| dc.description.abstract | Developing a large software system involves many complicated, varied, and
inter-dependent tasks, and these tasks are typically implemented using a
combination of defined processes, semi-automated tools, and ad hoc
practices. Stakeholders in the development process --- including software
developers, managers, and customers --- often want to be able to track the
actual practices being employed within a project. For example, a customer
may wish to be sure that the process is ISO 9000 compliant, a manager may
wish to track the amount of testing that has been done in the current
iteration, and a developer may wish to determine who has recently been
working on a subsystem that has had several major bugs appear in it.
However, extracting the software development processes from an existing
project is expensive if one must rely upon manual inspection of artifacts
and interviews of developers and their managers. Previously, researchers
have suggested the live observation and instrumentation of a project to
allow for more measurement, but this is costly, invasive, and also requires
a live running project.
In this work, we propose an approach that we call software process
recovery that is based on after-the-fact analysis of various kinds of
software development artifacts. We use a variety of supervised and
unsupervised techniques from machine learning, topic analysis, natural
language processing, and statistics on software repositories such as version
control systems, bug trackers, and mailing list archives. We show how we can
combine all of these methods to recover process signals that we map back to
software development processes such as the Unified Process. The Unified
Process has been visualized using a time-line view that shows effort per
parallel discipline occurring across time. This visualization is called the
Unified Process diagram. We use this diagram as inspiration to produce
Recovered Unified Process Views (RUPV) that are a concrete version of this
theoretical Unified Process diagram. We then validate these methods using
case studies of multiple open source software systems. | en |
