[Dbworld] Workshop - QUantitative TEchniques for SoftWare Agile Process

QUTE-SWAP

QUantitative TEchniques for SoftWare Agile Process
to be held on November 5, 2004. 
Web site URL: http://ra.crema.unimi.it/qute-swap/

Hosted at SIGSOFT 2004 / FSE-12
http://www.isr.uci.edu/FSE-12/workshops.html

Important Dates (subject to change):

Paper submission: July 30
Author notification: September 15
Camera-ready papers: October 1

Workshop Organizers:
Ernesto Damiani, University of Milan, Italy 
Michele Marchesi, University of Cagliari, Italy 
Giancarlo Succi, University of Bozen-Bolzano, Italy      
Davide Carboni, CRS4, Italy      
Alberto Sillitti, University of Bozen-Bolzano, Italy 
Contact Person:
Ernesto Damiani, EDamiani at crema dot unimi dot it 

Collecting and analyzing software process data can help 
to control and predict the performance of software development activities, 
helping software developers to achieve both business and technical objectives. 
Experience has shown that quantifying the software process operation 
can improve insight, e.g. allowing assessing 
the impact of process change on the software products.

On the other hand, the widespread adoption of agile processes 
and the increasing structural diversity of software development organizations 
around the world as well as new concerns e.g. about privacy, 
are driving the need for non-intrusive, 
cost-effective methods capable to deliver 
long term success in collecting process data without 
further increasing the burden of process management. 
Process data mining techniques are also being investigated 
aimed at extracting valuable knowledge, 
capable to improve software products' quality.

This workshop is aimed at highlighting the cutting edge 
of process data collection and analysis research, 
fostering information exchange between researchers working 
on data analysis for agile software process improvement 
and practitioners interested in exploiting software 
data collection and analysis techniques as a basis 
for making process decisions and predicting process performance.

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