Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!ames!henry.jpl.nasa.gov!elroy.jpl.nasa.gov!ucla-cs!lanai!johnny
From: johnny@lanai.cs.ucla.edu (Jia Hong Chen)
Newsgroups: comp.software-eng
Subject: Re: Software Failure Analysis
Keywords: Software failure analysis, quantization errors, resolution
Message-ID: <27545@shemp.CS.UCLA.EDU>
Date: 28 Sep 89 22:01:41 GMT
References: <10743@dasys1.UUCP> <34348@regenmeister.uucp> <592@halley.UUCP> <290@cs.nps.navy.mil>
Sender: news@CS.UCLA.EDU
Reply-To: johnny@lanai.UUCP (Jia Hong Chen)
Organization: UCLA CS DEPT.
Lines: 38


For ANOTHER view of Knight and Leveson's experiment, one recent paper
which might be interesting to you is "Failure Masking: a Source of
Failure Dependency in Multi-Version Programs", P. G. Bishop and F. D.
Pullen (Central Electricity Research Laboratories, Leatherthead, UK).
It appeared in the proceedings of the first International Working
Conference on Dependable Computing for Critical Applications at Santa
Barbara, August 23-25, 1989.

Quotated from the abstract: " ....  Error masking behavior can be
predicted from the specification (prior to implementation), and simple
modifications to the program design can minimize the error masking
effect and hence the observed dependency."

Prof. Algirdas Avizienis in the question and answer session after the
presentation of the paper by Bishop made some comments about the
paper.  I don't remember the exact words.  Basically he mentioned that
the "error masking" is another way of saying that if you reduced the
resolution of something and make comparisons on the the variable(s)
with the resolution reduced, you end up losing something.  During the
conference, I had chances to visit my friend's (a EE graduate at UCSB)
speech lab on campus.  He demonstrated to me some of their speech
coding (based on Linear Predictive Coding) research, with different
bit rates.  Intuitively with more bit rates you can provide higher
quality sound.  But with some algorithms, you might be able to improve
the quality with the same bit rate.

People who are familiar with the concept of "quantization errors"
should have no difficulty understanding the mumblings in the above
paragraph.  Also, I sort of remember some similar ideas when I studied
the D-algorithm back when I was an undergraduate at National Taiwan
University.

Check out the paper and make up you mind.  Don't trust everything
published.
Jia-Hong Chen

johnny@cs.ucla.edu                    ...!ucbvax!cs.ucla.edu!johnny