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SEA Side Software Engineering Annotations

Annotation11: Software Metrics

One hour presentation to inform you of new techniques and practices in software development.

Professor Sara StoecklinDirector of Software Engineering- Panama City

Florida State University – Computer Sciencesstoecklin@mail.pc.fsu.edu

stoeckli@cs.fsu.edu850-522-2091

850-522-2023 Ex 182

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Express in Express in NumbersNumbers

Express in Express in NumbersNumbers

Measurement provides a mechanism for objective evaluation

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Software Crisis Software Crisis

• According to American Programmer, 31.1% of computer software projects get canceled before they are completed,

• 52.7% will overrun their initial cost estimates by 189%.

• 94% of project start-ups are restarts of previously failed projects. Solution?systematic approach to software development and measurement

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Software Metrics

• It refers to a broad range of quantitative measurements for computer software that enable to– improve the software process

continuously– assist in quality control and productivity– assess the quality of technical products– assist in tactical decision-making

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Measure, Metrics, Indicators

• Measure.– provides a quantitative indication of the

extent, amount, dimension, capacity, or size of some attributes of a product or process.

• Metrics.– relates the individual measures in some way.

• Indicator.– a combination of metrics that provide insight

into the software process or project or product itself.

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What Should Be Measured?

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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Metrics of Process Improvement

• Focus on Manageable Repeatable Process

• Use of Statistical SQA on Process

• Defect Removal Efficiency

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Statistical Software Process ImprovementStatistical Software Process Improvement

All errors and defects are categorized by origin

The cost to correct each error and defect is recorded

No. of errors and defects in each category is counted and ranked in descending order

The overall cost in each category is computed

Resultant data are analyzed and the “culprit” category is uncovered

Plans are developed to eliminate the errors

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Causes and Origin of Defects

Logic20%

Sofware Interface6%

Hardware Interface8%

User Interface12%

Data Handling11%

Error Checking11%

Standards7%

Specification25%

Logic20%

Sofware Interface6%

Hardware Interface8%

User Interface12%

Data Handling11%

Error Checking11%

Standards7%

Specification25%

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Metrics of Project Management

• Budget• Schedule/ReResource

Management• Risk Management• Project goals met or

exceeded• Customer satisfaction

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Metrics of the Software Product

• Focus on Deliverable Quality

• Analysis Products• Design Product

Complexity – algorithmic, architectural, data flow

• Code Products• Production System

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How Is Quality Measured?

• Analysis Metrics– Function-based Metrics: Function

Points( Albrecht), Feature Points (C. Jones)

– Bang Metric (DeMarco): Functional Primitives, Data Elements, Objects, Relationships, States, Transitions, External Manual Primitives, Input Data Elements, Output Data Elements, Persistent Data Elements, Data Tokens, Relationship Connections.

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Source Lines of Code (SLOC)

• Measures the number of physical lines of active code

• In general the higher the SLOC in a module the less understandable and maintainable the module is

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Function Oriented Metric - Function Points

• Function Points are a measure of “how big” is the program, independently from the actual physical size of it

• It is a weighted count of several features of the program

• Dislikers claim FP make no sense wrt the representational theory of measurement

• There are firms and institutions taking them very seriously

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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

Analyzing the Information Domain

Assuming all inputs with the same weight, all output with the same weight, …

Complete Formula for the Unadjusted Function Points:

Assuming all inputs with the same weight, all output with the same weight, …

Complete Formula for the Unadjusted Function Points:

lesInternalFi terfacesExternalInInquiryOutputInputs

WeiWifWinWoWi

Unadjusted Function Points:Unadjusted Function Points:

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Taking Complexity into Account

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

MultiplierComplexity MultiplierComplexityFCM

Formula:Formula:

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Typical Function-Oriented Metrics

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

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LOC vs. FP

• Relationship between lines of code and Relationship between lines of code and function points depends upon the function points depends upon the programming language that is used to programming language that is used to implement the software and the quality of implement the software and the quality of the designthe design

• Empirical studies show an approximate Empirical studies show an approximate relationship between LOC and FPrelationship between LOC and FP

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LOC/FP (average)Assembly languageAssembly language 320320

CC 128128

COBOL, FORTRANCOBOL, FORTRAN 106106

C++C++ 64 64

Visual BasicVisual Basic 32 32

SmalltalkSmalltalk 22 22

SQLSQL 12 12

Graphical languages (icons)Graphical languages (icons) 4 4

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How Is Quality Measured?

• Design Metrics– Structural Complexity: fan-in, fan-out, morphology

– System Complexity:– Data Complexity:– Component Metrics: Size, Modularity, Localization,

Encapsulation, Information Hiding, Inheritance, Abstraction, Complexity, Coupling, Cohesion, Polymorphism

• Implementation MetricsSize, Complexity, Efficiency, etc.

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Comment Percentage (CP)

• Number of commented lines of code divided by the number of non-blank lines of code

• Usually 20% indicates adequate commenting for C or Fortran code

• The higher the CP value the more maintainable the module is

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Size Oriented Metric - Fan In and Fan Out

• The Fan In of a module is the amount of information that “enters” the module

• The Fan Out of a module is the amount of information that “exits” a module

• We assume all the pieces of information with the same size

• Fan In and Fan Out can be computed for functions, modules, objects, and also non-code components

• Goal - Low Fan Out for ease of maintenance.

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Size Oriented Metric - Halstead Software Science

Primitive Measures

number of distinct operators

number of distinct operands

total number of operator occurrences

total number of operand occurrences

Used to Derive

maintenance effort of software

testing time required for software

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a

YX

Predicate Nodes

Flow Graph

if (a) {X();

} else {Y();

}

if (a) {X();

} else {Y();

}

•V(G) = E - N + 2

• where E = number of edges

• and N = number of nodes

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McCabes Metric

• Smaller the V(G) the simpler the module.

• Modules larger than V(G) 10 are a little unmanageable.

• A high cyclomatic complexity indicates that the code may be of low quality and difficult to test and maintain

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Chidamber and Kemerer Metrics

• Weighted methods per class (MWC)

• Depth of inheritance tree (DIT)

• Number of children (NOC)

• Coupling between object classes (CBO)

• Response for class (RFC)

• Lack of cohesion metric (LCOM)

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Weighted methods per class (WMC)

• ci is the complexity of each method Mi of the class

– Often, only public methods are considered

• Complexity may be the McCabe complexity of the method

• Smaller values are better

• Perhaps the average complexity per method is a better metric?

n

iicWMC

1

The number of methods and complexity of methods involved is a direct predictor of how much time and effort is required to develop and maintain the class.

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Depth of inheritance tree (DIT)

• For the system under examination, consider the hierarchy of classes

• DIT is the length of the maximum path from the node to the root of the tree

• Relates to the scope of the properties– How many ancestor classes can potential affect a class

• Smaller values are better

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Number of children (NOC)

• For any class in the inheritance tree, NOC is the number of immediate children of the class– The number of direct subclasses

• How would you interpret this number?

• A moderate value indicates scope for reuse and high values may indicate an inappropriate abstraction in the design

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Coupling between object classes (CBO)

• For a class, C, the CBO metric is the number of other classes to which the class is coupled

• A class, X, is coupled to class C if– X operates on (affects) C or– C operates on X

• Excessive coupling indicates weakness of class encapsulation and may inhibit reuse

• High coupling also indicates that more faults may be introduced due to inter-class activities

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Response for class (RFC)

• Mci # of methods called in response to a message that invokes method Mi

– Fully nested set of calls

• Smaller numbers are better– Larger numbers indicate

increased complexity and debugging difficulties

n

iiMc

1

If a large number of methods can be invoked in response to a message, the testing and debugging of the class becomes more complicated

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Lack of cohesion metric (LCOM)

• Number of methods in a class that reference a specific instance variable

• A measure of the “tightness” of the code

• If a method references many instance variables, then it is more complex, and less cohesive

• The larger the number of similar methods in a class the more cohesive the class is

• Cohesiveness of methods within a class is desirable, since it promotes encapsulation

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Testing Metrics

• Metrics that predict the likely number of tests required during various testing phases

• Metrics that focus on test coverage for a given component

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Views on SE Measurement

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Views on SE Measurement

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Views on SE Measurement

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12 Steps to Useful Software Metrics

Step 1 - Identify Metrics Customers

Step 2 - Target Goals

Step 3 - Ask Questions

Step 4 - Select Metrics

Step 5 - Standardize Definitions

Step 6 - Choose a Model

Step 7 - Establish Counting Criteria

Step 8 - Decide On Decision Criteria

Step 9 - Define Reporting Mechanisms

Step 10 - Determine Additional Qualifiers

Step 11 - Collect Data

Step 12 - Consider Human Factors

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Step 1 - Identify Metrics Customers

Who needs the information?

Who’s going to use the metrics?

If the metric does not have a customer -- do not use it.

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Step 2 - Target Goals

Organizational goals

– Be the low cost provider

– Meet projected revenue targets

Project goals

– Deliver the product by June 1st

– Finish the project within budget

Task goals (entry & exit criteria)

– Effectively inspect software module ABC

– Obtain 100% statement coverage during testing

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Step 3 - Ask Questions

Goal: Maintain a high level of customer satisfaction

• What is our current level of customer satisfaction?

• What attributes of our products and services are most important to our customers?

• How do we compare with our competition?

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Step 4 - Select MetricsSelect metrics that provide information to help answer the questions

• Be practical, realistic, pragmatic

• Consider current engineering environment

• Start with the possible

Metrics don’t solve problems -- people solve problems

Metrics provide information so people can make better decisions

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Selecting Metrics

Goal: Ensure all known defects are corrected before shipment

• • • • • • •

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Metrics Objective Statement Template

To

understandevaluatecontrolpredict

theattributeof theentity

in order to

goal(s)

evaluate

% defectsfound &

corrected during testing

To thein order to

ensure all known defectsare corrected

before shipment

Example - Metric: % defects corrected

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Step 5 - Standardize Definitions

Developer User

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Step 6 - Choose a MeasurementModels for code inspection metrics

• Primitive Measurements:– Lines of Code Inspected = loc

– Hours Spent Preparing = prep_hrs

– Hours Spent Inspecting = in_hrs

– Discovered Defects = defects

• Other Measurements:– Preparation Rate = loc / prep_hrs

– Inspection Rate = loc / in_hrs

– Defect Detection Rate = defects / (prep_hrs + in_hrs)

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Step 7 - Establish Counting Criteria

Lines of Code

• Variations in counting

• No industry accepted standard

• SEI guideline - check sheets for criteria

• Advice: use a tool

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Counting Criteria - Effort

What is a Software Project?

• When does it start / stop?

• What activities does it include?

• Who works on it?

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Step 8 - Decide On Decision Criteria

Establish Baselines

• Current value– Problem report backlog

– Defect prone modules

• Statistical analysis (mean & distribution)– Defect density

– Fix response time

– Cycle time

– Variance from budget (e.g., cost, schedule)

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Step 9 - Define Reporting MechanismsOpen Fixed Resolved

Jan-97 23 13 3Feb-97 27 24 11Mar-97 18 26 15Apr-97 12 18 27

0

40

80

120

0 20 40 60 80 100 120

0

20

40

60

80

100

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

1 2 3 4 5 6 7 8 9 10 11 12

0

20

40

60

80

100

Jan Mar May July 0

40

80

120

160

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

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Step 10 - Determine Additional Qualifiers

A good metric is a generic metric

Additional qualifiers:

• Provide demographic information

• Allow detailed analysis at multiple levels

• Define additional data requirements

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Additional Qualifier Example

Metric: software defect arrival rate

• Release / product / product line

• Module / program / subsystem

• Reporting customer / customer group

• Root cause

• Phase found / phase introduced

• Severity

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Step 11 – Collect Data What data to collect?

• Metric primitives

• Additional qualifiers

Who should collect the data?• The data owner

– Direct access to source of data

– Responsible for generating data

– Owners more likely to detect anomalies

– Eliminates double data entry

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Examples of Data OwnershipOwner Examples of Data Owned

• Management • Schedule• Budget

• Engineers • Time spent per task• Inspection data including defects found• Root cause of defects

• Testers • Test Cases planned / executed / passed• Problems• Test coverage

• Configuration management • Lines of code specialists • Modules changed• Users • Problems

• Operation hours

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Step 12 – Consider Human Factors

The People Side of the Metrics Equation

• How measures affect people

• How people affect measures

“Don’t underestimate the intelligence of your engineers. For any one metric you can come up with, they will find at least two ways to beat it.” [unknown]

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Don’t

Measure individuals

Use metrics as a “stick”

Ignore the data

Use only one metric

Cost

Quality

Schedule

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DoSelect metrics based on goals

Goal 1 Goal 2

Question 1 Question 2 Question 3 Question 4

Metrics 1 Metric 2 Metric 3 Metric 4 Metric 5

[Basili-88]

Focus on processes, products & services

Processes,Products &

Services

Provide feedback

Feedback

Data

Data Providers Metrics

Obtain “buy-in”

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References• Chidamber, S. R. & Kemerer, C. F., “A Metrics Suite for Object Oriented Design”, IEEE Transactions on Software Engineering, Vol. 20,

#6, June 1994. • Hitz, M. and Montazeri, B. “Chidamber and Kemerer’s Metrics Suite: A Measurement Theory Perspective”, IEE Transaction on Software

Engineering, Vol. 22, No. 4, April 1996. • Lacovara , R.C., and Stark G. E., “A Short Guide to Complexity Analysis, Interpretation and Application”, May 17, 1994.

http://members.aol.com/GEShome/complexity/Comp.html • Tang, M., Kao, M., and Chen, M., “An Empirical Study on Object-Oriented Metrics”, IEEE Transactions on Software Engineering, 0-7695-

0403-5, 1999. • Tegarden, D., Sheetz, S., Monarchi, D., “Effectiveness of Traditional Software Metrics for Object-Oriented Systems”, Proceedings: 25th

Hawaii International Confernce on System Sciences, January, 1992, pp. 359-368.

• “Principal Components of Orthogonal Object-Oriented Metrics”http://satc.gsfc.nasa.gov/support/OSMASAS_SEP01/Principal_Components_of_Orthogonal_Object_Oriented_Metrics.htm

• Software Engineering Fundamentalsby Behforhooz & Hudson, Oxford Press, 1996Chapter 18: Software Quality and Quality Assurrance

• Software Engineering: A Practioner's Approachby Roger Pressman, McGraw-Hill, 1997

• IEEE Standard on Software Quality Metrics Validation Methdology (1061)

• Object-Oriented Metricsby Brian Henderson-Sellers, Prentice-Hall, 1996