Should be plagiarism free. Turnitin is used to check for plagiarism. English should be top quality.Whatever resources you use, make sure to reference them and include intext citations. Atleast 3 single spaced pages and ppt need on this. Templates are attached alongwith sample paper (DBO06.pdf) so you understand it well.Develop an engineering economy study of a problem of interest to you. The problem may be from work or from your other interestsUse at least two outside references (in addition to the texts and your notes) in developing your project. One reference should be from a refereed source (conference or journal paper).Use at least two techniques from the class. Example techniques:Cash flow analysis (note that all cash flow analysis techniques are equivalent and count as only one method)Design optimizationDemand curvesDepreciation and taxesCost estimationBreakeven or sensitivity analysisProbabilistic risk analysisWrite and submit a technical paper documenting your results using the attached template – Paper Template.docDevelop and submit a Powerpoint presentation on your project using the attached template – Presentation Template.pptESTIMATING DIRECT RETURN ON INVESTMENT OF
James B. Dabney
Systems Engineering Program
University of Houston – Clear Lake
Houston, Texas
We define direct return on investment (ROI) as the
ratio of reduction in development cost arising from early
issue detection by independent verification and validation
(IV&V) to the cost of IV&V. This paper describes a
methodology to compute direct ROI for projects that
don’t maintain detailed cost-to-fix records. The method is
used in a case study in which IV&V was applied to a
mission-critical NASA software project. For this project,
direct IV&V ROI was 11.8, demonstrating that IV&V
was cost effective.
Verification and validation, return on investment,
defect leakage, cost modeling.
A standard management measure for determining the
worth of an investment is return on investment (ROI),
also known as benefit/cost ratio [1]. For software
independent verification and validation (IV&V) [2], [3],
we believe that there are many benefits and therefore
many components of ROI. For example, benefits include
reduced development cost, increased confidence in the
final product, improved quality, reduced risk, and
improved safety. Unfortunately, all of these benefits are
difficult to measure. Consequently, IV&V ROI is
inherently difficult to calculate. Among these benefits,
reduced development cost is the least difficult to quantify.
We refer to ROI based solely on reduced development
cost as direct ROI. A previous paper [4] presented a
methodology to compute direct ROI for projects that
maintain detailed records of the cost to fix each
discovered defect. This paper extends the methodology to
the more common (in our experience) situation in which
detailed cost-to-fix records are not maintained. The
method exploits the COCOMO-II [5] model, calibrated to
actual project results, to estimate cost-to-fix. The method
is illustrated using a case study from a mission-critical
Gary Barber and Don Ohi
L3 Communications Titan Group
NASA IV&V Facility
Fairmont, West Virginia
{gary.barber, don.ohi}
NASA software project. Additionally, the sensitivities of
IV&V ROI to variations in IV&V scheduling and
developer defect removal efficiency are studied.
We define direct ROI as the ratio (Cx-Ci)/CIVV, where
Cx is the project cost without IV&V, Ci is the project cost
with IV&V, and therefore the difference δCr = (Cx-Ci) is
reduction in development cost due to early issue
identification by the IV&V team. CIVV is the cost of the
IV&V effort. Cost can be expressed in any consistent
unit. Typically, equivalent person-months (EPM) or
equivalent person-hours (EPH) are convenient.
The denominator of the ROI ratio is usually fairly
easy to obtain from IV&V project records. The
numerator, on the other hand, can only be estimated.
While it is possible to determine the actual development
cost, the cost savings due to early issue discovery and
resolution cannot be known with certainty since it is not
possible to know when (or even if) each issue identified
by IV&V would have been found had IV&V not been
used. Therefore, the central task in computing direct ROI
is to devise a credible estimate of the cost savings.
The basis of our approach to computing δCr is to
compute rework cost for the actual with-IV&V data and
to conservatively estimate the rework cost without IV&V
by assuming that issues identified by IV&V would have
been discovered later in the project by the developer with
the same probability distribution as other issues
discovered by the developer. In a previous paper [4], we
considered projects for which actual rework costs are
documented. For many projects, rework costs must be
estimated using a tool such as COCOMO-II [5], [6].
The outline of this paper is as follows. First, we will
summarize previous investigations related to IV&V ROI
(presented in more detail in [4]). Next, we will review the
direct ROI computation methodology [4]. Then, we will
describe the modification to the direct ROI methodology
using COCOMO-II formulas. We will then present results
of applying this methodology to a NASA mission-critical
software project. Next, we will discuss the sensitivity of
direct ROI to variations in IV&V scheduling and
developer defect removal efficiency. Finally, we will
summarize results and recommend future work.
Although there has been extensive investigation over
the past several years into the ROI of software process
improvement [7], a rigorous methodology for measuring
ROI of the various software assurance disciplines was not
established prior to [4]. However, several earlier studies
did shed light on IV&V ROI and provided valuable
insight. The previous studies are discussed in some detail
in [4]. They are summarized briefly here.
Arthur [2], [8] determined via a controlled experiment
using two independent development teams, one of which
employed IV&V, that IV&V has the potential to
significantly increase the cost effectiveness of defect
identification and removal. However, an earlier study at
the NASA Software Engineering Laboratory [9]
demonstrated that IV&V is not guaranteed to be cost
effective, supporting the need to compute IV&V ROI so
that IV&V resources may be used to greatest advantage.
Rogers and McCaugherty [10] devised a rough estimate
of IV&V ROI using defect removal costs from Jones [11]
and actual error counts. Finally, Eickelmann [12] derived
upper bounds for IV&V ROI based on developer
Capability Maturity Model (CMM) level and IV&V
budget. Together, the literature bearing on IV&V ROI
supports three conclusions:

The efficacy, and therefore the ROI, of IV&V can
vary considerably.

Employed properly, IV&V can be extremely
beneficial, resulting in higher software quality and
reduced cost to remove defects.

No model had been proposed prior to [4] that used
actual project cost and error data accumulated in
active NASA projects to determine IV&V ROI.
Direct ROI Methodology
This section summarizes the direct ROI methodology
presented in detail in [4]. The fundamental problem in
computing IV&V ROI is to estimate the project cost
without IV&V, given the project cost with IV&V and
suitable project databases. The basis of computing the
without-IV&V cost is the escalation of cost to fix an error
as the project proceeds. Coupled with the probability
distribution of developer discovery of the defects and
actual cost-to-fix, without-IV&V cost-to-fix can be
Relative Cost-to-Fix Ratios
It is well-known that the cost to fix a software defect
increases as the project proceeds. This fact has been
recognized for many years [13] and is confirmed by
recent data [14], [15], [16], [17]. This cost escalation is
often used as a justification for software engineering
process improvements and software quality assurance
activities [18], [19]. The escalation of cost to fix a defect
has been reported by numerous sources. Based on
analysis of cost-to-fix escalation data from [13] – [20], a
normalized cost-to-fix escalation table (Table 1) was
developed. Details of the derivation are presented in [4].
Table 1: Relative cost-to-fix ratios
Phase issue found
The rows in Table 1 indicate the cost-to-fix escalation
for each type of issue, assuming that all defects are
introduced in the development phase corresponding to
issue type. Therefore, the model predicts that the cost to
fix a design issue discovered in the integration lifecycle
phase is 26 times the cost to fix the same issue had it been
discovered in the design phase.
Defect Leakage Probabilities
The defect leakage model is based on the assumption
that the developer would discover the same percentage of
defects existing at the beginning of a particular
development phase in the absence of IV&V as they
discovered with IV&V present. That is, the probability of
developer discovery of a particular defect without IV&V
present is the same as the probability of discovery
actually experienced with IV&V present. Thus, the
probability ptf of the developer discovering a defect of
type t in phase f is
ptf = tf
N tf
where Dtf is the actual number of defects of type t found
by the developer in phase f and N tf is the number of
defects of type t known to exist at the beginning of phase
f. That is, N tf is the number of defects of type t actually
found in phase f or a later phase by either the developer or
IV&V. Thus, only known defects are counted because we
have no credible estimate of unknown defects.
Next, in order to simplify the computations, a total
probability Ptif is required for each defect type t, phase
found by IV&V i, and development phase f. Here, Ptif is
the probability that the developer would find a particular
defect, actually found in phase i by IV&V, in subsequent
phase f, computed by accounting for defect removal in
previous phases:
f −1

Ptif = ptf  1 − ∑ Ptiφ 

φ =R
where R indicates the requirements phase and f − 1
indicates the phase before phase f.
Computing ROI
On projects for which the software developer tracks
the cost to fix each defect corrected, it is necessary only to
estimate the cost to fix each error identified by IV&V,
had IV&V not been present. This estimate is simply the
expected value of cost to fix each error given the cost
escalation factors of Table 1 and probabilities Ptif for the
subsequent phases.
To illustrate the computation, assume that IV&V
discovered a requirements issue in the design lifecycle
phase. Using the cost-to-fix ratios of Table 1, the
estimated cost to fix the error had IV&V not been present
C fD = (10PRDC + 50PRDT + 130PRDI + 368PRDO )/5
c x = ci C fD
where ci is the actual recorded cost to fix the IV&Vdiscovered issue and subscripts R, D, C, T, I, O
correspond to phases (and defect types) requirements,
design, code, test, integration, operations, respectively.
The return on investment is the ratio
∑c − ∑c
where CIVV is the total IV&V cost.
COCOMO-Based ROI Computation
For many (in our experience, most) projects, the
developer does not track the cost to fix each discovered
defect. For these cases, it is necessary to estimate the costto-fix using a software cost model. This section describes
the modification to the direct ROI methodology to
estimate cost-to-fix using COCOMO-II [5] software cost
estimating formulas.
COCOMO-II Calibration
COCOMO-II is a learning curve model which
estimates development cost (equivalent person-months
(EPM)) by
CT = A S E
where S is program size in source lines of code (SLOC)
and A and E are system-dependent constants. Exponent E
depends on five development project characteristics. The
value of E for typical NASA projects is approximately
1.1. Coefficient A depends on seventeen key process areas
which include management characteristics and software
engineering practices.
To account for rework, COCOMO-II uses a term,
BRAK, which is an estimate of the SLOC equivalent of
the rework effort. The actual development cost (in EP
months) and the delivered SLOC is normally available,
and it is possible to produce fairly accurate estimates of
BRAK from issue logs and databases, as discussed in the
next section. Given the delivered product size (new SLOC
plus effective reused SLOC (ESLOC [5]), the effective
project size is
With exponent E estimated from project characteristics,
coefficient A can be computed directly from Equation (2),
thus accurately calibrating the cost model to the project
results. Using this data to calibrate COCOMO-II to the
project, we can estimate the without-IV&V BRAK and
then compute a total without-IV&V development cost.
BRAK Estimation
Function points provide a means to associate the size
of a software product with its functionality [21], [22]. A
single unadjusted function point denotes a functional
behavior of a software system. Function points are
attractive because it is less difficult, early in a project, to
estimate functional characteristics than to estimate
directly SLOC. The function point methodology starts
with characterization of the functional characteristics and
classifying each by function point type. Next, each
individual function point is multiplied by a scale factor
kw that depends on type of function point and
complexity. This product is adjusted for development
process characteristics, resulting in adjusted function
points. Finally, adjusted function points can be multiplied
by a language scale factor kL that converts adjusted
function points to SLOC.
The function point methodology can be used to
estimate BRAK SLOC. To compute BRAK, the function
points for each issue are assessed first. This is
accomplished by reviewing each issue report and
tabulating the number and complexity of each type of
function point. Then, we compute the BRAK associated
with each type of function point for each issue i as
BRAK i = FPi kw k L ks
where FPi is the number of function points of a particular
type and complexity, kw is the scale factor from [21] that
depends on the type of function point and complexity, kL
is the language scale factor [5] that relates SLOC to FP
for a particular programming language, and ks is a scale
factor that accounts for reduction in effort resulting from
early issue detection. The basis for ks is a requirements
issue discovered in the integration phase, requiring
complete rework for the particular requirement. Thus, for
a requirements issue discovered in the integration phase,
ks is 1.0. Values of ks computed directly from Table 1 are
listed in Table 2.
Table 2: SLOC Reduction Factors ks
Phase issue found
discover the same issue in the same development phase.
Table 4 shows the defect adjusted function points for the
developer. Using the methodology described in Section 4,
ROI was computed to be 11.8.
Table 3: IV&V-discovered issue
adjusted function points
Next, we must estimate the without-IV&V BRAK.
For each function point type for each IV&V issue, the
without-IV&V BRAK is computed from
where all terms are as previously defined except that ks is
replaced by ksD which is an average of ks for the
remaining phases weighted by the percentage of
developer-discovered issues per phase, in a manner
identical to that used to compute cx . Thus, ks is projectindependent and ksD is project-dependent. Using the
without-IV&V BRAK, effective without-IV&V project
size is computed using (3) and estimated without-IV&V
development effort is computed using Equation (2) (and
the previously determined value of A). Finally, ROI is
computed as the ratio of development cost reduction due
to IV&V to the cost of IV&V,
C x − Ci
Note that for the with-IV&V case, we compute BRAK
using both developer- and IV&V- discovered issues. For
the without-IV&V case, we recompute BRAK only for
IV&V-discovered issues. BRAK for developerdiscovered issues remains the same and thus the
increment to BRAK is due exclusively to IV&Vdiscovered issues.
Case Study
The COCOMO variant of the direct ROI methodology
was applied to a moderately-sized software development
project for a mission-critical, safety-critical near-real-time
software system. The project entailed approximately
78,000 source lines of code (SLOC), including 30,000
lines of reused code. Total development effort (including
rework) was approximately 381 EPM and the IV&V
effort was approximately 53 EPM. This project did not
track the cost to fix each issue, so it was necessary to use
the COMOMO-II variant of the direct ROI model.
Table 3 lists the adjusted function points of all issues
identified uniquely by IV&V. That is, an issue was
credited to IV&V only if the developer did not also
Table 4: Developer-discovered issue
adjusted function points
Phase found
where CT is the without-IV&V development cost
(estimated), Ci is the actual development cost experienced
using IV&V, and CIVV is the cost of the IV&V effort.
BRAK = FP kw kLksD
Phase Found
Sensitivity Analysis
In [4] the sensitivity of the direct ROI methodology to
variations in cost-to-fix escalation was considered.
Another important factor in IV&V ROI is the timing of
issue discovery. The timing has two implications. The
first is a direct consequence of the cost-to-fix escalation.
The potential ROI impact of a particular issue is clearly
greater the earlier in the lifecycle the issue is discovered.
However, the timing of issue discovery (by IV&V and the
developer) also affects the developer defect discovery
probability distributions. To understand direct ROI
consequences of variations in defect discovery phasing, a
second sensitivity analysis, discussed next, was
performed. The sensitivity analysis considered variations
in IV&V defect detection timing and developer defect
discovery timing.
Variations in IV&V Defect Detection
It is apparent that early lifecycle IV&V activities have
the potential to produce higher direct ROI than late
lifecycle activities. This component of the sensitivity
study measured this effect by varying IV&V issue
discovery rates across the lifecycle, based on the
reasoning that IV&V issue discovery rates will correlate
with IV&V effort distribution. To test the sensitivity of
ROI to the placement of IV&V effort, defect discovery
profiles were generated for four cases for the same
hypothetical project:
1. FULL: IV&V applied over the entire lifecycle
2. EARLY: IV&V applied only to the earlier lifecycle
3. LATE: IV&V applied only to the later portion of the
4. NO DESIGN: No developer or IV&V defects
discovered during the design phase (for the case
where the developer skips the design phase)
In order to calculate the ROI for IV&V in each of
these cases, defect discovery profiles were needed for
both the developer and IV&V. Table 5 lists project
characteristics that were held constant for all projects.
Table 5: Simulated project characteristics
Project Characteristics
Developer Effort
SLOC Conversion Factor, KL (C
Average Adjusted Function Points
per Defect
Defects Introduced per Type1
100,000 SLOC
1,000 EPM
5.5 AFP/defect
156 defects
194 defects
274 defects
200 defects
120 defects
Defect discovery per phase for both developers and
IV&V was simulated by assuming a constant Defect
Removal Efficiency (DRE) across phases for each issue
type. DRE represents the fraction of issues present that
were detected by either the developer or IV&V. The
details of the simulation are provided in the sensitivity
spreadsheet Table 6 lists the results from the sensitivity
study of ROI to IV&V issue detection distribution.
Table 6: ROI Sensitivity to IV&V Issue Detection
Test Case
The Early case resulted in the highest ROI, as
expected. Defects detected early by IV&V would have the
potential to leak the farthest had IV&V not been present.
Defect data based on [23]
This case also resulted in the highest number of defects
leaking to the operations phase.
A surprising result was that the Late IV&V resulted in
a higher ROI than Full lifecycle IV&V. The Late case
provided better protection from leakage to operations than
the early case. The reason for higher ROI than full
lifecycle IV&V is the leakage of IV&V discovered
defects for the case without IV&V all occurred in the
steeper portion of the escalation curves. The no design
phase case had a combination of the effects of the early
and late IV&V cases.
Variations in Developer Defect Detection
To examine the impact of developer defect detection
on the results of the direct ROI model, we used the Full
lifecycle case from the previous experiment on variations
in IV&V defect detection. For this experiment, we held
the IV&V defect detection profile constant instead of
allowing it to vary as defects present per-phase times the
DRE. This approach was used to understand the effects on
direct ROI of variations of developer defect profiles using
identical IV&V results. The total number of defects
detected by the developer was held constant across all of
the cases to examine the effects of phasing independent of
the number of defects.
Since the full lifecycle case from the experiment in
section 6.1 was based on defect detection using a constant
DRE, we consider that representative of a full lifecycle
focus by the developer on defect removal and call it DEV
FULL here. For the DEV EARLY case, the developers
detect ~ 90 % of their portion of the defects in phase. For
the DEV LATE case, the bulk of the defects discovered
by the developer were in the test, integration, and
operations phases.
Table 7: ROI sensitivity to developer issue detection
Test Case
Table 7 shows that the phasing of developer issues
does have an impact on the direct ROI results. The impact
is due to the dependence of direct ROI on developer
defect discovery probabilities. That the DEV LATE case
results in higher IV&V ROI is easy to understand –
delaying the developer defect discovery activities
increases the probability of finding defects later in the
lifecycle. The DEV EARLY case increases direct ROI
because issues discovered in-phase by the developer don’t
contribute to the probability computations.
Conclusions and Future Work
The direct ROI methodology provides a
straightforward means to compute direct ROI for IV&V
projects. This paper has presented a variant of the direct
ROI methodology that uses the COCOMO-II formulas to
estimate rework costs. The use of the methodology was
demonstrated using a case study and produced results
similar to those achieved previously for a project for
which detailed cost-to-fix records were maintained.
The sensitivity analysis of this paper demonstrated
that the direct ROI model is moderately sensitive to
variations in the timing (with respect to the development
lifecycle) of IV&V and developer defect detection
and Applications Conference, IEEE Computer Society
Press, 1984.
[10] R. A. Rogers, D. B. McCaugherty, and F. Martin, A
case study on IV&V Return on Investment, Proceedings
of the NDIA 3rd Annual Systems Engineering and
Supportability Conference, 2000.
[11] C. Jones, Software Quality:Analysis and Guidelines
for Success, International Thomson Computer Press,
Boston, MA, 1997.
This research was supported by the NASA
Independent Verification and Validation Center and L-3
Titan Group.
[12] N. Eickelmann, A. Anant, J. Baik, and W. Harrison,
Developing Risk-Based Financial Analysis Tools and
Techniques to Aid IV&V Decision Making, NASA
Contract S-54493-G Technical Report, NASA IV&V
Facility, Fairmont, WV, 2001.
[13] B. W. Boehm, Software Engineering Economics,
Prentice-Hall, Englewood Cliffs, NJ, 1981.
[1] W. G.Sullivan, J. A. Bontadelli, and E. M. Wicks,
Engineering Economy, 12th Ed., Prentice Hall, Upper
Saddle River, New Jersey, 2002.
[14] J. Rothman, What does it cost you to fix a defect?
And why should you care? Rothman Consulting Group,
Inc.,, October, 2000.
[2] J. D. Arthur, W. Frakes, S. Gupta, M. Cannon, M.
K.Groener, Z. Khan, A Study and Project-Based
Evaluation of the Software Engineering Evaluation
System SEES), Technical Report, Department of
Computer Science, Virginia Polytechnic Institute and
State University, Blacksbug, Va., 1997.
[15] J. Rothman, What does it cost to fix a defect?, February, 2002.
[3] S. Easterbrook, The role of independent V&V in
upstream software development processes, 2nd World
Conference on Integrated Design and Process
Technology, Austin, Texas, 1996.
[4] J. B. Dabney, G. Barber, and D. Ohi, “Estimating
Direct Return on Investment of Independent Verification
and Validation,” 8th IASTED Conference on Software
Massachusetts, November, 2004.
[5] B. Boehm, C. Abts, A. W. Brown, S. Chulani, B.
Clark, E. Horowitz, R. Madachy, D. Reifer, and B.
Steece, Software Cost Estimation with COCOMO II,
Prentice Hall, Upper Saddle River, NJ, 2000.
[6] B. Boehm, B. Clark, E. Horowitz, and C. Westland,
The COCOMO 2.0 Software Cost Estimation Model,
University of Southern California, 1995.
[7] J. Herbsleb, A. Carleton, J. Rozum, J. Siegel, and D.
Zubrow, Benefits of CMM-based software process
improvement: Initial results, Technical Report CMU/SEI94-TR-013, Software Engineering Institute, Pittsburg,
Pennsylvania, 1994.
[8] J. D. Arthur, M. K. Groener, K. J. Hayhurst, and C. M.
Holloway, Evaluating the effectiveness of independent
verification and validation, IEEE Computer, October,
1999, 79 – 83.
[9] G . Page, F. E. McGarry, and D. N. Card, A practical
experience with independent verification and validation,
Proceedings of the 8th International Computer Software
[16] T. McGibbon, A business case for software process
improvement, Data & Analysis Center for Software, Air
Force Research Laboratory – Information Directorate,
Rome, NY.
[17] Case study: Finding defects earlier yields enormous
savings, Cigital,, 2003.
[18] G. M. Schneider, J. Martin, and W. T. Tsai, An
experimental study of fault detection in user requirements
documents, ACM Transactions on Software Engineering
and Methodolgy, 1( 2), April, 1992, 188 – 204.
[19] From Software Quality Control to
Assurance, Mortice Kern Systems Inc., 2001.
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Dexterity Software,, 2001.
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University of Houston – Clear Lake
SENG 5230
Title of Paper
Your Name
The abstract should be one paragraph that summarizes the entire paper.
Introduce the topic and explain its significance. Describe the analysis
techniques used and key results.
1 Introduction
Briefly introduce the problem. For example, if the problem is a replacement analysis, explain what the
system does and why . Describe the present system and proposed alternatives. The introduction should
contain background information, but not a lot of detail.
You should select a topic which relates to the course material. You are free to choose something from your
job, a topic related to your thesis research, or a topic you identify from reviewing relevant literature. A
typical problem for this course is to determine whether adding a new capability is worthwhile, or choosing
among alternatives for solving a problem. For example, one student studied the alternatives of repairing or
replacing a small environmental chamber. The student developed cash flows for the two alternatives (which
required a modest amount of research) using in-house cost models and equivalent worth analysis. Other
students have considered developing a business such as a web-hosted business, homeland security
problems, and infrastructure proposals such as highway development in India, and water purification
alternatives in developing nations.
Conclude the introduction with a brief overview of the remaining sections.
2 Problem description
Explain the problem in detail. List assumptions you are making.
3 Analysis
Present your analysis. Include enough detail to allow the reader to follow what you’re doing. You might
find it helpful to include as figures tables copied from a spreadsheet.
You should use at least two techniques discussed in class and two external references. The techniques
should not be variations of the same technique. For example, you can’t count two different cash flow
analysis methods as different techniques, – they’re all different versions of the same thing. The techniques
discussed in this course include demand optimization, design optimization, cash flow analysis, cost
estimation, depreciation and taxes, and sensitivity analysis.
4 Results
Discuss the results of the analysis.
5 Summary and Conclusions
Summarize the problem, the analysis, and the results. State conclusions and suggest future work if
6 References
Provide a list of references, at least two in addition to your text and class notes. Each of the references must
be cited at least once in the text. The references should be listed in the order in which they are cited in the
report. The style of the citation depends on the context. If you are citing the authority of a reference, you
might say something like “For example, Jones [1] claims the moon is green cheese.” If you are mentioning
that several others have studied this problem, you might say “There have been other researchers that claim
the moon is made of rocks [2], [3].” Some examples for journal article, book, web page:
1. J. Jones, “Title of article,” Journal Name, Vol XX, No. yy, pp. nn – mm, Month, Year.
2. S. Smith, Title of Book, Publisher, City, State, year.
3. B. Brown, “Web page title,”,
Presentation Title
Your Name
SENG xxxx

Problem description
Summary and conclusions
• Background of the problem you’re solving
Problem description
• State problem
• Briefly describe analysis
• What did you learn or conclude
Summary and conclusions
• Summarize problem
• Summarize analysis
• Summarize results

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