters will be judged by a panel during the conference.



PROMISE 2016 submissions must meet the following criteria:
  • be original work, not published or under review elsewhere.
  • conform to the ACM SIG proceedings templates from
  • not exceed 10 (4) pages for full (short) papers including references.
  • Papers should be submitted via EasyChair (please choose the paper category appropriately):
  • Accepted papers will be published in the ACM digital library.

Programme Committee

  • Lefteris Angelis, Aristotle University of Thessaloniki, Greece
  • Gabriele Bavota, Free University of Bozen-Bolzano, Italy
  • Bora Caglayan, Ryerson University, Canada
  • Tracy Hall, Brunel University, UK
  • Rachel Harrison, Oxford Brookes University, UK
  • Jacky Keung, City University of Hong Kong, China
  • Foutse Khomh, DGIGL École Polytechnique de Montréal, Canada
  • Ekrem Kocaguneli, Microsoft, USA
  • Chris Lokan, University of New South Wales, Australia
  • Lech Madeyski, Wroclaw University of Technology, Poland
  • Emilia Mendes, BTH, Sweden & University of Oulu, Finland
  • Tim Menzies, North Carolina State University, USA
  • Leandro Minku, University of Leicester, UK
  • Massimiliano Di Penta, University of Sannio, Italy
  • Rudolf Ramler, Software Competence Center Hagenberg, Austria
  • Daniel Rodriguez, The University of Alcalá, Spain
  • Federica Sarro, University College London, UK
  • Martin Shepperd, Brunel University, UK
  • Ayse Tosun Misirli, Istanbul Technical University, Turkey
  • Burak Turhan, University of Oulu, Finland
  • Stefan Wagner, University of Stuttgart, Germany
  • Hironori Washizaki, Waseda University, Japan
  • Dietmar Winkler, Vienna University of Technology, Austria
  • Yang Ye, Stevens Institute of Technology, USA
  • Yuming Zhou, Nanjing University, China

Steering Committee

General Chair

PC Co-Chairs

Publication Chair

Publicity Chair

Local Organization Co-Chairs

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