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\title{TSE Submission}

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My co-authors and I are please to submit the article, 
``Reliable Effects Screening: A Distributed Continuous Quality
Assurance Process for Monitoring Performance Degradation in Evolving
Software Systems''
for consideration for possible publication in the IEEE Transactions on 
Software Engineering. 

Because this article contains a fair amount of statistical 
material, we respectfully suggest that associate editor
{\bf Audris Mockus}, who holds a Ph.D. in statistics, might be
a good choice to handle the paper's review.

This paper extends earlier work which appeared at the 2005 
International Conference on Software Engineering. These 
extensions greatly enhance the readability of the paper,
give more details on the previously presented work, and,
most importantly, add a crucial step that allows developers
to verify key technical assumptions that previously had to 
be taken on faith, adding considerable risk to the use of 
the technique.

Some of the specific changes we have made include:

\begin{itemize}

\item Edited the entire article to improve readability

\item Rewrote and reorganized much of Section II to better
explain our support system and tools. Also added formal 
descriptions of the configuration model and the adaptation strategies. 

\item Added more detail in Section III-C, Screening Designs in Action
\begin{itemize}
  \item Added more detailed discussions on aliasing structures of screening
    designs
  \item  Added the definition of ``defining relations'' and some discussion on
    how they can be used to find the aliasing structure of a given design
\end{itemize}

\item Added new technical material which appears in Section IV-E
\begin{itemize}
  \item Discussed the implicit assumptions made in the earlier 
    main effects screening process
  \item Introduced the D-optimal Designs
  \item Discussed how D-optimal designs can be used to
    augment main effects screening experiments to reliably and efficiently
    validate these assumptions.
  \item Presented empirical evaluation of leveraging D-optimal designs to 1)
    break aliasing relations and 2) look for higher effects.
\end{itemize}

\item Added an extended Discussion section that adds:
\begin{itemize}
  \item more details on the experiments overall results 
  \item quantifies the code generation metrics 
  from the Model-driven Development tools.
\item discusses how Design Of Experiments theory helps developers
 in several unexpected ways. 

\item  Added Section VI: Guidelines for practitioners

\item Considerably improved related work section to compare and
  contrast our work more fully with related research.

\end{itemize}
\end{itemize}

We are submitting the original paper along with our new 
article so that you may confirm this.


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