Hybrid Multi-level Crossover for Unit Test Case Generation (SSBSE 2021)

HYBRID MULTI-LEVEL CROSSOVER
FOR UNIT TEST CASE GENERATION
1
Pouria Derakhshanfar Annibale Panichella
Mitchell Olsthoorn

Test4
SEARCH-BASED UNIT TEST GENERATION 2
JAR
Application
Evolutionary-based algorithm
Random
initialization
Test0
Test5
Test1
Test3
Test2
…
Evaluate fitness
of tests
Search objective(s)
Fitness value(s)
Fitness value(s)
Fitness value(s)
Fitness value(s)
Fitness value(s)
Fitness value(s)
Selection
Class under tests
CLASS

JAR
Application
Random
initialization
Evaluate fitness
of tests
Selection Crossover
Test1
Test4
Offspring1
Offspring2
Class under tests
CLASS

SEARCH-BASED CRASH REPRODUCTION 5
JAR
Application
Random
initialization
Evaluate fitness
of tests
Selection Crossover
Test1
Test4
Offspring1
Offspring2
Mutation
Or Or
Edit Remove Add
Or Or
Edit Remove Add
Class under tests
CLASS

JAR
Application
Random
initialization
Evaluate fitness
of tests
Selection Crossover Mutation
Reinsertion
NewTest0
NewTest5
NewTest4
NewTest1
NewTest3
NewTest2
…
Class under tests
CLASS

JAR
Application
Random
initialization
Evaluate fitness
of tests
Selection Crossover Mutation
Reinsertion
Class under tests
CLASS
Test1
Test4
Offspring1
Offspring2
!
No crossover for input data

CROSSOVER OPERATOR 8
public void test1 () {
Fraction f0 = new Fraction (2, 3);
Fraction f1 = new Fraction (2, -1);
f0. divideBy (f1);
f0.add ( Fraction.ZERO);
}
Parent 1
f0.add (f1);
f0.pow (2.0);
}
Parent 2

f0. divideBy (f1);
}
Parent 1
f0.add (f1);
f0.pow (2.0);
}
Parent 2
f0.add (f1);
f0.pow (2.0);
}
Offspring 1
f0. divideBy (f1);
}
Offspring 2

f0. divideBy (f1);
}
Parent 1
f0.add (f1);
f0.pow (2.0);
}
Parent 2
f0.add (f1);
f0.pow (2.0);
}
Offspring 1
f0. divideBy (f1);
}
Offspring 2
Hybrid Multi-level Crossover (HMX) operator
Traditional test-level crossover + Data-level recombination

OUR SOLUTION 11
HMX: HYBRID MULTI-LEVEL CROSSOVER
input: Two parent test cases P1 and P2
output: Two offspring test cases O1 and O2
begin
O1,O2 <—— SINGLE-POINT-CROSSOVER(P1,P2);
// detect candidate data inputs for data-level crossover
C1,C2,M1,M2 <—— DETECT-CORSSOVER-CANDIDATES(O1,O2)
foreach SIG ∈ C1.keys ∪ C2.keys do
// choose random constructor with same signature
S1 <—— random.choice(C1[SIG])
O1,O2 <—— DATA-CROSSOVER(O1,O2,PARAMS(S1),PARAMS(S2))
done
foreach SIG ∈ M1.keys ∪ M2.keys do
// choose random method with same signature
S1 <—— random.choice(M1[SIG])
done
return O1,O2;
end

TEST-LEVEL CROSSOVER OPERATOR 12
begin
done
done
return O1,O2;
end

TEST-LEVEL CROSSOVER OPERATOR 13
f0. divideBy (f1);
}
P1
f0.add (f1);
f0.pow (2.0);
}
P2
f0.add (f1);
f0.pow (2.0);
}
O1
f0. divideBy (f1);
}
O2

DETECT CROSSOVER CANDIDATES 14
begin
done
done
return O1,O2;
end
Data-level crossover can not be applied
to every combination of input data:
A. Performing crossover on input
data with different types is
not logical.
B. Applying crossover on input
parameters with the same data
type that belong to different
methods may lead to generate
offsprings that are farther from
the desired search objective.

f0.add (f1);
f0.pow (2.0);
}
O1
f0. divideBy (f1);
}
O2
C1
Key
(Signature of constructors
invoked in both O1 and O2)
Value
(list of constructors called in O1
that has the same signature)
Fraction|<init>(int, int)
new Fraction (2, 3)
new Fraction (2, -1)
Each method or constructor implemented in a project has a unique string called Signature.
Signature format:
CLASS_NAME|FUNCTION_NAME(PARAM1_TYPE, PARAM2_TYPE, …)RETURN_TYPE
C2
Key
Value
new Fraction (3, 1)
new Fraction (1, 3)

f0.add (f1);
f0.pow (2.0);
}
O1
f0. divideBy (f1);
}
O2
M1
Key
(Signature of methods
Value
(list of methods called in O1
Fraction|add(Fraction)V add(f1)
Each method or constructor implemented in a project has a unique string called Signature.
Signature format:
CLASS_NAME|FUNCTION_NAME(PARAM1_TYPE, PARAM2_TYPE, …)RETURN_TYPE
M2
Key
(Signature of methods
Value
(list of methods called in O2
Fraction|add(Fraction)V add(Fraction.ZERO)

DATA CROSSOVER 17
begin
done
done
return O1,O2;
end

DATA CROSSOVER 18
f0.add (f1);
f0.pow (2.0);
}
O1
f0. divideBy (f1);
}
O2
C1
Key
Value
new Fraction (2, 3)
new Fraction (2, -1)
C2
Key
Value
new Fraction (3, 1)
new Fraction (1, 3)
DATA-CROSSOVER(…)

DATA CROSSOVER 19
begin
done
done
return O1,O2;
end

DATA CROSSOVER 20
1. Numerical data types —> Simulated Binary Crossover (SBX)
2. Strings —> String Crossover
HMX SUPPORTS DATA RECOMBINATION FOR TWO DATA TYPES

DATA CROSSOVER 21
SIMULATED BINARY CROSSOVER (SBX)
O1: new Fraction (1.5, -1)
O2: new Fraction (3.8, 1)
Crossover (2, 3)
Crossover (-1, 1)
Deb, K., Sindhya, K., Okabe, T.: Self-adaptive simulated binary crossover for real parameter optimization. In:
GECCO 2007.
1.5
3.8
O1: new Fraction (2, -1)
O2: new Fraction (3, 1)

DATA CROSSOVER 22
SINGLE-POINT STRING CROSSOVER
String 1:
lor em
String 2:
ipsum
lorsum
ipem
McMinn, P.: Search-based software test data generation: A survey. Software Testing
Verification and Reliability. (2004)

EMPIRICAL EVALUATION 23
1. State-of-the-art search-based unit test generation: EvoSuite running DynaMOSA with default
parameters
• Baseline: EvoSuite + SPX
• Novel approach: EvoSuite + HMX
2. Benchmark:
• 9 projects, which commonly use numeric and string input data.
• 100 non-trivial classes under test that accept at least one string or numeric value as input
parameter.
3. Search budget = 5 minutes
4. Repeated each execution for 100 times
5. in total: 23,200 executions
EMPIRICAL EVALUATION

RQ1: TO WHAT EXTENT DOES HMX IMPROVE STRUCTURAL COVERAGE
COMPARED TO THE SINGLE-POINT CROSSOVER? 24
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1.00
HMX SPX
Crossover
Line
coverage
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0.00
0.25
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0.75
1.00
HMX SPX
Crossover
Branch
coverage
OVERVIEW OF STRUCTURAL COVERAGE COMPARING
HMX TO SPX

RQ1: TO WHAT EXTENT DOES HMX IMPROVE STRUCTURAL COVERAGE
COMPARED TO THE SINGLE-POINT CROSSOVER? 25
Pairwise comparison of impact of HMX on structural coverage with a small, medium, and
large effect size and a statistical significance <0.05
1. Impact factor measurement: Vargha-Delaney Â12 statistic
2. Assess the significance of results: non-parametric Wilcoxon Rank Sum test
Metric
#Win #Lose
Small Medium Large Small Medium Large
Branch coverage 5 3 22 1 0 0
Line coverage 1 3 19 1 0 0
Summary:
HMX crossover operator achieves significantly higher (~30% of the cases) or equal structural
code coverage for unit test case generation compared to the baseline SPX.
Maximum increase of branch coverage achieved by HMX: 19.1%
Maximum increase of line coverage achieved by HMX: 19.4%

RQ2: HOW DOES HMX IMPACT THE FAULT-DETECTION CAPABILITY OF THE
GENERATED TESTS? 26
OVERVIEW OF MUTATION SCORE ACHIEVED BY
HMX AND SPX
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0.25
0.50
0.75
1.00
HMX SPX
Crossover
Weak
mutation
score
0.00
0.25
0.50
0.75
1.00
HMX SPX
Crossover
Strong
Mutation
score

RQ2: HOW DOES HMX IMPACT THE FAULT-DETECTION CAPABILITY OF THE
GENERATED TESTS? 27
Pairwise comparison of impact of HMX on fast detection capability of search process with a
small, medium, and large effect size and a statistical significance <0.05
1. Impact factor measurement: Vargha-Delaney Â12 statistic
2. Assess the significance of results: non-parametric Wilcoxon Rank Sum test
Metric
#Win #Lose
Small Medium Large Small Medium Large
Weak mutation 3 3 21 1 0 0
Strong mutation 8 0 15 3 0 0
Summary:
HMX achieves significantly higher (~25% of the cases) or equal fault detection capability
compared to SPX and is outperformed only in one and three classes for weak and strong
mutation, respectively.
Maximum increase of weak mutation achieved by HMX: 14%
Maximum increase of strong mutation achieved by HMX: 12.2%

FUTURE WORK 28
• Additional crossover operators for other types (e.g., array, list, map).
• Extend our experiment by assessing other crossover operators for numbers (e.g.,
parent-centric and arithmetic crossover) and strings (e.g., multi-point crossover).

REPLICATION PACKAGE
THANK YOU FOR LISTENING
29
Replication Package
https://zenodo.org/record/5102598#.YV6uAS8Rqrw

Hybrid Multi-level Crossover for Unit Test Case Generation (SSBSE 2021)

Hybrid Multi-level Crossover for Unit Test Case Generation (SSBSE 2021)

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