\begin{slide}[toc=Eg4: static code]{Eg4: Defect predictors from Static code measures}
	\bi
		\item IEEE TSE  (\cite{me06d})
		\item Modules from eight NASA projects, MDP, 
				described using LOC, McCabe, Halstead metrics
				\item New methods
			\bi
				\item Shoot-out between : 
						\bi
								\item Bayesian; 
								\item simple rule learners; e.g. $v(g) \ge 10$
								\item complex tree learners; C4.5
						\ei
				\item Simple pre-processor on the exponential numerics
					\bi
						\item $num = log( num < 0.000001 ? 0.000001 : num)$
					\ei
			\ei
	\item Prior state(s)-of-the-art, percentage of defects found:
			\bi
				\item IEEE Metrics 2002 panel: manual software reviews finds $\approx 60\%$ 
				\item Raffo: industrial reviews finds TR(min,mod,max) = TR(35, 50, 65)\% 
				\item My old data mining experiments: prob \{detection,false alarm\}=\{36,17\}\%
			\ei
\ei
			\end{slide}	
