STM-U3

Prof. N. Prasanna Balaji

SOFTWARE TESTING METHODOLOGYTerm:2009-2010

B.Tech III IT – SEC- II First Semester

Unit-III PPT Slides

Text Books: 1.Software Testing Techniques: Boris Beizer 2. Craft of Software Testing: Brain Marrick

Sub Topic No’s

Sub Topic name Lecturer No

Slide No’s

1 Transaction flow Testing: Overview L1 3

2 Transaction flow Graph L2 6

3 Transaction flow testing techniques L3 18

4 Dataflow testing: Basics L4 27

5 Dataflow Madel L5 47

6 Strategies in dataflow testing L6 57

7 Application of dataflow testing L7 69

8 Review: Previous years Question papers L8 73

ref boris beizer 3

Definitions of Transaction,Transaction-flow, TFG

Transaction-flow

Transaction-flow represents a system’s processing. Functional testing methods are applied for testing T-F.

Transaction-flow Graph

TFG represents a behavioral (functional) model of the program (system) used for functional testing by an independent system tester.

Transaction

• is a unit of work seen from system’s user point of view.

• consists of a sequence of operations performed by a system, persons or external devices.

• is created (birth) due to an external act & up on its completion (closure), it remains in the form of historical records.

ref boris beizer 4

A Simple Transaction

Example: the sequence of steps in a transaction in an online information retrieval system

1. accept inputs 7. Accept Inputs

2. Validate inputs (Birth of tr.) 8. Validate inputs

3. Transmit ack. to the user 9. Process the request

4. Process input 10. Update file

5. Search file 11. Transmit output

6. Request direction from user 12. Record transaction in log & cleanup (Closure)

Users View of a transaction : Single step

Systems view : Sequence of many operations

ref boris beizer 5

Example of a Transaction flow (diagram)

User (terminal) Terminal controller CPU

User User BeginBegin

Request order

from CPU

Request Type

cancel

AcceptOrder from

ProcessForm

BTransmitPage to terminal

CAcceptInputField

MoreFields?

TransmitTo CPU

User wantsReview?D

CPU-AcceptConfirm

Valid ?Y

MorePages ?

NDoneDone

TransmitDiagnosticto Terminal

Set upReview

ref boris beizer 6

Definitions

Transaction-flow Graph : a scenario between users & computer

Transaction-flow : an internal sequence of events in processing a transaction

Uses of Transaction-flow

Specifying requirements of big, online and complicated systems.

Airline reservation systems, air-traffic control systems.

Loops are less as compared to CFG. Loops are used for user input error processing

ref boris beizer 7

Implementation of Transaction-Flow (in a system) U2

• Implicit in the design of system’s control structure & associated database.

• No direct one-to-one correspondence between the “processes” and “decisions” of transaction-flow, and the corresponding program component.

• A transaction-flow is a path taken by the transaction through a succession of processing modules.

• A transaction is represented by a token.

• A transaction-flow graph is a pictorial representation of what happens to the tokens.

Input S A S B S C S S

ES : Scheduler A, B, C, D, E : Processes

Output

ref boris beizer 8

Implementation of Transaction-Flow U2

System Control Structure (architecture of the implementation) :

FrontEnd

InputQueue EXECUTIVE

SCHEDULER - AND / OR OPERATING SYSTEMDISPATCHER

OutputModule

OutputQueue

ProcessQueues A Processor B Processor C Processor D Processor E Processor

Application Processes

Do All B’s

Do All D’s

DiscReads

Do All C’s

Do All A’s

TapeWrites

TapeReads

Do All B’s

Do All E’s

DiscWrites

Executive / Dispatcher Flowchart (a sample sequence)

ref boris beizer 9

System control structureSystem control structure

System is controlled by a scheduler …

A Transaction is created by filling in a Transaction Control Block (TCB) by user inputs and by placing that token on input Q of Scheduler.

Scheduler examines and places it on appropriate process Q such as A. When A finishes with the Token, it places the TCB back on the scheduler Q.

Scheduler routes it to the next process after examining the token :

1. It contains tables or code to route a token to the next process.

2. It may contain routing information only in tables.

3. Scheduler contains no code / data. Processing modules contain code for routing.

ref boris beizer 10

Transaction Processing SystemTransaction Processing System (simplified):

• There are many Tr. & Tr-flows in the system.

• Scheduler invokes processes A to E as well as disk & tape read & writes.

• The order of execution depends on priority & other reasons.

Cyclic structure like in this example is common in process control & communication systems.

The criteria for implementation mechanism depends on performance and resource optimization.

ref boris beizer 11

A perspective of Transaction-Flow U2

Transaction-flow testing is a block box technique. (as we assumed nothing regarding computer, communications, environment, O.S., transaction identity or structure or state.)

1. TFG is a kind of DFG.

• TFG has tokens, & DFG has data objects with history of operations applied on them.

• Many techniques of CFG apply to TFG & DFG

2. Decision nodes of TFG have exception exits to the central recovery process.

3. So, we ignore the effect of interrupts in a Transaction-flow.

ref boris beizer12

Transaction Flows – splitting & merging decisionsU2-B

Splits of transactions (Births)

1. A decision point in TFG

Alternative 1

Alternative 2

Daughter Tr.

ParentParent

Daughter Tr.

Daughter Tr.Parent

2.Biosis

3.Mitosis

ref boris beizer13

Transaction Flows – splitting & merging decisionsU2

Mergers of transactionsMergers of transactions

1.1. Junction Junction

DaughterParent

Parent

3. Conjugation

Path 2

Path 1

Continue

Daughter Tr.

Predator

2. Absorption

ref boris beizer14

TFG – splitting, merging Transactions U2

NOTES:NOTES:

• Multiple meanings now for decision and junction symbols in a TFG.

• Simple TFG model is not enough to represent multi-processor systems & associated coordination systems.

• Petrinet model uses operations for all the above. But Petrinets are applied to H/W, N/W protocol testing – but not Software.

ref boris beizer15

Simplified TFG model U2

Simplify TFG modelSimplify TFG model

• Add New Tr-Flows for Add New Tr-Flows for Biosis, Mitosis, Absorption, ConjugationBiosis, Mitosis, Absorption, Conjugation

• Problems for programmer, designer & test designer.

• Need to design specific tests – possibility of bugs.

ref boris beizer16

Transaction-flow Structure U2-B

Reasons for UnstructurednessReasons for Unstructuredness

1.1. Processes involve Human UsersProcesses involve Human Users

1.1. Part of Flow from External SystemsPart of Flow from External Systems

1.1. Errors, Failures, Malfunctions & Recovery ActionsErrors, Failures, Malfunctions & Recovery Actions

1.1. Transaction Count, Complexity. Customer & EnvironmentTransaction Count, Complexity. Customer & Environment

ref boris beizer17

Transaction-flow Structure U2-B

Reasons for Unstructuredness …Reasons for Unstructuredness …

5.5. New Transactions, & ModificationsNew Transactions, & Modifications

6.6. Approximation to RealityApproximation to Reality

• Attempt to StructureAttempt to Structure

ref boris beizer18

Transaction - Flow Testing - Steps U2-B

First, Build / Obtain Transaction FlowsFirst, Build / Obtain Transaction Flows

• Represent ExplicitlyRepresent Explicitly

• Design details the Main Tr-FlowsDesign details the Main Tr-Flows

• Create From PDLCreate From PDL

• HIPO charts & Petrinet RepresentationsHIPO charts & Petrinet Representations

Objective – Trace the transactionObjective – Trace the transaction

ref boris beizer19

1.1. Inspections, Reviews & WalkthroughsInspections, Reviews & Walkthroughs

Start From Preliminary DesignStart From Preliminary Design

1.1. Conducting WalkthroughsConducting Walkthroughs

• Discuss enough Transaction Types (Discuss enough Transaction Types (98% Transactions))

• User needs & Functional terms User needs & Functional terms (Design independent)

• Traceability to RequirementsTraceability to Requirements

ref boris beizer20

1.1. Inspections, Reviews & Walkthroughs …Inspections, Reviews & Walkthroughs …

2.2. Design Tests for C1 + C2 coverageDesign Tests for C1 + C2 coverage

3.3. Additional Coverage (> C1+C2)Additional Coverage (> C1+C2)

• Paths with loops, extreme values, domain boundaries• Weird cases, long & potentially troublesome Tr.

4.4. Design Test cases for Tr. Splits & mergersDesign Test cases for Tr. Splits & mergers

5.5. Publish Selected Test Paths earlyPublish Selected Test Paths early

6.6. Buyer’s Acceptance – functional & acceptance testsBuyer’s Acceptance – functional & acceptance tests

ref boris beizer21

Transaction - Flow Testing Techniques U2-B

2.2. Path SelectionPath Selection

1.1. Covering Set (C1+C2) of Functionally Sensible Tr.Covering Set (C1+C2) of Functionally Sensible Tr.

2.2. Add Difficult PathsAdd Difficult Paths

• Review with designers & implementers

• Exposure of interface problems & duplicated processing

• Very few Implementation bugs may remain

Transaction-flow Path Covering Set belongs inSystem Feature Tests

ref boris beizer22

3.3. SensitizationSensitization

1. Functionally Sensible Paths – Simple

2. Error, Exception, External Protocol Interface Paths - Difficult

Testing Tr.–Flows with External Interfaces

• Use patches & break points, mistune, and break the rules,

ref boris beizer23

4.4. InstrumentationInstrumentation

1. Link Counters are not Useful.

2. Need

• Trace

• Queues on which Tokens resided

• Entries to & Exits from Dispatcher

• A Running Log

Make Instrumentation as part of System Design

ref boris beizer24

5.5. Test Data basesTest Data bases

1. Design & Maintenance of a Test Data base - Effort

2. Mistakes

• Unawareness about design of a centrally administered test DB

• Test DB design by Testers

• Using one DB for all tests (need 4 to 5)

Need experienced System & Test Designers

ref boris beizer25

6.6. Test ExecutionTest Execution

1. Use Test Execution Automation

2. Have to do a large # of Tests for C1+C2 coverage

ref boris beizer26

Transaction - Flow Testing - Implementation U2-B

1.1. Transaction based systemsTransaction based systems• TCBTCB

1.1. Centralized, Common Processing QueuesCentralized, Common Processing Queues• Just O(n) Queues for Links of O(nJust O(n) Queues for Links of O(n22))

2.2. Transaction DispatcherTransaction Dispatcher• Uses tables & Finite State MachinesUses tables & Finite State Machines

3.3. Recovery & Other LogsRecovery & Other Logs• Key events in Tr – Flow Key events in Tr – Flow

4.4. Self-Test SupportSelf-Test Support• Privileged modes in Transaction control tablesPrivileged modes in Transaction control tables

Data - Flow Testing - Basics U2

AnomalyAnomaly

Unreasonable processing on dataUnreasonable processing on data

• Use of data object before it is definedUse of data object before it is defined• Defined data object is not usedDefined data object is not used

• Data Flow Testing (DFT) uses Control Flow Graph (CFG) Data Flow Testing (DFT) uses Control Flow Graph (CFG) to explore dataflow anomalies.to explore dataflow anomalies.

• DFT Leads to testing strategies between PDFT Leads to testing strategies between P and P1 / P2 and P1 / P2

Definition:Definition:

DFT is a family of test strategies based on selecting paths DFT is a family of test strategies based on selecting paths through the program’s control flow in order to explore the through the program’s control flow in order to explore the sequence of events related to the status of data objects.sequence of events related to the status of data objects.

Example:Example:

Pick enough paths to assure that every data item has been Pick enough paths to assure that every data item has been initialized prior to its use, or that all objects have been used for initialized prior to its use, or that all objects have been used for something.something.

MotivationMotivation

• Confidence in the programConfidence in the program

• Data dominated designData dominated design. . Code migrates to data..

• Source Code for Data DeclarationsSource Code for Data Declarations

• Data flow Machines vs Von Neumann’sData flow Machines vs Von Neumann’s

• Abstract M I M D Abstract M I M D • Language & compiler take care of parallel Language & compiler take care of parallel

computationscomputations

Data - Flow Testing - Basics - Motivation U2

Program Control flow with Von Neumann’s paradigmProgram Control flow with Von Neumann’s paradigm Given m, n, p, q, find eGiven m, n, p, q, find e..

e = (m+n+p+q) * (m+n-p-q)e = (m+n+p+q) * (m+n-p-q)

a := m + na := m + nb := p + qb := p + qc := a + bc := a + bd := a - bd := a - b

e := c * de := c * d

a = n+m

Multiple representations of control flow graphs possible.

Program Flow using Data Flow Machines paradigmProgram Flow using Data Flow Machines paradigm

BEGIN PAR DO

READ m, n, n, p, qEND PARPAR DO

a := m+nb := p+q

END PARPAR DO

c := a+bd := a-b

END PARPAR DO

e := c * dEND PAREND

n m p q

a := m+n b := p+q

c := a+b

The interrelations among the data items remain same.The interrelations among the data items remain same.

d := a-b

e := c * d

• Control flow graphControl flow graph• Multiple representationsMultiple representations

• Data Flow GraphData Flow Graph A spec. for relations among the data objects. A spec. for relations among the data objects.

Covering DFG => Explore all relations under some test.Covering DFG => Explore all relations under some test.

AssumptionsAssumptions

• Problems in a control flowProblems in a control flow

• Problems with data objectsProblems with data objects

Data Flow Graphs (DFG)Data Flow Graphs (DFG)

• It is a graph with nodes & directed links

• Test the Von Neumann way - Convert to a CFG

Annotate : program actions (weights)

Data Object State & UsageData Object State & Usage

Program Actions (d, k, u):Program Actions (d, k, u):

Defined (created)Defined (created) - - explicitly or implicitlyexplicitly or implicitly (d)(d)

Killed (released)Killed (released) - - directly or indirectlydirectly or indirectly (k)(k)

UsedUsed -- (u)(u)

• In a calculation In a calculation -- (c)(c)

• In a predicateIn a predicate -- directly or indirectlydirectly or indirectly (p)(p)

Data Flow AnomaliesData Flow Anomalies

A Two letter sequence of Actions (d, k, u)A Two letter sequence of Actions (d, k, u)

dddd : : harmless, suspiciousharmless, suspicious

dkdk :: probably a bug.probably a bug.

dudu :: normalnormal

kdkd :: normalnormal

kkkk :: harmless, but probably a bugharmless, but probably a bug

kuku :: a buga bug

udud :: normal. Redefinition.normal. Redefinition.

ukuk :: normalnormal

uuuu :: normalnormal

Action

Program Flow using Data Flow Machines paradigmProgram Flow using Data Flow Machines paradigm

BEGIN PAR DO

READ m, n, n, p, qEND PARPAR DO

a := m+nb := p+q

END PARPAR DO

c := a+bd := a-b

END PARPAR DO

e := c * dEND PAREND

n m p q

a := m+n b := p+q

c := a+b

The interrelations among the data items remain same.The interrelations among the data items remain same.

d := a-b

e := c * d

Data - Flow Testing - Basics – Data Flow Anomalies U2

Actions on data objectsActions on data objects

-- no action from no action from START to this pointSTART to this pointFrom this point till the From this point till the EXITEXIT

- d- d normalnormal

- u- u anomalyanomaly - k- k anomalyanomaly

k-k- normalnormal u -u - normalnormal - possibly an anomaly - possibly an anomaly d -d - possibly anomalouspossibly anomalous

Data Flow Anomaly State graphData Flow Anomaly State graph

• Data Object StateData Object State

• K, D, U, AK, D, U, A

• Processing StepProcessing Step

• k, d, uk, d, u

• Object stateObject state• Unforgiving Data flow state graphUnforgiving Data flow state graph

d, k, u

DefinedUsed

Undefined

Anomalous

Forgiving Data flow state graphForgiving Data flow state graph

A DD, DK, KU

Data Flow State GraphsData Flow State Graphs

• Differ in processing of anomaliesDiffer in processing of anomalies

• Choice depends on Choice depends on Application, language, contextApplication, language, context

Static vs Dynamic Anomaly DetectionStatic vs Dynamic Anomaly Detection

• Static analysis of data flows

• Dynamic analysisIntermediate data valuesIntermediate data values

Insufficiency of Static Analysis (for Data flow)Insufficiency of Static Analysis (for Data flow)

1. Validation of Dead Variables

2. Validation of pointers in Arrays

3. Validation of pointers for Records & pointers

1. Dynamic addresses for dynamic subroutine calls

2. Identifying False anomaly on an unachievable path

1. Recoverable anomalies & Alternate state graph

2. Concurrency, Interrupts, System Issues

Data Flow ModelData Flow Model

• Based on CFG

• CFG annotated with program actions

• link weights : dk, dp, du etc..

• Not same as DFG

• For each variable and data object

Data - Flow Testing - Basics : Data Flow Model U2

Procedure to Build:Procedure to Build:

1.1. Entry & Exit nodesEntry & Exit nodes

1.1. Unique node identificationUnique node identification

1.1. Weights on out linkWeights on out link

2.2. Predicated nodesPredicated nodes

3.3. Sequence of linksSequence of links

1.1. JoinJoin

2.2. Concatenate weightsConcatenate weights

3.3. The converseThe converse

Data - Flow Testing - Basics : Data Flow Model U2

Example:Example: a an – 1 – 1Z = b + ---------Z = b + ---------

START a - 1 a - 1INPUT a, b, nZ := 0IF a = 1 THEN Z := 1GOTO DONE1r := 1 c := 1POWER:

c := c * ar := r + 1IF r <= n THEN GO TO POWERZ := (c – 1) / (a – 1)

DONE1:Z := b + Z

Data - Flow Testing - Basics – Data Flow model U2

CFG for the ExampleCFG for the Example

Read a,b,nZ := 0 Z := 1 Z := b + Z

Z := (c-1)/(a-1)

a = 1?P1

r < n ?P2

r := 1 c:=1 r := r+1, c:= c*a

CFG annotated – Data Flow Model for ZCFG annotated – Data Flow Model for Z

d or kd cd or ckd

a = 1?P1

r < n ?P2

d or kd

CFG annotated – Data Flow Model for cCFG annotated – Data Flow Model for c

ckd or kd

a = 1?P1

r < n ?P2

CFG annotated – Data Flow Model for CFG annotated – Data Flow Model for rr

ckd or kd

a = 1?P1

r < n ?P2

CFG annotated – Data Flow Model for CFG annotated – Data Flow Model for bb

a = 1?P1

r < n ?P2

CFG annotated – Data Flow Model for CFG annotated – Data Flow Model for nn

a = 1?P1

r < n ?P2

CFG annotated – Data Flow Model for CFG annotated – Data Flow Model for aa

a = 1?P1

r < n ?P2

A DFM for each variable A DFM for each variable

Single DFM for multiple variablesSingle DFM for multiple variables

Use weights subscripted with variablesUse weights subscripted with variables

Data - Flow Testing – Data Flow Testing Strategies U2

• A structural testing strategy (A structural testing strategy (path testingpath testing))

• Add, data flow strategies Add, data flow strategies with link weightswith link weights

• Test path segments to have a Test path segments to have a ‘d’‘d’ (or (or u, k, du, dku, k, du, dk))

DEFINITIONSDEFINITIONS

• w.r.t. a variable or data object ‘v’w.r.t. a variable or data object ‘v’• Assume all DF paths are achievableAssume all DF paths are achievable

1.1. Definition-clear path segmentDefinition-clear path segmentno no k, kdk, kd

2.2. Loop-free path segmentLoop-free path segment

2.2. Simple path segmentSimple path segment

3.3. dudu path from node path from node ii to to kk

• definition-clear & simple cdefinition-clear & simple c• definition-clear & loop-free pdefinition-clear & loop-free p

DFT StrategiesDFT Strategies

1.1. All-du paths (ADUP)All-du paths (ADUP)

2.2. All uses (AU) strategyAll uses (AU) strategy

3.3. All p-uses/some c-uses and All c-uses/some p-usesAll p-uses/some c-uses and All c-uses/some p-uses

1.1. All Definitions StrategyAll Definitions Strategy

1.1. All p-uses, All c-uses StrategyAll p-uses, All c-uses Strategy

Purpose:Purpose:

Test Design, Develop Test CasesTest Design, Develop Test Cases

1.1. All-du paths (ADUP)All-du paths (ADUP)

• Strongest DFTStrongest DFT

• EveryEvery dudu path for every variable for every definition to every path for every variable for every definition to every use use

2.2. All uses (AU) strategyAll uses (AU) strategy

• At least one At least one definition clear path segment from every definition clear path segment from every definition of every variable to every use of that definition be definition of every variable to every use of that definition be exercised under some test.exercised under some test.

• At least one path segment from every definition to every use that can be reached from that definition.

3.3. All p-uses/some c-uses and All c-uses/some All p-uses/some c-uses and All c-uses/some p-usesp-uses

• APU + cAPU + c

• Stronger than P2Stronger than P2

• ACU + pACU + p

• Weaker than P2Weaker than P2

4.4. All Definitions Strategy (AD)All Definitions Strategy (AD)

• Cover every definition by Cover every definition by at least oneat least one p or c p or c

• Weaker than ACU + p and APU + cWeaker than ACU + p and APU + c

5.5. All-Predicate Uses, All-Computational Uses All-Predicate Uses, All-Computational Uses StrategyStrategy

• APU :APU :

• Include definition-free path for every definition of every Include definition-free path for every definition of every variable from the definition to predicate use.variable from the definition to predicate use.

• ACU :ACU :

• Include for every definition of every variable include at Include for every definition of every variable include at least one definition-free path from the definition to every least one definition-free path from the definition to every computational use.computational use.

Ordering the strategies Ordering the strategies

All Paths

All du Paths

All-uses Paths (AU)

All-c / some-p (ACU+p)

All c uses (ACU)

All-p / some-c APU+c

All P-uses APU

All Branches P2

All Stmts P1

All Defs AD

Testing, Maintenance & Debugging in the Data Flow contextTesting, Maintenance & Debugging in the Data Flow context

Slicing:

• A static program A static program sliceslice is a part of a program defined wrt is a part of a program defined wrt a variable ‘a variable ‘vv’ and a statement ‘’ and a statement ‘ss’; It is the set of all ’; It is the set of all statements that could affect the value of ‘statements that could affect the value of ‘vv’ at stmt ‘’ at stmt ‘ss’.’.

Stmt1 var vstmt2Stmt3 var vStmt4 var v

Stmt s var v

Dicing:Dicing:

• A program dice is a part of slice in which all stmts. which are known to be correct have been removed.

• Obtained from ‘slice’ by incorporating correctness information from testing / debugging.

Debugging:Debugging:

• Select a slice.

• Narrow it to a dice.

• Refine the dice till it’s one faulty stmt.

Dynamic Slicing:Dynamic Slicing:

• Refinement of static slicingRefinement of static slicing

• Only achievable paths to the stmt ‘s’ in question are Only achievable paths to the stmt ‘s’ in question are included.included.

Slicing methods bring together testing, maintenance & debugging..

Data - Flow Testing - – Data Flow Testing Strategies U2

Application of DFTApplication of DFT

• Comparison Random Testing, P2, AU - by Ntafos

• AU detects more bugs than

• P2 with more test cases• RT with less # of test cases

• Comparison of P2, AU - by Sneed

• AU detects more bugs with 90% Data Coverage Requirement.

• Comparison of # test cases for ACU, APU, AU & ADUP

• by Weyuker using ASSET testing system

• Test Cases Normalized. t = a + b * d d = # binary decisions

• At most d+1 Test Cases for P2 loop-free

• # Test Cases / Decision

ADUP > AU > APU > ACU > revised-APU

Comparison of # test cases for ACU, APU, AU & ADUP by Shimeall & Levenson

Test Cases Normalized. t = a + b * d (d = # binary decisions)

At most d+1 Test Cases for P2 loop-free

# Test Cases / Decision

ADUP ~ ½ APU*

AP ~ AC

DFT vs P1, P2

• DFT is Effective

• Effort for Covering Path Set ~ Same

• DFT Tracks the Coverage of Variables

• Test Design is similar

DFT - TOOLSDFT - TOOLS

• Cost-effective development

• Commercial tools :

• Can possibly do Better than Commercial Tools

• Easier Integration into a Compiler

• Efficient Testing

Data - Flow Testing – Questions from the previous year’s exams U2

1. How is data flow testing (DFT) helpful in fulfilling gaps in path testing?

2. Explain the data flow Graphs (DFG).

3. How can anomaly be detected? Explain different types of data flow anomalies and Data flow Anomaly State Graphs.

4. Write applications of Data Flow Testing.

5. Name and explain Data flow testing strategies.

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