allen, what you are referring to is commonly called (wholistic) top-down estimation and (partitioned) bottom-up estimation.
this paper bei jorgensen should give some references: http://www.simula.no/departments/engineering/.artifacts/jorgensen_shepperd_b estreview_tse.pdf a common practice in the environments i am currently working is that in the bidding process a rough top-down estimate is made by some senior people mostly by comparison with accomplished projects. in many cases the difference between the estimate and the bidding price (estimate + margin) is not thoroughly addressed. during the execution of the projects bottom-up estimates are made in order to determine the schedule. at this stage partitioning is more or less natural as tasks of reasonable size have to be assigned. in this regard "partitioning" does not generate any overhead but greatly improves chances of reasonable estimates and avoidance of unidentified tasks - and consequently a reliable project schedule. best regards, gerold -----Ursprüngliche Nachricht----- Von: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Auftrag von [EMAIL PROTECTED] Gesendet: Sonntag, 21. Januar 2007 23:23 An: discuss@ppig.org Betreff: PPIG discuss: software estimating and partitioning A key aspect of programming in practice is the reliable estimation of size, time and effort. It seems like most people that are good at estimating do so by partitioning the problem into smaller pieces that can be handled more easily. Then, final estimates are accomplished by combining the pieces. This procedure is certainly what engineering approaches teach and I think other approaches as well. But I haven't been able to find much empirical data suggesting that software estimation done by partitioning is superior to that done more "wholistically". I assume that I am missing something huge and obvious since partitioning is such an important cognitive tool (and has been for such a long time). But, I haven't found empirical references yet Can anybody direct me to references on this topic. Thanks very much Dr. Allen Milewski Department of Software Engineering Monmouth University [EMAIL PROTECTED] --
