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Chapter 4Chapter 4Software Process and Project Software Process and Project

MetricsMetrics

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Measurement & Measurement & MetricsMetrics... collecting metrics is too hard ... ... collecting metrics is too hard ...

it's too time-consuming ... it's too it's too time-consuming ... it's too political ... it won't prove anything ...political ... it won't prove anything ...

Anything that you need to Anything that you need to quantify can be measured in quantify can be measured in some way that is superior to some way that is superior to not measuring it at all ..not measuring it at all ..

Tom GilbTom Gilb

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Why do we Why do we Measure?Measure?

To characterizeTo characterize To evaluateTo evaluate To predictTo predict To improveTo improve

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A Good Manager A Good Manager MeasuresMeasures

measurementmeasurement

What do weWhat do weuse as ause as abasis?basis? • • size?size? • • function?function?

project metricsproject metrics

process metricsprocess metricsprocessprocess

productproduct

product metricsproduct metrics

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Process MetricsProcess Metrics

majority focus on quality achieved majority focus on quality achieved as a consequence of a repeatable as a consequence of a repeatable or managed processor managed process

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

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 Changes (number) and their

characteristicscharacteristics Distribution of effort on SE tasksDistribution of effort on SE tasks

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Product MetricsProduct Metrics focus on the quality of focus on the quality of

deliverablesdeliverables measures of analysis modelmeasures of analysis model complexity of the designcomplexity of the design

internal algorithmic complexityinternal algorithmic complexity architectural complexityarchitectural complexitydata 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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Metrics Metrics GuidelinesGuidelines Use common sense and organizational sensitivity Use common sense and organizational sensitivity

when interpreting metrics data.when interpreting metrics data. Provide regular feedback to the individuals and teams Provide regular feedback to the individuals and teams

who have worked to collect measures and metrics.who have worked to collect measures and metrics. Don’t use metrics to appraise individuals.Don’t use metrics to appraise individuals. Work with practitioners and teams to set clear goals Work with practitioners and teams to set clear goals

and metrics that will be used to achieve them.and metrics that will be used to achieve them. Never use metrics to threaten individuals or teams.Never use metrics to threaten individuals or teams. Metrics data that indicate a problem area should not Metrics data that indicate a problem area should not

be considered “negative.” These data are merely an be considered “negative.” These data are merely an indicator for process improvement.indicator for process improvement.

Don’t obsess on a single metric to the exclusion of Don’t obsess on a single metric to the exclusion of other important metrics.other important metrics.

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Normalization for Normalization for MetricsMetricsNormalized data are used to evaluate the process

and the product (but never individual people)

size-oriented normalization —the line of code approach function-oriented normalization —the function point approach

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Typical Size-Oriented Typical Size-Oriented MetricsMetrics

errors per KLOC (thousand lines of code)errors per KLOC (thousand lines of code) defects per KLOCdefects per KLOC $ per LOC$ per LOC page of documentation per KLOCpage of documentation per KLOC errors / person-montherrors / person-month LOC per person-monthLOC per person-month $ / page of documentation$ / page of documentation

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Typical Function-Oriented Typical Function-Oriented MetricsMetrics

errors per FP (thousand lines of code)errors per FP (thousand lines of code) defects per FPdefects per FP $ per FP$ per FP pages of documentation per FPpages of documentation per FP FP per person-monthFP per person-month

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

independent of programming language uses readily countable characteristics of the "information domain" of the problem does not "penalize" inventive implementations that require fewer LOC than others makes it easier to accommodate reuse and the trend toward object-oriented approaches

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Computing Function Computing Function PointsPointsAnalyze information

domain of the application and develop counts

Weight each count by assessing complexity

Assess influence of global factors that affect the application

Compute function points

Establish count for input domain and system interfaces

Assign level of complexity or weight to each count

Grade significance of external factors, F such as reuse, concurrency, OS, ...

degree of influence: N = Fi

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

function points = (count x weight) x C

where:

i

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Analyzing the Information Analyzing the Information DomainDomain

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 15 10

= = = = =

count-total

X X X X X

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Taking Complexity into Taking Complexity into AccountAccount

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

data communications distributed functions heavily used configuration transaction rate on-line data entry end user efficiency

on-line update complex processing installation ease operational ease multiple sites facilitate change

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Measuring Measuring QualityQuality

Correctness — the degree to which a Correctness — the degree to which a program operates according to program operates according to specificationspecification

Maintainability—the degree to which a Maintainability—the degree to which a program is amenable to changeprogram is amenable to change

Integrity—the degree to which a Integrity—the degree to which a program is impervious to outside program is impervious to outside attackattack

Usability—the degree to which a Usability—the degree to which a program is easy to useprogram is easy to use

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Defect Removal Defect Removal EfficiencyEfficiency

DRE = (errors) / (errors + defects)

where

errors = problems found before release

defects = problems found after release