neering, project costs are people costs),a
you can bring code to fruition faster.
˲ Despair: there is a firm limit to the
time you can gain: 25%. It seems a universal constant of software engineering.
The “despair” typically gets the most
attention at first, since it sets an abso-
lute value on how much money can buy
(so to speak) in software: try as hard as
you like, you will never get below 75% of
the nominal (optimal) value. The “im-
possible zone” in Figure 1 expresses the
fundamental limitation. This negative
result is the reasoned, precise modern
replacement for the older folk “law.”
The positive part is just as important.
A 75%-empty glass is also 25% full. It may
be disappointing for a project manager
to realize no amount of extra manpower
will make it possible to guarantee more
than a 25% reduction in time. But it is
just as important to know such a reduc-
tion, not at all insignificant, is reachable
given the right funding, people, tools,
and management skills. The last point is
critical: money by itself does not suffice,
you need management; Brooks’ Law, as
noted, is mostly an observation of the ef-
fects of bad management.
Figure 1 only carries the essential
idea, and is not meant to provide precise
numerical values. Figure 2, the original
figure from McConnell’s book, plots effort against time rather than the reverse
but, more importantly, it shows several
curves, each corresponding to a published empirical study or cost model
surveyed by the book.
On the left of the nominal point, the
curves show how, according to each
study, increased cost leads to decreased
time. They differ on the details: how
much the project needs to spend, and
which maximal reduction it can achieve.
They all agree on the basic Shortest Possible Schedule result: spending can decrease time, and the maximal reduction
will not exceed 25%.
The figure also provides an answer,
although a disappointing one, to another
question. So far this discussion has as-
sumed time was the critical resource and
we were prepared to spend more to get a
product out sooner. But sometimes it is
the other way around: the critical resource
is cost or, concretely, the number of devel-
a This is the accepted view, even though one
might wish the industry paid more attention
to investment in tools in addition to people.
opers. Assume nominal analysis tells us
the project will take four developers a year
and, correspondingly, cost 600K (choose
your currency). We have a budget of 400K.
Can we spend less by hiring fewer devel-
opers, accepting it will take longer?
On that side, right of the nominal
point in Figure 2, McConnell’s survey
of surveys shows no consensus. Some
studies and models do lead to decreased
costs, others suggest that with the in-
crease in time, the cost will increase, too.
(Here is my interpretation, based on
my experience rather than on any sys-
tematic study: you can achieve the origi-
nal goal with a somewhat smaller team
over a longer period, but the effect on
the final cost can vary. If the new time
is t’= t + T and the new team size s’= s -
S, t and s being the nominal values, the
cost difference is proportional to Ts - t’S.
It can be positive as well as negative, de-
pending on the values of the original t
and s and the precise effect of reduced
team size on project duration.)
The firm result, however, is the left
part of the figure. The Shortest Possible
Schedule theorem confirms what good
project managers know: you can, within
limits, shorten delivery times by bringing all hands on deck. The precise version deserves to be widely known.
1. Boehm, B. W. Software Engineering Economics, Prentice
2. McConnell, S. Software Estimation Demystifying the
Black Art, Microsoft Press, 2006.
3. McConnell, S.: Brooks’ Law Repealed, in IEEE Software,
vol. 16, no. 6, pp. 6–8, November-December 1999.
Bertrand Meyer is a professor of software engineering
(emeritus) at ETH Zurich (Switzerland), chief technology
officer of Eiffel Software (Goleta, CA, USA), professor at
Politecnico di Milano (Italy), and head of the software
engineering lab at Innopolis University (Russia).
© 2019 ACM 0001-0782/20/1 $15.00
Figure 1. General view of the Shortest Possible Schedule theorem.
Add manpower etc.
Figure 2. Original illustration of the Shortest Possible Schedule (reproduced with the
The “Impossible” Zone
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1. 1 1. 2 1. 3
MK II FP
Source: Adapted and extended from Software Sizing and Estimating: MK II (Symons 1991), Software
Cost Estimation with Cocomo II (Boehm et al 2000), “Estimating Web Development Costs: There Are
Differences” (Reifer 2002), and Practical Project Estimation, 2nd Edition (ISB5G 2005).
Source: Adapted and extended from Software Sizing and Estimating: MK II (Symons 1991), Software Cost Estimation
with Cocomo II (Boehm et al 2000), “Estimating Web Development Costs: There Are Differences” (Reifer 2002), and
Practical Project Estimation, 2nd Edition (ISBSG 2005).