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RE: CFP 2007 Third International Predictor Models in Software Engineering (PROMISE) Workshop
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Date: -- (:)
From: Gary D. Boetticher <gboetticher@h...>
Subject: RE: CFP 2007 Third International Predictor Models in Software Engineering (PROMISE) Workshop

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<DIV><FONT face="Courier New" size=2>Hi ,</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New" size=2>I would like to invite you to consider 
submitting a paper to the 2007 PROMISE workshop which will be held in conjunction 
with ICSE. <BR><BR> The CFP is given below. </FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New" size=2>Thanks!</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New" size=2>Gary</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV><FONT face=Arial size=2>
<DIV><BR><FONT face="Courier New">Call For Papers (CFP) (ICSE related workshop): 
Third International Workshop - <BR>PredictOr Models In Software Engineering 
(PROMISE)</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><BR><FONT face="Courier New">Third International Workshop on Predictor 
Models in Software 
Engineering<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
&nbsp;&nbsp;&nbsp;&nbsp; 
(PROMISE 
2007)<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
</FONT><A href="http://promisedata.org/2007/CFP.html" 
target=_blank><FONT 
face="Courier New">http://promisedata.org/2007/CFP.html</FONT></A><BR><FONT 
face="Courier New">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
&nbsp; 
Sunday May 20, 
2007<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
Minneapolis, Minnesota&nbsp;&nbsp; USA</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; In conjunction with 
29th Int. Conf. on Software 
Engineering<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
</FONT><A href="http://web4.cs.ucl.ac.uk/icse07/" 
target=_blank><FONT 
face="Courier New">http://web4.cs.ucl.ac.uk/icse07/</FONT></A></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Objectives<BR>----------</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">As in any engineering field, realistic prior 
assessment<BR>of the potential cost, problems, timing, performance,<BR>safety, 
security, and numerous other properties of software<BR>projects is essential for 
effective and efficient planning,<BR>design, and implementation of those 
projects.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">A mature engineering discipline needs to have a 
standard<BR>set of predictive methods that practitioners can use, as<BR>well as 
standards for interpreting the results of those<BR>methods. To become widely 
accepted and used in the field,<BR>models need to be validated on data from a 
wide range<BR>of applications, in different development environments,<BR>and 
with different reliability and performance goals.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">The PROMISE workshop aims to broaden knowledge 
of<BR>predictive models that have been successfully developed,<BR>to provide a 
forum for the discussion of new models,<BR>to provide a catalog of system data 
that researchers can<BR>use to evaluate proposed models so that practitioners 
can<BR>use these models to compare predicted results to their<BR>own 
projects.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">As a follow-up to last year's workshop, this 
workshop<BR>focuses upon &quot;issues and challenges surrounding 
building<BR>predictive software models.&quot; Predictor models already exist<BR>
for 
software development effort and fault injections as<BR>well as co-update or 
change predictors, software quality<BR>estimators and software escalation 
(&quot;escalation&quot; predictors<BR>try to guess what bug reports will require 
the 
attention of<BR>the senior experts). However, in most cases they have 
been<BR>presented in venues that cover a diverse set of interests.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><BR><FONT face="Courier New">Goals of the 
Workshop<BR>---------------------<BR>The goals of this one-day workshop 
are:</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* To expand the current public repository of data 
sets<BR>&nbsp; related to software engineering in order to conduct repeatable, 
<BR>&nbsp; refutable or improvable experiments.&nbsp; Such an 
empirical<BR>&nbsp; process is essential to the maturity of the field 
of<BR>&nbsp; predictive software models and software engineering<BR>&nbsp; in 
general. After only two years, the current PROMISE<BR>&nbsp; repository already 
contains 24 data sets.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* To deliver to the software engineering community 
useful<BR>&nbsp; and usable and verified models or methods:</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp; o &quot;Models&quot; predict software 
properties of 
interest to<BR>&nbsp;&nbsp;&nbsp; 21st century software practitioners. Numerous 
such <BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; models are already under 
development, including models <BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
that predict software quality, development effort, 
<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
requirements/design/code<BR>&nbsp;&nbsp;&nbsp; traceability etc.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp; o &quot;Methods&quot; are learning systems 
for 
building particular<BR>&nbsp;&nbsp;&nbsp; models for particular 
situations.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* To compile a list of open research questions 
that are<BR>&nbsp; deemed essential by the researchers in the 
field.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* To show, by example, to the next generation of 
software<BR>&nbsp; engineering researchers that empiricism is useful, <BR>&nbsp; 
practical, exciting, and insightful.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* To bring together researchers and practitioners 
with<BR>&nbsp; the aim of sharing experience and expertise.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* To steer discussion and debate on various 
aspects and<BR>&nbsp; issues related to building predictive software 
models.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><BR><FONT face="Courier New">Public Data 
Policy<BR>------------------</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">PROMISE 2007 gives the highest priority to case 
studies,<BR>experience reports, and presented results that are based<BR>on 
publically available datasets. To increase the chance<BR>of acceptance, authors 
are urged to submit papers that<BR>use such datasets.&nbsp; Data can come from 
anywhere including<BR>the workshop Web site. Such papers should include the 
URL<BR>address of the dataset(s) used.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">A copy of the public datasets used in the accepted 
papers<BR>will be posted on &quot;The PROMISE Software Engineering<BR>Repository.
&quot; 
Therefore, if applicable, the authors should<BR>obtain the necessary permission 
to donate the data prior<BR>to submitting their paper. All donors will be 
acknowledged<BR>on the PROMISE repository Web site.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">The use of publicly available datasets will 
facilitate<BR>generation of repeatable, verifiable, refutable, and<BR>improvable 
results, as well as providing an opportunity<BR>for researchers to test and 
develop their hypothesis,<BR>algorithms, and ideas on a diverse set of 
software<BR>systems. Examples of such datasets can be found at</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp;&nbsp; </FONT><A 
href="/exchweb/bin/redir.asp?URL=http://promisedata.org/repository" target=_blank>
<FONT 
face="Courier New">http://promisedata.org/repository</FONT></A></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">We ask all researchers in the field to assist us 
with<BR>expanding the PROMISE repository by donating their data<BR>sets. For 
inquiries regarding data donation please send<BR>an email to </FONT><A 
href="mailto:mail@promisedata.org" target=_blank><FONT 
face="Courier New">mail@promisedata.org</FONT></A></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><BR><FONT face="Courier New">Topics of Interest<BR>------------------<BR>In 
line with the above mentioned goals, the main topics<BR>of interest 
include:</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Applications of predictive models to software 
<BR>&nbsp; engineering data.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* What predictive models can be learned from 
<BR>&nbsp; software engineering data?</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Strengths and limitations of predictive 
models.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Empirical Model Evaluation Techniques. 
<BR>&nbsp; o What are best baseline models for different classes 
of<BR>&nbsp;&nbsp;&nbsp; predictive software models?<BR>&nbsp; o Are existing 
measures and techniques to evaluate<BR>&nbsp;&nbsp;&nbsp; and compare model 
goodness such as precision, recall, <BR>&nbsp;&nbsp;&nbsp; error rate, or ROC 
analysis adequate for evaluating software<BR>&nbsp;&nbsp;&nbsp; models? Or are 
more specific measures geared toward<BR>&nbsp;&nbsp;&nbsp; software engineering 
domain needed?<BR>&nbsp; o Are certain measures better suited for certain 
<BR>&nbsp;&nbsp;&nbsp; classes of models?<BR>&nbsp; o What are the appropriate 
techniques to test the <BR>&nbsp;&nbsp;&nbsp; generated models e.g. hold-out, 
cross-validation, or <BR>&nbsp;&nbsp;&nbsp; chronological 
splitting?</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Field evaluation challenges and 
techniques.<BR>&nbsp; o What are the best practices in evaluating the generated 
<BR>&nbsp;&nbsp;&nbsp; software models in the real world?<BR>&nbsp; o What are 
the obstacles in the way of field testing a <BR>&nbsp;&nbsp;&nbsp; model in the 
real world?<BR>&nbsp; o How to overcome obstacles in the acceptance of 
<BR>&nbsp;&nbsp;&nbsp; predictive models in the real world?</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* How to test the generated models? </FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* What are the obstacles in the way of field 
testing <BR>&nbsp; a model in the real world?<BR>&nbsp; o What predictive models 
are more prone to <BR>&nbsp;&nbsp;&nbsp; model shift? (Concept drift).<BR>&nbsp; 
o When does a model need to be replaced?<BR>&nbsp; o What are the best 
approaches to keeping the model <BR>&nbsp;&nbsp;&nbsp; in sync with software 
changes? </FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Building models using machine learning, 
statistical <BR>&nbsp; methods, and other methods.<BR>&nbsp; o How do these 
techniques lend themselves to building <BR>&nbsp;&nbsp;&nbsp; predictive 
software models?<BR>&nbsp; o Are some methods better suited for certain 
<BR>&nbsp;&nbsp;&nbsp; classes of models?<BR>&nbsp; o How do these algorithms 
scale up when handling <BR>&nbsp;&nbsp;&nbsp; very large amounts of 
data?<BR>&nbsp; o What are the challenges posed by the nature of 
data<BR>&nbsp;&nbsp;&nbsp; stored in software<BR>&nbsp;&nbsp;&nbsp; repositories 
that make certain techniques less<BR>&nbsp;&nbsp;&nbsp; effective than the 
others?</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Cost benefit analysis of predictive 
models<BR>&nbsp; o Is cost-benefit analysis a necessary step in 
evaluating<BR>&nbsp;&nbsp;&nbsp; all predictive models?<BR>&nbsp; o What are the 
requirements for one to be able to perform<BR>&nbsp;&nbsp;&nbsp; a cost benefit 
analysis?<BR>&nbsp; o What particular costs and benefits should be 
considered<BR>&nbsp;&nbsp;&nbsp; for these models?</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Case studies on building predictive software 
models.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><BR><FONT face="Courier New">Benchmark Dataset 
Papers<BR>------------------------</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">To encourage data sharing and/or publicize new 
and<BR>challenging research direction, a special category of<BR>papers will be 
considered for inclusion in the workshop.<BR>Papers submitted under this 
category should at least<BR>include the following information:</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* The public URL to a new dataset<BR>* Background 
notes on the domain<BR>* What problem does the data represent?<BR>* What would 
be gained if the problem was solved?</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Proposes a measure of goodness to be used to 
judge the<BR>&nbsp; results; for instance a good defect detector has a 
<BR>&nbsp; high probability of detection and a low probability <BR>&nbsp; of 
false alarm.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* A review of current work in the field (e.g. what 
is <BR>&nbsp; wrong with current solutions or why has no one solved <BR>&nbsp; 
this problem before?)</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Description of data format.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp; Recommended format is 
Attribute-Relation File Format (ARFF)</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp;&nbsp;&nbsp; </FONT><A 
href="/exchweb/bin/redir.asp?URL=http://www.cs.waikato.ac.nz/~ml/weka/arff.html" 
target=_blank><FONT 
face="Courier New">http://www.cs.waikato.ac.nz/~ml/weka/arff.html</FONT></A></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp; For an example of such a dataset see 
</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp;&nbsp;&nbsp; &quot;Cocomo NASA/Software 
cost estimation&quot;</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp; on the &quot;PROMISE Software 
Engineering 
Repository&quot;</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp;&nbsp;&nbsp; </FONT><A 
href="/exchweb/bin/redir.asp?URL=http://promisedata.org/repository" target=_blank>
<FONT 
face="Courier New">http://promisedata.org/repository</FONT></A></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp; However, if ARFF is not an 
appropriate format for<BR>&nbsp;&nbsp; your data, please provide a detailed 
description of<BR>&nbsp;&nbsp; your data format in the paper.&nbsp; A guideline 
from UCI<BR>&nbsp;&nbsp; Machine Learning repository for documenting 
datasets<BR>&nbsp;&nbsp; can be found in</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp;&nbsp;&nbsp; </FONT><A 
href="ftp://ftp.ics.uci.edu/pub/machine-learning-databases/DOC-REQUIREMENTS" 
target=_blank><FONT 
face="Courier New">ftp://ftp.ics.uci.edu/pub/machine-learning-databases/DOC-
REQUIREMENTS</FONT></A></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp;&nbsp; This information is placed before the 
actual data<BR>&nbsp;&nbsp; when using ARFF format.&nbsp; However, if you are 
using an<BR>&nbsp;&nbsp; alternative format that does not support comments 
in<BR>&nbsp;&nbsp; the dataset, provide this information in a separate 
file<BR>&nbsp;&nbsp; with extension .desc, and submit the URL of this 
file.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Preferably some baseline results</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><BR><FONT face="Courier New">Submission 
Process<BR>------------------</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Submissions are five to ten pages long (max). 
Papers must<BR>be original and previously unpublished. SUBMISSIONS 
WHICH<BR>INCLUDE EMPIRICAL RESULTS BASED ON PUBLICLY ACCESSIBLE<BR>DATASETS WILL 
BE GIVEN THE HIGHEST PRIORITY.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Accepted papers and other materials for the 
Proceedings<BR>must be revised to conform to IEEE style guidelines<BR>defined 
at:</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><A 
href="/exchweb/bin/redir.asp?
URL=http://www.computer.org/portal/site/ieeecs/menuitem.c5efb9b8ade9096b8a9ca0108b
cd45f3/index.jsp?%26pName=ieeecs_level1%26path=ieeecs/publications/cps%
26file=cps_format1.xml%26xsl=generic.xsl" target=_blank><FONT 
face="Courier New">
http://www.computer.org/portal/site/ieeecs/menuitem.c5efb9b8ade9096b8a9ca0108bcd45
f3/index.jsp?&amp;pName=ieeecs_level1
&amp;path=ieeecs/publications/cps&amp;file=cps_format1.xml&amp;xsl=generic.xsl
</FONT></A><FONT 
face="Courier New">&amp;</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Templates for submissions are found 
at:</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">* Latex:&nbsp;&nbsp; </FONT><A 
href="/exchweb/bin/redir.asp?URL=http://promisedata.org/2007/style/latex/" 
target=_blank><FONT 
face="Courier New">http://promisedata.org/2007/style/latex/</FONT></A><BR><FONT 
face="Courier New">* Word:&nbsp;&nbsp; </FONT><A 
href="/exchweb/bin/redir.asp?URL=http://promisedata.org/2007/style/word/" 
target=_blank><FONT 
face="Courier New">http://promisedata.org/2007/style/word/</FONT></A></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Accepted file formats are Postscript and PDF. The 
details<BR>of paper and data submission process are available at:</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><A href="/exchweb/bin/redir.asp?URL=http://promisedata.org/2007/CFP.html" 
target=_blank><FONT 
face="Courier New">http://promisedata.org/2007/CFP.html</FONT></A></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">To submit papers: </FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">&nbsp; * Email them to: </FONT><A 
href="mailto:2007@promisedata.org" target=_blank><FONT 
face="Courier New">2007@promisedata.org</FONT></A><BR><FONT 
face="Courier New">&nbsp; * Make the title of that email <BR>&nbsp;&nbsp;&nbsp; 
&quot;[SUBMISSION]: your paper title&quot; </FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Each paper will be reviewed by the program 
committee in<BR>terms of their technical content and their relevance to<BR>the 
scope of the workshop, as well as its ability to<BR>stimulate discussion. At 
least one author of accepted<BR>papers is required to register and attend the 
workshop.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Prior to the workshop the accepted papers will be 
posted<BR>on the workshop web page at:</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><A href="/exchweb/bin/redir.asp?URL=http://promisedata.org/2007" 
target=_blank><FONT 
face="Courier New">http://promisedata.org/2007</FONT></A></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">This is to facilitate a more fruitful discussion 
during<BR>the workshop.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Journal of Empirical Software Engineering: Special 
Issue<BR>--------------------------------------------------------</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Papers accepted to PROMISE 2007 (and 2006) will 
be<BR>eligible for submission to a special issue of the Journal<BR>of Empirical 
Software Engineering on repeatable experiments<BR>in software 
engineering.</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><BR><FONT face="Courier New">The issue will be edited by Tim Menzies. 
</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><BR><FONT face="Courier New">Important 
Dates<BR>---------------<BR>Submission of workshop papers&nbsp;&nbsp;&nbsp; 
January&nbsp; 20, 2007<BR>Notification of workshop papers&nbsp; February 10, 
2007<BR>Publication ready 
copy&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
March&nbsp;&nbsp;&nbsp;&nbsp; 5, 2007</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">General Chair<BR>-------------<BR>Gary 
Boetticher&nbsp;&nbsp;&nbsp;&nbsp; Univ. of Houston - Clear Lake</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Steering Committee<BR>------------------<BR>Gary 
Boetticher&nbsp;&nbsp;&nbsp;&nbsp; Univ. of Houston - Clear Lake<BR>Tim 
Menzies&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; West Virginia 
University, US<BR>Tom Ostrand&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
AT&amp;T</FONT></DIV>
<DIV><FONT face="Courier New"></FONT>&nbsp;</DIV>
<DIV><FONT face="Courier New">Program Committee<BR>-----------------<BR>Vic 
Basili&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; University of Maryland, 
US<BR>Dan Berry&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; University 
of Waterloo, Canada, US<BR>Barry Boehm&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
University of Southern California <BR>Gary Boetticher&nbsp;&nbsp;&nbsp; Univ. of 
Houston - Clear Lake, US<BR>Lionel Briand&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Carleton 
University, Canada<BR>Bojan Cukic&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; West 
Virginia University, USA<BR>Alex Dekhtyar&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
University of Kentucky, US<BR>Martin Feather&nbsp;&nbsp;&nbsp;&nbsp; NASA JPL, 
US<BR>Norman Fenton&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Queen Mary (U. of London), 
UK<BR>Jane Hayes&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; University of 
Kentucky, USA<BR>Jairus Hihn&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; NASA 
JPL's Deep Space Network, US<BR>Gunes 
Koru&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; U. of Maryland, Balt. Cty 
US<BR>Tim Menzies&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; West Virginia 
University, US<BR>Martin Neil&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Queen 
Mary(U. of London), UK<BR>Allen Nikora&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; NASA 
JPL, US<BR>Tom Ostrand&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; AT&amp;T, 
US<BR>Daniel Port&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; University of 
Hawaii, USA <BR>Julian Richardson&nbsp; NASA ARC, US<BR>Guenther 
Ruhe&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; University of Calgary, Canada<BR>Martin 
Shepperd&nbsp;&nbsp;&nbsp; Brunel University, UK<BR>Forrest 
Shull&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Fraunhofer Centre Maryland, USA <BR>Willem 
Visser&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; NASA ARC, US<BR>Elaine 
Weyuker&nbsp;&nbsp;&nbsp;&nbsp; AT&amp;T, US<BR>Laurie 
Williams&nbsp;&nbsp;&nbsp; North Carolina State Univ., USA<BR>Marv 
Zelkowitz&nbsp;&nbsp;&nbsp;&nbsp; University of Maryland, US<BR>Du 
Zhang&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Cal. State 
Univ., Sacramento, USA</FONT></DIV>
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