J. Software Engineering & Applications, 2010, 3: 1-10
doi:10.4236/jsea.2010.31001 Published Online January 2010 (http://www.SciRP.org/journal/jsea)
Copyright © 2010 SciRes JSEA
Models for Improving Software System Size Estimates
during Development
William W. AGRESTI1, William M. EVANCO2, William M. THOMAS3
1Carey Business School, Johns Hopkins University, Baltimore, USA; 2Statistical Solutions, Philadelphia, USA; 3The MITRE Corpo-
ration, McLean, VA, USA.
Email: agresti@jhu.edu, wevanco@verizon.net, bthomas@mitre.org
Received August 28th, 2009; revised September 16th, 2009; accepted September 29th, 2009.
This paper addresses the challenge of estimating eventual software system size during a development project. The ap-
proach is to build a family of estimation models that use information about architectural design characteristics of the
evolving software product as leading indicators of system size. Four models were developed to provide an increasingly
accurate size estimate throughout the design process. Multivariate regression analyses were conducted using 21 Ada
subsystems, totaling 183,000 lines of code. The models explain from 47% of the variation in delivered software size
early in the design phase, to 89% late in the design phase.
Keywords: Software Size, Estimation, Ada, Regression, Re-Estimation, Metrics
1. Introduction
Before software development projects start, customers and
managers want to know the eventual project cost with as
much accuracy as possible. Cost estimation is extremely
important to provide an early indicator of what lies ahead:
will the budget be sufficient for this job? This need has
motivated the development of software cost estimation
models, and the growth of a commercial market for such
models, associated automated tools, and consulting sup-
port services.
When someone who is new to the use of cost estima-
tion models looks at the estimation equation, it can be
quite disconcerting. The only recognizable variable on
the right-hand side is size, surrounded by a few modify-
ing factors and shape parameters. So, if the project is just
beginning, how do you know the size of the system?
More experienced staff members explain that you must
first estimate the size so that you can supply that value in
the equation, thus enabling you to estimate cost. How
good can the cost estimate be if it depends so strongly on
a quantity that won’t be known until the end of the pro-
ject? The underlying logic prompting this question is
irrefutable. This situation is well known and recognized
(e.g., [1, 2]). Users of such cost estimation models are
dependent, first and foremost, on accurate size estimates.
Of course, there are other ways to estimate costs (e.g., by
analogy and experience), but the more analytically satis-
fying models estimate cost as a function of size. Thus,
the need for an accurate cost estimate often translates
into a need for an accurate size estimate.
This paper addresses size estimation, but distinguishes
between estimation before development and during de-
velopment. Size estimation prior to the start of a software
project typically draws on analogy and professional
judgment, with comparisons of the proposed system to
previously developed systems of related functionality.
Function point counts may also be used to provide an
estimate of size, but, of course, the accuracy of the size
estimate depends on accurate knowledge of the entities
being counted.
Once the project begins, managers would like to keep
improving their size estimate. However, we are moti-
vated by our observations of current practice in which
managers, during development, revert to predevelopment
estimates of size (and effort and cost) because of a lack
of effective ways to incorporate current development
information to refine their initial estimates [3]. Ideally
there would be straightforward methods to improve the
predevelopment estimates based on early project experi-
ences and the evolving product. In this paper we focus on
the evolving product as a source of information for up-
dating the size estimate.
The research reported in this paper addresses the ques-
tion of how to improve our capability for estimating the
size of software systems during a development project.
More specifically, it reports on building a family of
models for successively refining the size estimate during
Models for Improving Software System Size Estimates during Development
the development process. The notion of a family of mod-
els is intended to address the challenge of successively
refining the initial estimate as the project unfolds. The
research has three motivations: the widely known poor
record of large software projects to be delivered on time
and on budget (due in part to poor estimation capability),
the persistent illogic of needing to know size to estimate
cost, and the challenge of successive size reestimation
during a project.
The remainder of the paper discusses related work, the
design and implementation process, the estimation mod-
els, the empirical study, statistical results, limitations of
the analyses, and future directions.
2. Related Work
Reasonably accurate size estimates may be achievable
when an organization has experience building systems in
the domain of the new project. The new system may be
analogous to previous ones or related to a product line
that is familiar. There may even be significant reuse of
class libraries, designs, interfaces, or code to make the
estimation easier. But accurately estimating eventual
system size grows more difficult when the new system
ranks higher in novelty or, using the nomenclature of
systems engineering, is unprecedented. Even when the
application domains are similar, new projects may re-
quire significantly more code to account for enhanced
requirements for security and robustness that were not
associated with previous systems.
An especially appealing category of estimation models
is one that consists of models that use early constructs
from the evolving system. The analyses that are closest
to the research reported here were done as part of the
evolution of COCOMO models over the years. CO-
COMO II has the characteristic of the models being built
in this paper by recognizing the need for a family of
models. For COCOMO II, the models take advantage of
increased learning during the project: the early prototyp-
ing stage, early design stage, and postarchitecture stage
As development gets underway in a project, the repre-
sentations used in specification and design provide op-
portunities for the measurement of key constructs used in
those representations. Then models can be built to relate
values from those constructs to measures of system size.
This practice has been in place for decades. One of the
earliest and most influential of such models was Tom
DeMarco’s Bang metric that related values from data
flow diagrams and other notations to eventual code size.
[5]. Similar models have been built, based on capturing
measures of entity-relationship and state-transition dia-
grams from early system specifications [6]. Bourque and
Cote [7] performed an experiment to develop and vali-
date such a predictive model based on specification
measures that were obtained from data flow and entity-
relationship diagrams. It was found that a model using
the number of data elements crossing an elementary pro-
cess in the data flow diagram as the sole explanatory
variable performed fairly well as a predictive model.
Much of the research using early system artifacts for
estimation is directed at estimating effort and cost. Pau-
lish, et al., discussed the use of the software architecture
document as the primary input to project planning [8].
Mohagheghi et al., described an effort estimation model
based on the actual use cases of the system being built
[9]. Pfleeger used a count of objects and methods as a
size measure in a model of software effort [10]. Jensen
and Bartley investigated the use of object information
obtained from specifications in models of programmer
effort [11]. They proposed that development effort is a
function of the number of objects, operations, and inter-
faces in the system, and that counts of these entities can
be obtained from a textual specification.
More closely related to the current paper is reported
research that is focused on estimating size. Laranjeira [12]
provided a method for sizing object-oriented systems
based on successive estimations of refinements of the
system objects. As the system becomes more refined, a
greater confidence in the size estimates is obtained. He
proposed the use of statistical techniques to determine a
rate of convergence to the actual estimate. The estimation
is still subjective, but the method gives an indication of
progress toward the convergence of the estimates, and as
such provides an objective, statistically based confidence
interval for the estimates.
Minkiewicz [13] offers a useful overview of the evolu-
tion and value of various measures of size, including the
most widely used, lines of code and function points. The
model in [14] estimated size, as measured by function
points [15] directly from a conceptual model of the sys-
tem being built. Tan et al. built a model to estimate lines
of code based on the counts of entities, relationships, and
attributes from the conceptual data model [16]. A model
by Diev [17] related early information on use cases into a
size estimate, measured in function points. The models in
[18] produce lines-of-code estimated from a
VHDL-based design description. Antoniol et al. investi-
gated the adapting of function points to object-oriented
systems by defining object-oriented function points (OO
FPs) [1]. They first identified constructs in object- ori-
ented systems (e.g., classes and methods) to use as pa-
rameters for OOFPs, then built a flexible model to esti-
mate system size. Their pilot study showed that the
model was promising as a way to estimate lines of code
from OOFPs.
There has been considerable research on ways to use
object-oriented designs in the Unified Modeling Lan-
guage (UML) to estimate size as measured in function
points. Capturing the design in UML facilitates develop-
ing an automated tool to extract counts of entities and
Copyright © 2010 SciRes JSEA
Models for Improving Software System Size Estimates during Development
Copyright © 2010 SciRes JSEA
other data that can be used in empirical studies to de-
velop size estimation models. Most similar to the re-
search here are studies that developed families of models
that provided a succession of estimates as more design
information is known. For example, Zivkovic et al. [19]
developed automated approaches to estimating function
point size using UML design information, including data
types and transactional types. Hericko and Zivkovic’s
analysis [20] was most similar to the research reported
here because it involved basing the size estimate on more
detailed information about a UML design. The first esti-
mate used use case diagrams, the second estimate added
information from activity diagrams, and the final esti-
mate added information from class diagrams. The esti-
mates, which did improve as new information was in-
corporated into the models, produced an estimate meas-
ured in function points, as opposed to the lines of code
used in the research in this paper.
3. The Design and Implementation Process
Our analysis and model building relies on assumptions
concerning the progress of design and implementation.
This section discusses these assumptions.
We are investigating systems that were built in Ada,
which proceeds from specifying relationships among
larger units (packages) to a specification of the interior
details of these units. Ada was used as a design notation,
which means that a machine-readable design artifact is
available for observation and analysis. Royce [21] was
one of the first to discuss this use of a single language in
the Ada process model: “Regardless of level, the activity
being performed is Ada coding. Top-level design means
coding the top-level components (Ada main programs,
task executives, global types, global objects, top-level
library units, etc.). Lower-level design means coding the
lower-level program unit specifications and bodies.”
The development teams used a generic object-oriented
design process with steps to identify the objects, identify
the operations, establish the visibility of the operations,
specify the interface, and implement the objects. Fol-
lowing such a method implies that certain information
about the design will be available at earlier times in the
development process than other information. For exam-
ple, a count of the number of operations will be available
prior to a count of the number of parameters that will be
needed. While there is iteration involved in the method,
and the process must be repeated at various levels of ab-
straction, following such a process should result in a
steady release of successively more detailed information
about the evolving system. Figure 1 attempts to capture
this unfolding of information by showing notional
growth curves for several entities in an Ada development
process. The number of library units stabilizes first, fol-
lowed by the context coupling, number of visible pro-
gram units, and so on until finally all the source lines of
code are defined when the objects are fully implemented.
Our approach in size estimation is to take advantage of
this evolving machine-readable product, using character-
istics of the design artifact to refine our size estimates at
successive stages in the development process, where
each stage corresponds to a time when a particular aspect
of the design has stabilized (e.g., a count of the number
of library units).
Figure 1. Notional growth curves of design features
Models for Improving Software System Size Estimates during Development
Figure 2. Four size models: design representations and features
Figure 2 depicts one view of the successive design and
implementation of an Ada system. In Figure 2, we iden-
tify four intermediate stages (A, B, C, and D) as the sys-
tem is built.
More detailed information is available at each stage
and this information can be used to obtain more accurate
size estimates.
We acknowledge that the approach and, therefore, the
applicability of the results, depend on the assumptions
concerning how the design evolves. In the process model
described here, the needed behavior and functionality of
the system are first organized into loci of capabilities that
are captured in the design artifact as library units. This
view is consistent with Royce's identification of top-level
design activities in the quote above, and with the first
process step of identifying the objects. Thus, Stage A in
Figure 2 corresponds to a stage where all library units
have been identified.
As the design becomes more detailed, designers iden-
tify the visible program units in each library unit (Stage
B). The visible program units are the operations accessi-
ble by other units; at this stage, designers are also identi-
fying relationships among library units. For a particular
library unit to fulfill its role in the design, it needs re-
sources of other units and these resources are provided
through context coupling. This corresponds to the second
and third process steps, in which the operations are iden-
tified and the visibility established.
More is known about the design at Stage C. To im-
plement the visible program units, the developer defines
hidden program units to perform necessary, but local,
functions in support of the visible operations. This stage
corresponds to the process step of implementing the ob-
Stage D is well into the detailed coding of the system.
At this stage all declarations have been made. Admittedly,
at this stage, the source lines of code are building and the
actual system size (in terms of lines of code) is becoming
known. However, having an explicit Stage D recognizes
cases in which the declarations may be relatively com-
plete, but the procedural code is not.
4. Estimation Models
The stages shown in Figure 2 provide a logical progres-
sion for system development. The information available
at each of these stages has the potential to be used to de-
termine size estimates with greater accuracy than those
estimates derived at the inception of the project. What is
needed, of course, are models that show how to use the
information to estimate size.
The models for estimated size (Size) are of the form:
Size = alx1 + a2x2 + ...+
where Size is measured in source lines of code; x1, x2...
are the explanatory variables; al, a2... are the parameters
Copyright © 2010 SciRes JSEA
Models for Improving Software System Size Estimates during Development5
to be estimated; and is an error term.
Four different models were built, corresponding to the
four different stages in the design phase as shown in Fig-
ure 2. These models show source lines of code estimated
from ----
• Model A: the number of library units.
• Model B: the number of visible and imported pro-
gram units declared.
• Model C: the number of visible, imported, and hid-
den program units declared.
• Model D: the number of types and objects declared.
At Stage A in Figure 2, the number of library units de-
fined serves as an early indicator of system size. The size
model at this stage is a coefficient multiplied by the num-
ber of user defined library units in the system. Thus, the
model for estimating source lines of code is:
SizeA = al * (#of library units) +
Stage B is further into the design process and more is
known about the details of the library units that were the
basis for the estimation at Stage A. So, at Stage B, the
number of visible program units declared and program
units imported from other subsystems by library units
through the context coupling mechanism are the parame-
ters of the size model. The rationale is that the number of
visible program units is a proxy for the functionality of a
package. Statistically we expect that more program units
will translate into a need for more lines of Ada code. The
rationale for the number of imported program units is
that they are being imported because they are needed, so
they must enter into the code of the receiving library unit.
So, if there are more imports, statistically we expect that
there will be more lines of code required in the library
unit. Thus, the model appears as:
SizeB = al * (# of visible program units)
+ a2 * (# of imported program units) +
At Stage C, the size estimation model depends on the
same information as Stage B, but with the inclusion of
the number of hidden program units. Again, hidden pro-
gram units perform local processing needed to support
the visible program units. The model is of the form:
SizeC = al * (# of visible program units)
+ a2 * (# of imported program units)
+ a3 * (# of hidden program units) +
As the design progresses, more detailed declarations
become available. The size estimation model at stage D
uses the number of declarations of types and objects as
the basis for its estimate of system size. This model ap-
pears as:
SizeD = al * (# of type/subtype declarations
+ #object declarations) +
5. Empirical Study
To estimate the parameters of the models, we analyzed a
collection of 21 Ada subsystems, from four projects de-
veloped at the Software Engineering Laboratory of the
National Aeronautics and Space Administration/Goddard
Space Flight Center [22]. The analysis was restricted to
subsystems containing more than one Ada library unit,
and consisting of at least 1,000 source lines of code. The
subsystems in our data set ranged in size from 2,000 to
27,000 source lines of code, excluding comment and
blank lines.
A locally developed Ada source analyzer program was
used [23], although other static analysis tools could yield
the same data. We extracted counts of source lines of
code; library units; visible, imported, and hidden pro-
gram units; and type and object declarations from the
delivered product. These counts were used in regression
analyses to develop models of delivered product source
lines of code.
Because the explanatory variables were taken from
completed project code data, we cannot make claims as
to whether all of the entities (library units, visible pro-
gram units declared and imported, hidden program units,
and declarations) were defined at stages A, B, C, and D
in strict adherence to the process model. For example,
while the model for Stage B depends on the number of
visible program units, and the process model calls for
defining the visible program units at that stage in the
process, it may be that some number of additional visible
program units were added very late in the process (e.g.,
due to a requirements change). However, the process
model does provide for successive elaboration of the
design and code in a seamless way with Ada as both a
design and implementation language. For example, one
of the authors (WA) was technical manager of one pro-
ject using this process model and had over 35,000 lines
of compiled Ada at the time of Critical Design Review.
That is, the compiled Ada was essentially the design
structure of the system, and, because it was in Ada, it
was amenable to automated analysis. If a size reestima-
tion model like this is used in practice, the model could
be calibrated and validated on an ongoing basis during
projects, so that the model is based on the actual number
of visible program units defined at Stage B.
6. Statistical Results
Size estimates can be made throughout the design phase
based on information with increasing detail. Because of
the additional information, we would expect these esti-
mates to be more accurate as the project moves into the
later design phases. In terms of the statistical analyses, a
greater fraction of the variation in lines of code (as
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Models for Improving Software System Size Estimates during Development
Copyright © 2010 SciRes JSEA
measured by the coefficient of determination of a regres-
sion analysis) would be explained as the design phase
progresses. In this section we present the results of re-
gression analyses of size estimation models.
As discussed previously, these models progress through
greater levels of information availability as the design
progresses, and they can be used to update the size esti-
mates for the purposes of project management. Regres-
sion analysis was used to build the models, with the ex-
pected outcome that the size estimates will become more
accurate as more design information becomes available.
The regressions for all four models were linear in both
the source lines of code and the explanatory variables. A
zero intercept term was assumed since zero values for the
explanatory variables used to explain the source lines of
code would necessarily imply that no lines of code would
be generated. Unpublished results of regression analyses
for models with the intercept terms resulted in intercept
estimates that were not significantly different from zero,
a conclusion also reached by Antoniol [1].
The first column of Table 1 shows the regression re-
sults for Model A. These results can be translated into the
equation, SizeA = 303.8 * (# of library units). The corre-
sponding predicted vs. actual plot is given in Figure 3.
The adjusted R2 is 0.47 (Note 1) and the coefficient for
the number of library units is highly significant as meas-
ured by the standard error associated with the coefficient
estimate. Note that the coefficient estimate indicates that
about 304 source lines of code will be generated for each
library unit that is defined early in the design phase.
However, the plot of Figure 3 shows a few observations
for which the predicted vs. actual values are strongly
Table 1. Linear regression results for source lines of code
Variable Model
Visible Program Units 48.0 36.8
(6.9) (6.6)
Imported Program Units 3.0 2.8
(0.4) (0.4)
Hidden Program Units 71.7
Types and Objects 22.2
R2 .47 .77 .87 .89
aStandard error of associated coefficient. All coefficient estimates are
significant to within the 1% level of significance.
Actual size*
*Size measured as non-comment source lines of code
Figure 3. Model A for system size: predicted vs. actual
Models for Improving Software System Size Estimates during Development7
Actual size*
*Size measured as non-comment source lines of code
Figure 4. Model B for system size: predicted vs. actual
Actual size*
*Size measured as non-comment source lines of code
Copyright © 2010 SciRes JSEA
Models for Improving Software System Size Estimates during Development
Copyright © 2010 SciRes JSEA
Figure 5. Model C for system size: predicted vs. actual
Actual size*
*Size measured as non-comment source lines of code
Figure 6. Model D for system size: predicted vs. actual
Model B focuses on the program units contained in the
library units. More specifically, only those program units
are considered that are visible, and hence can be exported
or imported. The identification of such program units is
expected to be the next step after the library units have
been declared. Table 1 shows the regression results and
Figure 4 shows the plot for Model B. This model is a
substantial improvement over Model A.
The regression explains 77% of the variation in lines
of code. Each visible program unit declaration contrib-
utes about 48 source lines of code, while an imported
program unit declaration accounts for about 3 source
lines of code. The coefficient estimates are again highly
significant. From Figure 4, we see that Model B leads to
a significantly better fit between actual and predicted
values. The outliers apparent in Figure 3 are pulled closer
to the 45-degree line along which predicted values would
exactly equal the actual values.
Model C is an enhancement to Model B whereby the
number of hidden program units is added to the analysis.
This model represents the next logical step in the devel-
opment of the design. Program units, declared and hence
hidden in the bodies of packages and subprograms, are
identified after the overall architecture of the system is
established through the identification of visible and im-
ported program units.
Table 1 shows the regression results and Figure 5
shows the plot for Model C, which explains approxi-
mately 87% of the variation in source lines of code. Each
visible program unit declared contributes about 37 source
lines of code, each hidden program unit contributes about
72 source lines of code, and each imported program unit
contributes about 3 lines of code. These coefficient esti-
mates are all highly significant.
The fact that hidden program units contribute more
source lines of code than the visible program units indi-
cates that many of the implementation details of the visi-
ble units are postponed until the implementation of the
hidden units. The visible program units essentially make
calls to the hidden program units for needed functionality.
The points plotted in Figure 5 hug the 45-degree line a
bit tighter than in Model B.
Finally, Model D utilizes information about all types
and objects in the system, whether visible or hidden. This
information might be available only after the design
process was substantially complete, and in some cases
after implementation had been partly accomplished.
Table 1 shows that the Model D explains about 89% of
the variation of source lines of code. Each type or object
Models for Improving Software System Size Estimates during Development9
accounts for about 22 source lines of code, and the coef-
ficient estimate is highly significant. Figure 6 shows that
the predicted values of source lines of code are close to
the actuals.
7. Limitations of Analyses
The analyses discussed above would be expected to have
high levels of predictability for projects in an environ-
ment similar to the one for which the empirical analyses
were conducted. However, in a different environment,
the use of alternative development methodologies (e.g.,
web-based applications, prototyping and Commercial-
off-the-Shelf (COTS) integration), the application of dif-
ferent quality assurance criteria, and variations in the
application domain might have an impact on these esti-
mates. For example, a quality assurance criterion limiting
the number of lines of code in a library unit would affect
the results of any empirical analysis using library units as
an explanatory variable. Similarly, two different design
methodologies could lead to different decompositions of
the design into library units and program units.
It is therefore recommended that a software develop-
ment organization use these results as evidence that it is
possible to build a family of models for the successive
re-estimation of software size. The key to building useful
models is to assess the development process being used,
and then identify entities that are defined at successive
stages in the process. Collect data on ongoing projects,
recording the counts of these entities. With data from
multiple projects that use the same process, an organiza-
tion can then perform its own empirical analyses to de-
termine the values of the coefficients for the models,
guided by the approach used here. Once the family of
models is then validated by use on additional projects,
the models will become more valuable in estimating
software size at various stages during a development
8. Summary and Future Directions
We have built a family of models for estimating software
size based on successively available design information.
The models demonstrate that the estimates can improve
as more design information becomes available. The
analyses were conducted at the subsystem level. Another
possibility is to develop modules using library units as
the unit of observation. The larger number of empirical
observations at the library unit level would permit the
exploration of a greater variety of explanatory variables.
If desired, the library unit estimates could then be rolled
up to get size estimates for subsystem or project levels.
As we have stressed, rather than using the model coef-
ficients established here, a software development orga-
nization may use the modeling approach here but con-
duct its own empirical analyses to assure applicability to
its unique environment. The resulting coefficient esti-
mates could be included in handbooks for managers to
use in refining their size estimates. With increasingly
more accurate size estimates during a project, there is
improved manageability, thus reducing the chances of
cost and schedule variances.
9. Acknowledgements
We acknowledge the U. S. Air Force Research Laborato-
ries and the MITRE Corporation for their support of the
original analysis.
Note 1. The measure of adjusted R2 used here is de-
fined as recommended by Kvalseth for models without
an intercept term [24]. That is, for a sample of n observa-
tions and a model with k parameters, if pi denotes the
fitted value of yi, and m the sample arithmetic mean of
the yi, then R2 = 1 - a * (pi - yi)2/ (yi - m)2, where a =
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