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From: crm@romeo.cs.duke.edu (Charlie Martin)
Newsgroups: comp.software-eng
Subject: Re: Programmer productivity
Message-ID: <16266@duke.cs.duke.edu>
Date: 30 Nov 89 19:20:25 GMT
References: <16170@duke.cs.duke.edu> <34819@regenmeister.uucp> <16186@duke.cs.duke.edu> <31986@watmath.waterloo.edu> <16231@duke.cs.duke.edu> <9986@june.cs.washington.edu>
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In article <9986@june.cs.washington.edu> peterd@june.cs.washington.edu (Peter C. Damron) writes:
>In article <16231@duke.cs.duke.edu> crm@romeo.UUCP (Charlie Martin) writes:
>
>Excuse my ignorance, but how does one use source lines of code (SLOC)
>in any predictive way?  Presumably, we are talking about estimating
>how long a particular project will take.  I understand that once you
>know how many SLOC it will take for the project then you can predict
>how long the project will take.  But, how does one translate a
>specification into some number of SLOC?  This seems difficult to me.
>

It *is* difficult.  The best semiformal way is to have a collection of
estimates and final SLOC results for a bunch of programs in the same
general domain (MIS, real-time hardware control, whatever).  These
provide a basis for building empiricals of SLOC from something that is
measurable or countable in the specification.

More often, the expert takes a wild-assed guess from the specification
and some analysis of it, which amounts to working with the same data
base of information on a less formal (and less accurate) level.

Even so, the guess of number of SLOC is often accurate within 10-20
percent in practice.

The whole point of SLOC as a predictive number is that SLOC is
measurable, and time spent coding is also measurable.  Other rules (how
long to do detailed design) derive from lifecycle models and previopus
experience.  None of these is very good, and all of the questions of how
do you estimate SLOC and how do they relate to results are good ones.


Charlie Martin (crm@cs.duke.edu,mcnc!duke!crm) 


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