CSC 395 –Software Engineering

Lecture 33:

Planning & Estimation -or-

How Much for the Program using Windows

Estimating Duration & Cost

Accurate estimations crucial Companies need to make $$$ Customers want reasonable & reliable prices

Estimates depends on many, many variables Many are unpredictable (e.g., human factors) Programmers quit or “get hit by bus” Get freak Oct. snow storm that shuts down city Programmer pairs differ in effectiveness &

productivity (up to 28x) General models impossible to create

Planning and Process

Estimates improve as more details known Anyone confused by this point, give up

Planning and Process

Properly engineered, estimate stays static as range shrinks

Start of requirements, estimate is $1 million Likely actual cost is in $0.25M - $4M range

After requirements, estimate stays $1 million Actual cost range shrunk to $0.5M - $2M

After analysis, estimate still $1 million But cost range reduced to $0.67M - $1.5M This is also when contract is finalized!

Estimating Product’s End Size

Several metrics commonly relied upon Use measurable quantities for simple calculations Computed early in life cycle for use writing contracts

Includes “fudge factors” that can be tweaked Factor unique to each group and situation Tracking allows more precise estimates over time

FFP for medium-sized data processing projects Function points used in many places

FFP Calculation

Counts files (Fi), flows (Fl), & processes (Pr) Size (S) estimated as

S = Fi + Fl + Pr Cost (C) estimated with S & efficiency factor b

C = b S

Function Points Metric

Expressed in units of “function points” Calculated using several inputs:

Number of inputs (Inp) Number of outputs (Out) Number of Inquiries (Inq) Count of master files (Maf) Interfaces in system (Inf)

FP = 4 • Inp + 5 • Out + 4 • Inq + 10 • Maf + 7 • Inf

Complex FP Calculation

1. Classify each of the individual components (e.g., each Inp, Out, Inq, Maf, Inf) Assign appropriate number of function points Sum is UFP (unadjusted function points)

Complex FP Calculation

2. Compute DI (degree of influence) Sum of 14 factors ratings Each rating will be integer between

0 (“not present”) & 5 (“strong influence throughout”)

Complex FP Calculation

3. Compute TCF (total complexity factor)

TCF = 0.65 + 0.01 DI

4. FP (number of function points) is:

FP = UFP TCF

Analysis of Metrics

Amazingly, FP often calculated correctly Moreover, cost-per function point meaningful!

Maintenance can cause inaccuracies Major changes possible without changing Fi, Fl,

Pr, Inp, Out, Inq, Maf, Inf

Cost Estimation Techniques

Expert judgment by analogy Compare target product to completed products Provides a WASG (wild *** scientific guess) Studies show large groups eerily accurate

Bottom-up approach Split into smaller components (e.g., classes) Estimate costs for each component

Algorithmic cost estimation models Most commonly used approach

COCOMO

Set of estimation models developed in 1981 Updated in 1995 for modern development tools Both contain 3 models differing in complexity

Original design consists of three models: Macro-estimation model for whole product Intermediate COCOMO Micro-estimation model examining product details

Intermediate COCOMO

Estimate product length in KDSI Estimate of 1000s of source instructions

Select development mode

Development mode determines coefficients used

Size Innovation Deadline Environment

Organic Small Little Not tight Stable

Semi-detached

Medium Medium Medium Medium

Embedded Large Greater Tight Complex interfaces

a b

Organic 3.2 1.05

Semi-detached 3.0 1.12

Embedded 2.8 1.20

Intermediate COCOMO

Compute development (nominal) effort Effort computed as person-months

Effort = a (KDSI)b

Compute C (effort adjustment factor) Equals the product of up to 15 cost-drivers

Development time (in person-months) is:

Effort C

COCOMO Cost Drivers

Using COCOMO

Revision (COCOMO II) calculated similarly But much, much more complex

Estimated time then used to compute: Development costs & schedules Phase and activity distributions Maintenance costs after delivery

Ultimately based upon initial size estimate End result only as good as initial estimate

For Next Lecture

Consider maintenance of code Ultimately where the real money is made or lost Where majority of time in project is spent But least interesting for software engineering Much determined by decisions in development