Metrics

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Learning objectives

• Software metrics• Metrics for various phases• Why metrics are needed• How to collect metrics• How to use metrics

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Questions

• How big is the program?• Huge!!

• How close are you to finishing?• We are almost there!!

• Can you, as a manager, make any useful decisions from such subjective information?

• Need information like, cost, effort, size of project.

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Metrics

• Quantifiable measures that could be used to measure characteristics of a software system or the software development process

• Required in all phases• Required for effective management• Managers need quantifiable information,

and not subjective information • Subjective information goes against the

fundamental goal of engineering)

Overview

• Software process and project metrics are quantitative measures that enable software engineers to gain insight into the efficiency of the software process and the projects conducted using the process framework.

• In software project management, we are primarily concerned with productivity and quality metrics.

• There are four reasons for measuring software processes, products, and resources • to characterize, • to evaluate, • to predict, • to improve

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SOFTWARE METRICS FOR PROCESS AND PROJECTS

Software Process Metrics and Project Metrics are Quantitative Measures that enable Software Professionals to gain insight into the efficacy of Software Process and the Project that are conducted using the Process as a Framework.

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SOFTWARE METRICS FOR PROCESS AND PROJECTS

• Quality and Productivity data are collected, analysed and compared against past averages to :•Pinpoint/Uncover Problem areas before they “Go critical”

•Track potential Risks

•Assess Productivity improvements.

•Assess the status of an ongoing Project

•Adjust work flow or Tasks

•Evaluate the Project Team’s ability to Control Quality of

Software Work Product.7

• Measures that are collected by a Project team and converted into Metrics for use during a Project can also be transmitted to those with responsibility for Software Process improvement.

• For this reason, many of the same Metrics are used in both the Process and Project domain.

Definitions

• Measure - quantitative indication of extent, amount, dimension, capacity, or size of some attribute of a product or process.• E.g., Number of errors

• Metric - quantitative measure of degree to which a system, component or process possesses a given attribute. “A handle or guess about a given attribute.”• E.g., Number of errors found per person hours

expended

Why Measure Software?

• Determine the quality of the current product or process

• Predict qualities of a product/process

• Improve quality of a product/process

Motivation for Metrics

• Estimate the cost & schedule of future projects

• Evaluate the productivity impacts of new tools and techniques

• Establish productivity trends over time

• Improve software quality

• Forecast future staffing needs

• Anticipate and reduce future maintenance needs

Example Metrics

• Defect rates• Error rates

• Measured by:• individual• module• during development

• Errors should be categorized by origin, type, cost

Metric Classification

• Products• Explicit results of software development

activities• Deliverables, documentation, by products

• Processes• Activities related to production of software

• Resources• Inputs into the software development activities• hardware, knowledge, people

Product vs. Process

• Process Metrics • Insights of process paradigm, software

engineering tasks, work product, or milestones • Lead to long term process improvement

• Product Metrics • Assesses the state of the project• Track potential risks• Uncover problem areas• Adjust workflow or tasks• Evaluate teams ability to control quality

Types of Measures

• Direct Measures (internal attributes)• Cost, effort, LOC, speed, memory

• Indirect Measures (external attributes)• Functionality, quality, complexity, efficiency,

reliability, maintainability

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Classification of software metrics

• Product metrics• quantify characteristics of the product being

developed• size, reliability

• Process metrics• quantify characteristics of the process being

used to develop the software• efficiency of fault detection

• Resources• Inputs into the software development activities• hardware, knowledge, people

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Issues

• Cost of collecting metrics• Automation is less costly than manual method• CASE tool may not be free

• Development cost of the tool• Extra execution time for collecting metrics

• Interpretation of metrics consumes resources

• Validity of metrics• Does the metric really measure what it

should?• What exactly should be measured?

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Issues cont.

• Selection of metrics for measurement• Hundreds available and with some cost

• Basic metrics• Size (like LOC)• Cost (in $$$)• Duration (months)• Effort (person-months)• Quality (number of faults detected)

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Selection of metrics

• Identify problems from the basic metrics• high fault rates during coding phase

• Introduce strategy to correct the problems• To monitor success, collect more detailed

metrics• fault rates of individual programmers

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Utility of metrics

• LOC• size of product• take a regular intervals and find out how fast

the project is growing

• What if # defects per 1000 LOC is high?• Then even if the LOC is high, most of the code

has to be thrown away.

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Applicability of metrics

• Throughout the software process, like• effort in person-months• staff turnover• cost

• Specific to a phase• LOC• # defects detected per hour of reviewing

specifications

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Metrics: planning

• When can we plan the entire software project?• At the very beginning?• After a rapid prototype is made?• After the requirements phase?• After the specifications are ready?

• Sometimes there is a need to do it early.

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Metrics: planning

• graph of cost estimate

Phase during which cost estimation is madeRe

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Requirements Specifications Integration

Design Implementation

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Planning: Cost estimation

• Client wants to know:• How much will I have to pay?

• Problem with• underestimation (possible loss by the

developer)• overestimation (client may offer bid to

someone else)

• Cost• internal (salaries of personnel, overheads)• external (usually cost + profit)

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Cost estimation

• Other factors:• desperate for work - charge less• client may think low cost => low quality, so

raise the amount

• Too many variables• Human factors

• Quality of programmers, experience• What if someone leaves midway

• Size of product

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Planning: Duration estimation

• Problem with underestimation• unable to keep to schedule, leading to

• loss of credibility• possible penalty clauses

• Problem with overestimation• the client may go to other developers

• Difficulty because of similar reasons as for cost estimation

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Metrics: planning - size of product

• Units for measurement• LOC = lines of code• KDSI = thousand delivered source instructions

• Problems• creation of code is only a part of the total effort• effect of using different languages on LOC• how should one count LOC?

• executable lines of code?• data definitions• comments? What are the pros and cons?

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Problems with lines of code

• Problems• More on how to count

• Job control language statements?• What if lines are changed or deleted?• What if code is reused?

• Not all code is delivered to clients• code may be for tool support

• What if you are using a code generator?• Early on, you can only estimate the lines of

code. So, the cost estimation is based on another estimated quantity!!!

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Estimating size of product

• FFP metric for cost estimation of medium-scale products

• Files, flows and processes (FFP)• File: collection of logically or physically related

records that are permanently resident• Flow: a data interface between the product and

the environment• Process: functionally defined logical or arithmetic

manipulation of data

Size : S = #Files + #Flows + #Process,

Cost : C = d X S

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Techniques of cost estimation

• Take into account the following:• Skill levels of the programmers• Complexity of the project• Size of the project• Familiarity of the development team• Hardware• Availability of CASE tools• Deadline effect

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Techniques of cost estimation

• Expert judgement by analogy• Bottom up approach• Algorithmic cost estimation models

• Based on mathematical theories• resource consumption during s/w development

obeys a specific distribution

• Based on statistics• large number of projects are studied

• Hybrid models• mathematical models, statistics and expert

judgement

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COCOMO

• COnstructive COst MOdel• Series of three models

• Basic - macroestimation model

• Intermediate COCOMO

• Detailed - microestimation model

• Estimates total effort in terms of person-months

• Cost of development, management, support tasks included

• Secretarial staff not included

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Intermediate COCOMO

• Obtain an initial estimate (nominal estimate) of the development effort from the estimate of KDSI• Nominal effort = a X (KDSI)b person-months

System a b

OrganicSemi-detachedEmbedded

3.23.02.8

1.051.121.20

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Kind of systems

• Organic• Organization has considerable experience in

that area• Requirements are less stringent• Small teams• Simple business systems, data processing sys

• Semi-detached• New operating system• Database management system• Complex inventory management system

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Kind of systems

• Embedded• Ambitious, novel projects• Organization has little experience• Stringent requirements for interfacing,

reliability• Tight constraints from the environment• Embedded avionics systems, real-time

command systems

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Intermediate COCOMO (contd)

• Determine a set of 15 multiplying factors from different attributes (cost driver attributes) of the project

• Adjust the effort estimate by multiplying the initial estimate with all the multiplying factors

• Also have phase-wise distribution

Cost Driver Attributes

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Cost Drivers

Ratings

Very Low Low Nominal High

Very High Extra High

Product attributes

Required software reliability 0.75 0.88 1.00 1.15 1.40  

Size of application database   0.94 1.00 1.08 1.16  

Complexity of the product 0.70 0.85 1.00 1.15 1.30 1.65

Hardware attributes

Run-time performance constraints     1.00 1.11 1.30 1.66

Memory constraints     1.00 1.06 1.21 1.56

Volatility of the virtual machine environment

  0.87 1.00 1.15 1.30  

Required turnabout time   0.87 1.00 1.07 1.15  

Cost Driver Attributes

Cost Drivers

Ratings

Very Low Low Nominal High

Very High

Extra High

Personnel attributesAnalyst capability 1.46 1.19 1.00 0.86 0.71  Applications experience 1.29 1.13 1.00 0.91 0.82  Software engineer capability 1.42 1.17 1.00 0.86 0.70  Virtual machine experience 1.21 1.10 1.00 0.90    Programming language experience 1.14 1.07 1.00 0.95    

Project attributesApplication of software engineering methods 1.24 1.10 1.00 0.91 0.82  Use of software tools 1.24 1.10 1.00 0.91 0.83  Required development schedule 1.23 1.08 1.00 1.04 1.10  

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Determining the rating

• Module complexity multiplier• Very low: control operations consist of a

sequence of constructs of structured programming

• Low: Nested operators• Nominal: Intermodule control and decision

tables• High: Highly nested operators, compound

predicates, stacks and queues• Very high: Reentrant and recursive coding,

fixed priority handling

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COCOMO example

• System for office automation• Four major modules

• data entry: 0.6 KDSI• data update: 0.6 KDSI• date query: 0.8 KDSI• report generator: 1.0 KDSI• Total: 3.0 KDSI

• Category: organic• Initial effort: 3.2 * 31.05 = 10.14 PM

• (PM = person-months)

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COCOMO example (contd)

• From the requirements the ratings were assessed:• Complexity High 1.15• Storage High 1.06• Experience Low 1.13• Programmer Capability Low 1.17• Other ratings are nominal

• EAF = 1.15 * 1.06 * 1.13 * 1.17 = 1.61• Adjusted effort = 1.61 * 10.14 = 16.3 PM

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Metrics: requirements phase

• Number of requirements that change during the rest of the software development process• if a large number changed during

specification, design, …, something is wrong in the requirements phase

• Metrics for rapid prototyping• Are defect rates, mean-time-to-failure useful?• Knowing how often requirements change?• Knowing number of times features are tried?

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Metrics: specification phase

• Size of specifications document• may predict effort required for subsequent

products• What can be counted?

• Number of items in the data dictionary• number of files

• number of data items

• number of processes

• Tentative information• a process in a DFD may be broken down later into

different modules• a number of processes may constitute one module

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Metrics: specification phase

• Cost• Duration• Effort• Quality

• number of faults found during inspection• rate at which faults are found (efficiency of

inspection)

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Metrics: Design Phase

• Number of modules (measure of size of target product)

• Fault statistics• Module cohesion• Module coupling• Cyclomatic complexity• Fan-in, fan-out

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Cyclomatic complexity

• Number of binary decisions + 1• The number of branches in a module• Proposed by McCabe• Lower the value of this number, the better• Only control complexity, no data complexity• For OO, cyclomatic complexity is usually low

because methods are mostly small• also, data component is important for OO, but

ignored in cyclomatic complexity

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Architecture design as a directed graph• Fan-in of a module:

• number of flows into a module plus the number of global data structures accessed by the module

• Fan-out of a module:• number of flows out of the module plus the

number of data structures updated by the module

• Measure of complexity:• length X (fan-in X fan-out)2

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OO design metrics

• Assumption: The effort in developing a class is determined by the number of methods.

• Hence the overall complexity of a class can be measured as a function of the complexity of its methods.

Proposal: Weighted Methods per class (WMC)

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WMC

• Let class C have methods M1, M2, .....Mn.

• Let Ci denote the complexity of method

• How to measure Ci?

n

iicWMC

1

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WMC validation

• Most classes tend to have a small number of methods, are simple, and provide some specific abstraction and operations.

• WMC metric has a reasonable correlation with fault-proneness of a class.

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Depth of inheritance tree

• Depth of a class in a class hierarchy determines potential for re-use. Deeper classes have higher potential for re-use.

• Inheritance increases coupling... changing classes becomes harder.

• Depth of Inheritance (DIT) of class C is the length of the shortest path from the root of the inheritance tree to C.

• In case of multiple inheritance DIT is the maximum length of the path from the root to C.

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DIT evaluation

• Basili et al. study,1995. • Chidamber and Kemerer study, 1994.

• Most classes tend to be close to the root. • Maximum DIT value found to be 10.• Most classes have DIT=0.• DIT is significant in predicting error proneness of a

class. Higher DIT leads to higher error-proneness.

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Metrics: implementation phase

• Intuition: more complex modules are more likely to contain faults

• Redesigning complex modules may be cheaper than debugging complex faulty modules

• Measures of complexity:• LOC

• assume constant probability of fault per LOC• empirical evidence: number of faults related to the

size of the product

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Metrics: implementation phase

• McCabe’s cyclomatic complexity• Essentially the number of branches in a module• Number of tests needed for branch coverage of a

module• Easily computed• In some cases, good for predicting faults• Validity questioned

• Theoretical grounds

• Experimentally

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Metrics: implementation phase

• Halstead’s software metrics• Number of distinct operators in the module (+. -. If,

goto)• Number of distinct operands• Total number of operators• Total number of operands

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Metrics: implementation phase

• High correlation shown between LOC and other complexity metrics

• Complexity metrics provide little improvement over LOC

• Problem with Halstead metrics for modern languages• Constructor: is it an operator? Operand?

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Metrics: implementation and integration phase• Total number of test cases• Number of tests resulting in failure• Fault statistics

• Total number of faults• Types of faults

• misunderstanding the design• lack of initialization• inconsistent use of variables

• Statistical-based testing: • zero-failure technique

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Zero failure technique

• The longer a product is tested without a single failure being observed, the greater the likelihood that the product is free of faults.

• Assume that the chance of failure decreases exponentially as testing proceeds.

• Figure out the number of test hours required without a single failure occurring.

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Metrics: inspections

• Purpose: measure effectiveness of inspections• may reflect deficiencies of the development

team, quality of code

• Measure fault density• Faults per page - specs and design inspection• Faults per KLOC - code inspection• Fault detection rate - #faults / hour• Fault detection efficiency - #faults/person-hour

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Metrics: maintenance phase

• Metrics related to the activities performed. What are they?

• Specific metrics:• total number of faults reported• classifications by severity, fault type• status of fault reports (reported/fixed)

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References

• S. R. Scach - Classical and Object-Oriented Software Engineering (Look at metrics under Index)

• P. Jalote - An Integrated Approach to Software Engineering (Look at metrics under Index)