Mike Chapman, Chief Information Officer at Galaxy Global, commented today that “Current methods, in widespread use, don’t take into account the business context of the data miners. With TTOY.net we intend to construct business-aware data mining methods.”

This work will be funded as part of an on-going grant from NASA to Galaxy Global, to build a repository of software engineering products and metrics. For more, go to http://mdp.ivv.nasa.gov.

This is joint work with TTOY, University of Southern California, University of Hawaii, and JPL

@inProceedings{me05a,
     author=  {Tim Menzies and Zhihao Chen and   Dan Port and Jairus Hihn}
     title=  {Simple Software Cost Estimation:  Safe or Unsafe?}
     booktitle=  {Proceedings, PROMISE workshop,  ICSE 2005},
     year=  2005,
     notes={Available from   \url{http://menzies.us/pdf/05safewhen.pdf}}
}

With these tools, we strive to simplify human-in-the-loop theory formation and revision. The goal is to augment what humans do best with what computers do best. Humans, we believe, are very good at proposing new theories while computers are very good at reviewing and changing those theories.

(Note that by “theory formation” includes both knowledge engineering and software engineering tasks. KE uses explicit declarative theories while the thoeries in SE are often tacit and may only be explicated when, say, a test engineer asks the question “what is it, exactly, that this system is trying to do?”)

In the (almost) two decades of building tools for this domain, we have tried various approaches including logic programming, object-oriented programming, rule-based programming, visual programming, qualitative reasonng, design or analysis or inference patterns; and classical abduction.

Right now, our preferred method is a combination of stochastic abduction (which can generate consistent possibilities very fast) and treatment learning (a special kind of data miner invented by TTOY). The hypothesis here is such a combination is simple to implement, fast to run, can scale to very large theories, and which can significantly simplify the modeling process.

This is a joint study between TTOY, USC, and University of Hawaii.

@inProceedings{chen05,
     author=   {Zhihao Chen and Tim Menzies and Dan Port},
     title=   {Feature Subet Selection Improves Software Cost Estimation}
     booktitle= {Proceedings, PROMISE workshop, ICSE 2005, St. Louis},
     year=   {2005},
     notes= {Available from \url{http://menzies.us/pdf/05fsscocomo.pdf}}
}

TTOY and JPL have undertaken a three year study to use data mining methods to explore the effects of software project attributes on software costs.

Dr. Menzies has been working on advanced software engineering techniques since 1986. He is the author of nearly 150 research papers. For two years, was the Software Engineering Research Chair at NASA’s Independent Verification and Validation (IV&V) Facility. Currently, he is on the research faculty at Computer Science, Portland State, and Computer Science and Electrial Engineering, West Virginia University.

Dr. Menzies received his PhD from the University of New South Wales, Sydney, Australia.

In his career, he has been a nursel a professional knowledge engineer; a taxi-driver; a NASA rocket(ish) scientist; a newspaper editor; an OO consultant; and a university lecturer. Always Australian; but never a surfer/hippy (he tried, but a watch needs tension in the spring to make it go).

His degrees include BSc(Com. Sci.) UNSW, Australia, 1985; a MCogSc UNSW, Australia, 1988; and a PhD(AI) UNSW, Australia, 1995

Nationality: Australian with permanent resident status in the USA.

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