• Submission: July 3
  • Submission: July 10
  • Notification: July 24
  • Notification: July 26>
  • Camera ready: Aug 31
  • Meeting: Nov6


Tim Menzies : NcState, USA
Ye Yang : Stevens Institute, USA


Program Committee

Hoa Khanh Dam : U. Wollongong, Aust.
Gregory Gay : U. Sth Carolina, USA
Ho In : Korea, U., Korea
Jacky Keung : HK Poly U, HK
Sung Kim : HK Poly U, HK
Gunes Koru : UMBC, USA
Kenichi Matsumoto : NAIST,Japan
Ray Madachy : NPS, USA
Leandro Minku : U. Birmingham,UK
Guenther Ruhe : U.Calgary, Canada
Martin Shepperd : Brunel U. UK
Ricardo Valerdi : U.Arizona, USA
Jason He Zhang : Nanjing U. China
Liming Zhu : Nicta, Aust.

A repeated complaint is that industrial practitioners find it hard to apply the results generated from data science. Such actionable analytics are required to enable time-sensitive, environmental-aware decision making. How can we bridge the gap between the predictions we can generate to actions that users can apply?


Session 1

Morning break: 10:00 to 10:30

Session 2

Lunch: 12:30 to 1:30

Session 3

  • 1:30 Break out groups, work session
  • 2:30 Report back

Afternoon break: 3:30 to 4:00

Session 4

  • 4:00 To do lists, future plans
  • 4:30 close

Notes (from call for Papers)

The workshop goal is to:

  • Exchange research work on exploring new ideas, metrics, and algorithms in software prediction;
  • Discuss emergent challenges in software prediction;
  • Propose and ideally converge on a research road map for the next 5-10 year.

Accordingly, we ask for papers on related topics that include (but are not limited to) the following:

  • Experiences and lessons learned on the strengths and limitations of current software predictive models; (i.e. how reliable are existing methods?)
  • Challenges and barriers to adopt current models and methodologies in the context of new software technologies, such as crowdsourcing, architecture migration, cloud service composition, etc.
  • Roles of automation in improving predictive power in software estimation.
  • New metrics and models to better measure, search and recommend the underlying causal relationships of cost, schedule, and quality, etc.
  • Trends and needs of emergent software planning practices and impact on software estimation;
  • Research agenda for maturing and enriching software planning decision models.


All accepted papers will appear in the IEEE Digital Library.

Selected papers will be invited to submit to IEEE Software.


Full papers: max 10 pages (+2 pages refs).
Vision statements: max 4 pages (including refs)

Submit at goo.gl/bEuolb, PDF format= goo.gl/XxBwt.

Authors should use US letter style. LaTeX users should use this their document class:

% The compsoc option is not to be used.