chapter_04 software process and project metrics

8/3/2019 Chapter_04 Software Process and Project Metrics

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These courseware materials are to be used in conjunction with Software Engineering: A Practitioners Approach, 5/e and areprovided with permission by R.S. Pressman & Associates, Inc., copyright 1996, 2001

Chapter 4Chapter 4

Software Process and Project MetricsSoftware Process and Project Metrics


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Why do we Measure?Why do we Measure?

To characterizeTo characterize

To evaluateTo evaluate

To predictTo predict

To improveTo improve


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Product MetricsProduct Metrics

focus on the quality of deliverablesfocus on the quality of deliverables

measures of analysis modelmeasures of analysis model

complexity of the designcomplexity of the design internal algorithmic complexityinternal algorithmic complexity architectural complexityarchitectural complexity data flow complexitydata flow complexity

code measures (e.g., Halstead)code measures (e.g., Halstead)

measures of process effectivenessmeasures of process effectiveness e.g., defect removal efficiencye.g., defect removal efficiency


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Process MetricsProcess Metrics -- strategicstrategic

majority focus on quality achieved as amajority focus on quality achieved as aconsequence of a repeatable or managedconsequence of a repeatable or managedprocessprocess

statistical SQA datastatistical SQA data error categorization & analysiserror categorization & analysis

defect removal efficiencydefect removal efficiency propagation from phase to phasepropagation from phase to phase

reuse datareuse data


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Project MetricsProject Metrics --

tacticaltactical Effort/time per SE taskEffort/time per SE task

Errors uncovered per review hourErrors uncovered per review hour

Scheduled vs. actual milestone datesScheduled vs. actual milestone dates Changes (number) and their characteristicsChanges (number) and their characteristics

Distribution of effort on SE tasksDistribution of effort on SE tasks


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Normalization for MetricsNormalization for Metrics

Normalized data are used to evaluate the processand the product (but never individual people)

size-oriented normalization the line of code approach

function-oriented normalization the function pointapproach


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Typical SizeTypical Size--Oriented MetricsOriented Metrics

errors per KLOC (thousand lines oferrors per KLOC (thousand lines ofcode)code)

defects per KLOCdefects per KLOC

$ per LOC$ per LOC page of documentation per KLOCpage of documentation per KLOC

errors / personerrors / person--monthmonth

LOC per personLOC per person--monthmonth

$ / page of documentation$ / page of documentation


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Computing Function PointsComputing Function PointsAnalyze information

domain of theapplicationand develop counts

Weight each count byassessing complexity

Assess influence ofglobal factors that affectthe application

Computefunction points

Establish count for input domain andsystem interfaces

Assign level of complexity or weight to each count

Grade significance of external factors, Fsuch as reuse, concurrency, OS, ...

degree of influence: N = Fi

complexity multiplier: C = (0.65 + 0.01 x N)

function points = (count x weight) x Cwhere:

i


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Analyzing the Information DomainAnalyzing the Information Domain

complexity multiplier

function points

number of user inputs

number of user outputs

number of user inquiries

number of files

number of ext.interfaces

measurement parameter

3

4

3

7

5

countweighting factor

simple avg. complex

4

5

4

10

7

6

7

6

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=

=

=

=

=

count-total

X

X

X

X

X


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Taking Complexity into AccountTaking Complexity into Account

Factors are rated on a scale of 0 (not important)to 5 (very important):

data communications

distributed functionsheavily used configurationtransaction rateon-line data entryend user efficiency

on-line update

complex processinginstallation easeoperational easemultiple sitesfacilitate change


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Why Opt for FP Measures?Why Opt for FP Measures?

independent of programming language

uses readily countable characteristics of the"information domain" of the problem

does not "penalize" inventive implementations thatrequire fewer LOC than others

makes it easier to accommodate reuse and the

trend toward object-oriented approaches


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Measuring QualityMeasuring Quality

CorrectnessCorrectness the degree to which a programthe degree to which a programoperates according to specificationoperates according to specification

MaintainabilityMaintainabilitythe degree to which athe degree to which a

program is amenable to changeprogram is amenable to change IntegrityIntegritythe degree to which a program isthe degree to which a program is

impervious to outside attackimpervious to outside attack

UsabilityUsabilitythe degree to which a program isthe degree to which a program iseasy to useeasy to use


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Defect Removal EfficiencyDefect Removal Efficiency

DRE = (errors) / (errors + defects)

where

errors = problems found before release

defects = problems found after release


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Managing VariationManaging Variation

0

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1 3 5 7 9 11 13 15 17 19

Projects

Er,Erro rs fo u nd /

re vie w

ho u r

The mR Control Chart

chapter_04 software process and project metrics

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