# Presentation: 792696

## Disciplined Software Engineering Lecture #3

**Software Engineering Institute**
**Carnegie Mellon University**
**Pittsburgh, PA 15213**
**Sponsored by the U.S. Department of Defense**

## Lecture #3 Overview - Size  Estimating - 1

**Why estimate size?**
**Some estimating background**
**Size estimating principles**
**Estimating approaches**
**Estimating proxies**

## Metrics

**Measuring the output of a process is the first step to analyzing the process. **
**Automobile companies measure how many manufacturing hours go into producing a car. **
**As with any measurement, there are problems. You are trying to summarize a great deal of information into a single or small number of values. **
**How much does the high and low temperatures really tell you about the weather, or the barometric pressure, or the wind direction. **
**Looking at any one of these measures it is easy to find flaws, and short comings. **
**Should the temperature be recorded at ground level? or a top a central tower? in the shade or in the sunlight. **

## Lines-of-code

**What unit of measure should be used to measure software output?**
**The traditional and much maligned metric is called lines-of-code or LOC. **
**A summary of concerns with LOC:**
**Capers Jones notes:**
**1) should the code count include non-executable code, such as comments, or explanatory information. **
**2) how are differences between high-level and low-level languages represented. **
**3) Whether non-delivered code should be represented in a LOC count. **
**So, it appears that LOC counts may not be the best way to measure software development output. **

## Function Points

**If not LOC, then what??**
**Many other metrics have been developed. Most of these metrics have major flaws.**
**Albrecht developed Function Point Analysis (FPA) in the late 70’s **
**This method assumes that “the amount of function to be provided by the application (program) can be estimated from an itemization of the major components of data to be used or provided by it”.  **
** ****Albrecht believes that the complexity of a software project is determined by the functions of the software. **

## More on Function Points

**FPA breaks the functionality of a software project into five areas. **
**Then for each area, the number of simple, average, and complex functions is determined, followed by a technical complexity factor calculated from 14 software system characteristics. **
**These 14 characteristics are tied to Halstead’s Software Science Metrics which are tied to Putnam’s cost estimation models, which are based on Norden’s Rayleigh Curve model first published in the late 50’s based on computer hardware development. **

## So LOC is not so bad after all

**A big problem with Rayleigh Curves is that for early software development data, the curves appear to fit, but no one can explain why. **
**Beside that fact that function points have a shaky theoretical background, it is much harder to generate function points than LOC, therefore few use them. **
**Like the imperial measurement system, it is clearly inferior to the metric system, however, until people use the metric system, it will be of little relevance to most people. **
**Therefore, we will proceed with using line-of-code, being careful to minimize Jone’s issues**

## How long will the project take?

**Estimate of the size of a project**
**Lines of code**
**Function points**
**Person-months**
**Based on requirements and experience or mathematical model**

## Why Estimate Size?

**To make better plans**
**to better size the job**
**to divide the job into separable elements**
**To assist in tracking progress**
**can judge when job scope changes**
**can better measure the work**
**Value in this course**
**learn estimating methods **
**build estimating skills **

## Estimating Background

**Estimating models in other fields**
**large base of history**
**in wide use**
**generate detailed planning data**
**require a size estimate as input**
**Software size estimating experience**
**100% + errors are normal**
**few developers make estimates**
**fewer still use orderly methods**

## Size Estimating Principles - 1

**Estimating is an uncertain process.**
**no one knows how big the product will be**
**the earlier the estimate, the less is known**
**estimates can be biased by business and other pressures**
**Estimating is an intuitive learning process.**
**ability improves with experience and data**
**some people will be better at estimating than others**

## Estimating is a skill.
improvement will be gradual
you may never get very good

The objective, however, is to get consistent.
you will then understand the variability of your estimates
you seek an even balance between under and over estimates

**improvement will be gradual**
**you may never get very good**
**The objective, however, is to get consistent.**
**you will then understand the variability of your estimates**
**you seek an even balance between under and over estimates**

**Size Estimating Principles - 2**

## Slide 13

## Slide 14

## The principal advantages of using a defined estimating method are
you have known practices that you can work to improve
it provides a framework for gathering estimating data
by using consistent methods and historical data, your estimates will get more consistent

**you have known practices that you can work to improve**
**it provides a framework for gathering estimating data**
**by using consistent methods and historical data, your estimates will get more consistent**

**Size Estimating Principles - 3**

## The Size Estimating Framework

**The Size Estimating Framework**

## Estimating Approaches

**Fuzzy logic**
**Function points**
**Standard components**
**Delphi**

## Fuzzy Logic Size Estimating - 1

**Gather size data on previously developed programs**
**Subdivide these data into size categories:**
**very large, large, medium, small, very small**
**establish size ranges **
**include all existing and expected products**
**Subdivide each range into subcategories**

## Fuzzy Logic Size Estimating - 2

**Allocate the available data to the categories.**
**Establish subcategory size ranges.**
**When estimating a new program, judge which category and subcategory it most closely resembles.**

## A Fuzzy Logic Example - 1

**You have historical data on 5 programs as follows:**
**a file utility of 1,844 LOC**
**a file management program of 5,834 LOC**
**a personnel record keeping program of 6,845 LOC**
**a report generating package of 18,386 LOC**
**an inventory management program of 25,943 LOC**

## A Fuzzy Logic Example - 2

**You thus establish 5 size ranges, as follows**
**log(1844) = 3.266**
**log(25,943) = 4.414**
**the difference is 1.148**
**1/4th this difference is 0.287**
**the logs of the five ranges are thus spaced 0.287 apart**
**the limits or these ranges are at 0.1435 above and below the midpoint of each range**

## A Fuzzy Logic Example - 3

**The 5 size ranges are thus**
**very small - 1,325 to 2,566: file utility**
**small - 2,566 to 4,970: no members**
**medium - 4,970 to 9,626: file management and personnel record program**
**large - 9,626 to 18,641: report generator**
**very large - 18,641 to 36,104: inventory management**

## A Fuzzy Logic Example - 4

**Your new program has the following requirements**
**analyze marketing performance by product line**
**project the likely sales in each product category**
**allocate these sales to marketing regions and time periods**
**produce a monthly report of these projections and the actual results**

## A Fuzzy Logic Example - 5

**In comparing the new program to the historical data you make the following judgments**
**it is a substantially more complex application than either the file management or personnel programs**
**it is not as complex as the inventory management program**
**it appears to have significantly more function than the report package **
**You conclude that the new program is in the lower end of “very large,” or from 18 to 25 KLOC.**

## Fuzzy Logic - Summary

**To make a fuzzy logic estimate:**
**1 -  Divide the historical product size data into size ranges.**
**2 -  Compare the planned product with these prior products.**
**3 -  Based on this comparison, select the size that seems most appropriate for the new product.**

## Fuzzy Logic Size Estimating - Advantages

**Fuzzy logic estimating **
**is based on relevant historical data**
**is easy to use**
**requires no special tools or training**
**provides reasonably good estimates where new work is like prior experience**

## Fuzzy Logic Size Estimating - Disadvantages

**The disadvantages of fuzzy logic are**
**it requires a lot of data**
**the estimators must be familiar with the historically developed programs**
**it only provides a crude sizing**
**it is not useful for new program types**
**it is not useful for programs much larger or smaller than the historical data**

## Function Point Estimating - 1

**A function point is an arbitrary unit**
**based on application functions**
**inputs, outputs, files, inquiries  **
**scaled by simple, average, complex  **
**  **
**For job complexity:**
**adjust a further +/- 35% **

## Function Point Estimating - 2

**Procedure**
**determine numbers of each function type in the application**
**judge the scale and complexity of each function**
**calculate function point total**
**use historical data on development cost per function point to make the estimate**
**multiply function points times rate to get the estimate**

## A Function Point Example - 1

**Your new program has the following requirements**
**analyze marketing performance by product line**
**project the likely sales in each product category**
**allocate these sales to marketing regions and time periods**
**produce a monthly report of these projections and the actual results**

## A Function Point Example - 2

**You first estimate the numbers of raw function points as follows:**
**12 inputs: 12 x 4 = 48**
**7 outputs: 7 x 5 = 35**
**0 inquiries: 0 x 4 = 0**
**3 logical files: 3 x 10 = 30**
**2 interfaces: 2 x 7 = 14**
**total raw function points: 127**

## A Function Point Example - 3

**You next adjust for influence factors:**
**data communication: 4**
**on-line data entry: 4**
**complex processing: 3**
**operational ease: 5**
**facilitate change: 5**
**total influence factors: 21**
**Complexity multiplier = 0.65+21x0.01=0.86**

## A Function Point Example - 4

**The function point total is thus: 127x0.86=109.22**
**Using historical data on hours per function point, calculate the development time for the project.**

## Function Point Advantages

**The advantages of function points are:**
**they are usable in the earliest requirements phases**
**they are independent of programming language, product design, or development style**
**there exists a large body of historical data**
**it is a well documented method**
**there is an active users group**

## Function Point Disadvantages

**The disadvantages of function points are:**
**you cannot directly count an existing product’s function point content**
**without historical data, it is difficult to improve estimating skill**
**function points do not reflect language, design, or style differences**
**function points are designed for estimating commercial data processing applications**

## Standard Component Sizing - 1

**Establish the principal product size levels **
**components, modules, screens, etc.**
**determine typical sizes of each level **
**For a new product:**
**determine the component level at which   estimation is practical**
**estimate how many of those components will     likely be in the product**
**determine the maximum and minimum numbers possible**

## Standard Component Sizing - 2

**Calculate the size as the**
**number of components of each type**
**times typical sizes of each type**
**total to give size**
**Calculate for the maximum, minimum, and likely numbers of components.**
**Calculate size as:**
**{maximum+4*(likely)+minimum}/6**

## Standard Component Sizing Example - 1

**Your new program has the following requirements:**
**analyze marketing performance by product line**
**project the likely sales in each product category**
**allocate these sales to marketing regions and time periods**
**produce a monthly report of these projections and the actual results**

## Standard Component Sizing - Example - 2

**You have the following historical data on a number of standard components**
**data input component: 1,108 LOC**
**output component: 675 LOC**
**file component: 1,585 LOC**
**control component: 2,550 LOC**
**computation component: 475 LOC**

## Standard Component Sizing - Example - 3

**First, estimate the minimum, likely, and maximum numbers of components like these in the new product**
**data input component: 1, 4, 7**
**output component: 1, 3, 5**
**file component: 2, 4, 8**
**control component: 1, 2, 3**
**computation component: 1, 3, 7**

## Standard Component Sizing - Example - 4

**Second, calculate the minimum, likely, and maximum size of the product components**
**data input component: 1108, 4432, 7756**
**output component: 675, 2025, 3375**
**file component: 3170, 6340, 12680**
**control component: 2550, 5100, 7650**
**computation component: 475, 1425, 3325**

## Standard Component Sizing - Example - 5

**Third, calculate the minimum, likely, and maximum LOC of the new product**
**minimum: 7,978**
**likely: 13,616**
**maximum: 34,786**
**The size estimate is then**
**LOC = (7978+4*13616+34786)/6 = 16,205 LOC**
**the standard deviation is (34786-7978)/6=4468**
**the estimate range is: 11,737 to 20,673 LOC**

## Standard Component Sizing - Advantages and Disadvantages

**Advantages**
**based on relevant historical data**
**easy to use**
**requires no special tools or training**
**provides a rough estimate range**
**Disadvantages**
**must use large components early in a project**
**limited data on large components **

## Delphi Size Estimating 

**Uses several estimators**
**each makes an independent estimate**
**each submits estimate to a coordinator**
**  **
**Coordinator**
**calculates average estimate**
**enters on form: average, other estimates    (anonymous), and previous estimate**
**When reestimates stabilize**
**average is the estimate**
**range is range of original estimates**

## Delphi Example - 1 

**3 estimators are asked to estimate the product.**
**Their initial estimates are**
** ****A -  13,800 LOC**
** ****B -  15,700 LOC**
** ****C -  21,000 LOC**
**  **
**The coordinator then**
**calculates average estimate as 16,833 LOC**
**returns this with their original estimates to the estimators**

## Delphi Example - 2 

**The estimators then meet and discuss the estimates.  **
**Their second estimates are**
** ****A -  18,500 LOC**
** ****B -  19,500 LOC**
** ****C -  20,000 LOC**
**  **
**The coordinator then**
**calculates average estimate as 19,333 LOC**
**asks the estimators if they agree with this as the estimate**

## Delphi Size Estimating - 2

**Advantages**
**can produce very accurate results**
**utilizes organization’s skills**
**can work for any sized product**
**Disadvantages**
**relies on a few experts**
**is time consuming**
**is subject to common biases**

## Size Estimating Proxies - 1

**The basic issue**
**good size measures are detailed**
**early estimators rarely can think in detail **
**Alternatives**
**wait to estimate until you have the detail **
**make your best guess**
**identify a suitable proxy **

## Size Estimating Proxies - 2

**A good proxy should correlate closely to development costs.**
**A good proxy would be easy to visualize early in development.**
**It should also be a physical entity that can be counted.**

## Example Proxies 

**Function points**
**   **
**Objects**
** **
**Product elements**
**components**
**screens, reports, scripts, files**
**book chapters**

## Function Points as Proxies - 1

**Data show that function point counts correlate well with development time.**
**Function points can be visualized early in development.**
**To use function points properly, trained estimators are required.**

## Function Points as Proxies - 2

**Function points cannot directly be counted.**
**Conversion factors are available for counting LOC and calculating function points from the LOC value.**
**The function point users group (IFPUG) is refining the function point method.**

## Standard Components as Proxies

**Component count correlation with development depends on the components.**
**A lot of development data is required.**
**Component counts are hard to visualize early in development.**
**Components are machine countable.**

## Objects as Proxies - 1

**Correlation with development hours**
**numbers of objects correlate reasonably well**
**object lines of code (LOC) correlate very closely**
**object LOC can be estimated using the standard component estimating method**
**then calculate LOC estimate from historical relationship between object LOC and program LOC**

## Objects as Proxies - 2

**When objects are selected as application entities, they can be visualized early in development.**
**Functions and procedures can often be estimated in the same way.**
** **
**Objects, functions, procedures, and their LOC  can be automatically counted.**

## Object LOC Correlation With Development Hours

## Example Proxies - Other

**Possible candidates**
**screens, reports, scripts, files**
**book chapters**
**If the number of items correlates with development, you estimate the number of items.**
**With a suitable proxy size measure, you can  often estimate proxy size. **

## Chapter Pages vs. Time

## Next Lecture - Estimating Software Size - 2

**The PROBE estimating method**
**Statistical estimating considerations**
**A size estimating example**
**Estimating considerations**

## Assignment #3 - 1

**Read chapter 5**
**Using PSP0.1, write program 3A**
**count object or function LOC in a program**
**produce the name, LOC count, and method count for each object**
**produce the total program LOC count**
**you may enhance program 2A to make program 3A (but keep a copy of 2A)**

## Assignment #3 - 2

**Use program 3A to count programs 1A, 2A, and 3A and report the results in the test report.**
**Produce a report on the defect fix times for programs 1A, 2A, and 3A.**
**Refer to the program and assignment specifications in Appendices C and D.**

## Messages to Remember from Lecture 3 

**1 -  Accurate size estimates will help you to**
**      ****make better development plans.**
**2 -  Size estimating skill improves with practice.**
**3 -  A defined and measured process provides a **
**      ****repeatable basis for improvement.**
**4 -  There are several ways to make size **
**      ****estimates.**