unit 3 of software testing borisbeizer ppt

Narasimha Rao.PNarasimha Rao.P

Compiled with reference from:Compiled with reference from: Software Testing Techniques: Boris BeizerSoftware Testing Techniques: Boris Beizer Craft of Software Testing: Brain MarrickCraft of Software Testing: Brain Marrick

Unit 3 – Data FlowUnit 3 – Data Flow

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Data - Flow Testing - BasicsWe will see in this part of Unit 2:

• Concepts of Data flows

• Data-flow testing strategies

U2

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Data - Flow Testing - BasicsContents

• Synopsis

• Basics• Intro to Data flow, data flow graphs• Motivation & Assumption• Data flow model

• Data Flow Testing Strategies• General strategy• Definitions• Strategies:

• Slicing, Dicing, Data flow, Effectiveness

• Application of DFT, Tools & Effectiveness

U2

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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

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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.

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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

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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

b=p+q

c=a+b

d=a-b

e=c*d

Multiple representations of control flow graphs possible.

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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

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• 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.

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AssumptionsAssumptions

• Problems in a control flowProblems in a control flow

• Problems with data objectsProblems with data objects

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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)

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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)

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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

A

Action

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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

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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

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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

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

K

DU Aud

u

k, u

d, k

d, k, u

d

DefinedUsed

Undefined

Anomalous

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Forgiving Data flow state graphForgiving Data flow state graph

K

DU

DD

ud

ud

d

d

A DD, DK, KU

u

kKU

DK

k

u

u

kd

k

u

k

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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

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Static vs Dynamic Anomaly DetectionStatic vs Dynamic Anomaly Detection

• Static analysis of data flows

• Dynamic analysisIntermediate data valuesIntermediate data values

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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

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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

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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

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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

END

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Data - Flow Testing - Basics – Data Flow model U2

CFG for the ExampleCFG for the Example

1 2

3 4

5 6

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

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

a = 1?P1

r < n ?P2

Y

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

Y

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

1 2

3 4

5 6d

d or kd cd or ckd

a = 1?P1

r < n ?P2

Y

Y

d or kd

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

1 2

3 4

5 6

-d

ckd or kd

a = 1?P1

r < n ?P2

Y

Y

c-

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

1 2

3 4

5 6

-d

ckd or kd

a = 1?P1

r < n ?P2

Y

Y

p-

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

1 2

3 4

5 6d

a = 1?P1

r < n ?P2

Y

Y

c

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

1 2

3 4

5 6d

a = 1?P1

r < n ?P2

Y

Yp-

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

1 2

3 4

5 6d

a = 1?P1

r < n ?P2

Y

pc-

c

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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

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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))

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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

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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

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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.

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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

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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

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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.

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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

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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

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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.

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Debugging:Debugging:

• Select a slice.

• Narrow it to a dice.

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

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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..

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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.

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• 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

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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

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DFT vs P1, P2

• DFT is Effective

• Effort for Covering Path Set ~ Same

• DFT Tracks the Coverage of Variables

• Test Design is similar

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DFT - TOOLSDFT - TOOLS

• Cost-effective development

• Commercial tools :

• Can possibly do Better than Commercial Tools

• Easier Integration into a Compiler

• Efficient Testing

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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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Data - Flow Testing U-2C

To Unit 4 … Domain Testing

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unit 3 of software testing borisbeizer ppt