Over the past twenty years, the development of fault prediction
models has been a very active research area.  The reason for such
a significant attention to automated quality predictors lays in
their practical importance.  Current models are useful, as they
allow software project managers to better guide the allocation
usually meager quality assurance resources to artifacts which need
them the most.  

Recent results now indicate that
this
current research paradigm, which relied on relatively straightforward
application of machine learning tools, has reached its
limits.  Building software quality predictors via data mining is
essentially an inductive generalization over past experience.
According to Mitchells classic model of data mining~\cite{mitchell97}, any
inductive generalization explores a version space of possible
theories. All data miners hit a performance ceiling effect when
they cannot find additional information that better relates software
measure with fault occurrence.

To build better quality predictors that break through ceiling
effects, we must introduce more topology into the search space.
Standard machine learning algorithms lack the business knowledge
which characterizes software projects. To add that business knowledge,
we propose to investigate which business level project concerns
will be the most promising so they can provide guidance for automated
fault prediction models.  We plan to utilize human-in-the-loop rule
elicitation and maintenance combined with automated rule inference.
We expect that this approach will allow software managers to safely
focus on the application of quality assurance techniques of choice,
confident that automated quality predictors will raise alerts about
the artifacts where quality issues actually exist.



{\bf INTELLECTUAL MERIT:} It is time for a fundamental paradigm shift in
the way we develop models for software quality prediction. In order
to improve software quality prediction models, we will develop the
methodology and supporting tools which will make quality prediction
a joint endeavor between software quality experts and data mining
algorithms. Specifically, we will shift the focus from data mining
algorithms towards the software engineering data passed to those
algorithms.  This proposal will create a new paradigm in automatic
quality prediction, adding the information to the models search
space and allowing them to improve their predictive performance
along the dimensions that make the most sense for software engineering
projects.

{\bf BROADER IMPACTS:} The proposed research addresses key concerns with
the current generation of defect predictors which, when resolved,
will serve to advance the understanding of data mining in general
and software defect prediction in particular. Fault predictors are
widely used in industry to focus verification and validation efforts.
Better fault predictors would better focus usually scarce resources
leading to the detection of the most consequential faults earlier
in the development lifecycle. This, in turn, will lead to production
of higher quality software using fewer resources. The projects
educational activities will include involvement of undergraduate
and graduate students in research, the development of new and the
enhancement of existing courses. We have a good track record and
remain strongly committed to bringing research and educational
opportunities to students from the underrepresented groups.





