software testing unit 3

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Unit 3 – Data Flow Compiled with reference from: Software Testing Techniques: Boris Beizer Craft of Software Testing: Brain Marrick

Narasimha Rao.P

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

• Concepts of Data flows • Data-flow testing strategies

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U2

Data - Flow Testing - Basics Contents • 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

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Data - Flow Testing - Basics

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Anomaly Unreasonable processing on data • •

Use of data object before it is defined Defined data object is not used

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

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DFT Leads to testing strategies between P∝ and P1 / P2

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Data - Flow Testing - Basics

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Definition:

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

Example:

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

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Data - Flow Testing - Basics

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Motivation • Confidence in the program • Data dominated design.

Code migrates to data..

• Source Code for Data Declarations • Data flow Machines vs Von Neumann’s • Abstract M I M D • Language & compiler take care of parallel computations

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Data - Flow Testing - Basics - Motivation

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Program Control flow with Von Neumann’s paradigm Given m, n, p, q, find e. e = (m+n+p+q) * (m+n-p-q) a = n+m

a := m + n b := p + q c := a + b d := a - b

b=p+q c=a+b

e := c * d d=a-b e=c*d

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Multiple representations of control flow graphs possible. ref boris beizer

Data - Flow Testing - Basics - Motivation

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Program Flow using Data Flow Machines paradigm BEGIN PAR DO READ m, n, n, p, q END PAR PAR DO a := m+n b := p+q END PAR PAR DO c := a+b d := a-b END PAR PAR DO e := c * d END PAR END

n

p

m

a := m+n

q

b := p+q

c := a+b

d := a-b

e := c * d

The interrelations among the data items remain same.

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Data - Flow Testing - Basics - Motivation

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• Control flow graph • Multiple representations

• Data Flow Graph A spec. for relations among the data objects. Covering DFG => Explore all relations under some test.

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Data - Flow Testing - Basics

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Assumptions

• Problems in a control flow • Problems with data objects

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Data - Flow Testing - Basics Data Flow Graphs

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(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 - Flow Testing - Basics

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Data Object State & Usage Program Actions (d, k, u):

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Defined (created)

-

explicitly or implicitly

(d)

Killed (released)

-

directly or indirectly

(k)

Used

-

(u)

• In a calculation

-

(c)

• In a predicate

-

directly or indirectly

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(p)

Data - Flow Testing - Basics

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

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dd

:

harmless, suspicious

dk

:

probably a bug.

du

:

normal

kd

:

normal

kk

:

harmless, but probably a bug

ku

:

a bug

ud

:

normal.

uk

:

normal

uu

:

normal

A

Action

Redefinition.

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Data - Flow Testing - Basics - Motivation

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Program Flow using Data Flow Machines paradigm BEGIN PAR DO READ m, n, n, p, q END PAR PAR DO a := m+n b := p+q END PAR PAR DO c := a+b d := a-b END PAR PAR DO e := c * d END PAR END

n

p

m

a := m+n

q

b := p+q

c := a+b

d := a-b

e := c * d

The interrelations among the data items remain same.

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Data - Flow Testing - Basics – Data Flow Anomalies Actions on data objects -

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no action from START to this point From this point till the EXIT

-d

normal

-u

anomaly

-k

anomaly

k-

normal

u-

normal

d-

possibly anomalous

-

possibly an anomaly

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Data - Flow Testing - Basics

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Data Flow Anomaly State graph • Data Object State • K, D, U, A • Processing Step • k, d, u

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Data - Flow Testing - Basics

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Data Flow Anomaly State graph • Object state • Unforgiving Data flow state graph Undefined

K k, u d

u u

U d Used

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D Defined

d, k

A

Anomalous

d, k, u

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Data - Flow Testing - Basics

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Data Flow Anomaly State graph Forgiving Data flow state graph k

u

u

K

A ⇒ DD, DK, KU

KU k

u

k

DK

d k d

u u

U

D

k

d

d DD u

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Data - Flow Testing - Basics

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Data Flow State Graphs • Differ in processing of anomalies • Choice depends on Application, language, context

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Data - Flow Testing - Basics

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Static vs Dynamic Anomaly Detection

• Static analysis of data flows • Dynamic analysis Intermediate data values

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Data - Flow Testing - Basics

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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 Testing - Basics

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Data Flow Model

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•

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

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Procedure to Build: 1. Entry & Exit nodes 1. Unique node identification 1. Weights on out link 2. Predicated nodes 3. Sequence of links 1. Join 2. Concatenate weights 3. The converse

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ref boris beizer

Data - Flow Testing - Basics : Data Flow Model Example:

U2

an – 1 Z = b + --------a - 1

START INPUT a, b, n Z := 0 IF a = 1 THEN Z := 1 GOTO DONE1 r := 1 c := 1 POWER: c := c * a r := r + 1 IF r <= n THEN GO TO POWER Z := (c – 1) / (a – 1) DONE1: Z := b + Z END

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Data - Flow Testing - Basics – Data Flow model

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CFG for the Example Read a,b,n Z := 0 1

Z := 1 2

Z := b + Z 5

a = 1?

P1 Y

r := 1 c:=1

6

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

3

P2 r := r+1, c:= c*a

4

r<n? Y

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Data - Flow Testing - Basics – Data Flow model

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CFG annotated – Data Flow Model for Z

1

d or kd

d

2 P1

cd or ckd 5

a = 1?

6

Y d or kd

3

P2

4

r<n? Y

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Data - Flow Testing - Basics – Data Flow model

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CFG annotated – Data Flow Model for c

1

2

5

a = 1?

P1

6

Y c-

-d 3

ckd or kd P2

4

r<n? Y

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Data - Flow Testing - Basics – Data Flow model

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CFG annotated – Data Flow Model for r

1

2

5

a = 1?

P1

6

Y p-

-d 3

ckd or kd P2

4

r<n? Y

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ref boris beizer

Data - Flow Testing - Basics – Data Flow model

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CFG annotated – Data Flow Model for b

1

d

2 P1

c

5

a = 1?

6

Y

3

P2

4

r<n? Y

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Data - Flow Testing - Basics – Data Flow model

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CFG annotated – Data Flow Model for n

1

d

2 P1

5

a = 1?

6

Y p-

3

P2

4

r<n? Y

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Data - Flow Testing - Basics – Data Flow model

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CFG annotated – Data Flow Model for a

1

d

2 P1

5

a = 1?

6

p c-

3

c

P2

4

r<n? Y

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Data - Flow Testing - Basics – Data Flow model

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A DFM for each variable Single DFM for multiple variables Use weights subscripted with variables

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Data - Flow Testing – Data Flow Testing Strategies

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•

A structural testing strategy (path testing)

•

Add, data flow strategies with link weights

•

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

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Data - Flow Testing – Data Flow Testing Strategies DEFINITIONS • •

w.r.t. a variable or data object ‘v’ Assume all DF paths are achievable 1. Definition-clear path segment no k, kd 2. Loop-free path segment 1. Simple path segment 2. du path from node i to k • •

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definition-clear & simple definition-clear & loop-free

c p ref boris beizer

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Data - Flow Testing – Data Flow Testing Strategies DFT Strategies 1. All-du paths (ADUP) 2. All uses (AU) strategy 3. All p-uses/some c-uses and

All c-uses/some p-uses

1. All Definitions Strategy 1. All p-uses, All c-uses Strategy

Purpose:

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Test Design, Develop Test Cases ref boris beizer

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Data - Flow Testing – Data Flow Testing Strategies

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1. All-du paths (ADUP) •

Strongest DFT

•

Every du path for every variable for every definition to every use

2. All uses (AU) strategy

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•

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

•

At least one path segment from every definition to every use that can be reached from that definition. ref boris beizer

Data - Flow Testing – Data Flow Testing Strategies

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3. All p-uses/some c-uses and All c-uses/some p-uses

•

APU + c •

•

ACU + p •

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Stronger than P2

Weaker than P2

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Data - Flow Testing – Data Flow Testing Strategies 4. All Definitions Strategy (AD)

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•

Cover every definition by at least one

•

Weaker than ACU + p

and

p or c

APU + c

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Data - Flow Testing – Data Flow Testing Strategies 5. All-Predicate Uses, Strategy •

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

ACU : •

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All-Computational Uses

APU : •

•

U2

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

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Data - Flow Testing – Data Flow Testing Strategies

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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 Defs

AD All P-uses

APU

All Branches P2 All Stmts P1

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Data - Flow Testing – Data Flow Testing Strategies

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Testing, Maintenance & Debugging in the Data Flow context

Slicing: • A static program slice is a part of a program defined wrt a variable ‘v’ and a statement ‘s’; It is the set of all statements that could affect the value of ‘v’ at stmt ‘s’.

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Stmt1 stmt2 Stmt3 Stmt4

var v

Stmt s

var v

var v var v

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Data - Flow Testing – Data Flow Testing Strategies

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Testing, Maintenance & Debugging in the Data Flow context

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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Data - Flow Testing – Data Flow Testing Strategies

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Testing, Maintenance & Debugging in the Data Flow context

Debugging: • Select a slice. • Narrow it to a dice. • Refine the dice till it’s one faulty stmt.

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Data - Flow Testing – Data Flow Testing Strategies

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Testing, Maintenance & Debugging in the Data Flow context

Dynamic Slicing: • Refinement of static slicing • Only achievable paths to the stmt ‘s’ in question are included.

Slicing methods bring together testing, maintenance & debugging.

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Data - Flow Testing - – Data Flow Testing Strategies Application of DFT • Comparison Random Testing, P2, AU •

AU detects more bugs than • •

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

• Comparison of P2, AU •

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- by Ntafos

- by Sneed

AU detects more bugs with 90% Data Coverage Requirement.

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Data - Flow Testing - – Data Flow Testing Strategies

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Application of DFT • Comparison of # test cases for ACU, APU, AU & ADUP • by Weyuker using ASSET testing system •

Test Cases Normalized.

•

At most d+1 Test Cases for P2

•

# Test Cases / Decision ADUP > AU

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>

t=a+b*d

d = # binary decisions loop-free

APU > ACU > revised-APU

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Data - Flow Testing - – Data Flow Testing Strategies

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Application of DFT Comparison of # test cases for ACU, APU, AU & ADUP by Shimeall & Levenson Test Cases Normalized.

t=a+b*d

At most d+1 Test Cases for P2

(d = # binary decisions) loop-free

# Test Cases / Decision ADUP

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~ ½ APU*

AP ~ AC ref boris beizer

Data - Flow Testing - – Data Flow Testing Strategies Application of DFT 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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Data - Flow Testing - – Data Flow Testing Strategies DFT - 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 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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