Artificial software diversity: automatic synthesis of program sosies

Artificial software diversity:
automatic synthesis of
program sosies
Benoit Baudry
Joint work with Simon Allier, Ioannis Kavvouras, Julien
Langlois and Martin Monperrus

Diversity to handle / increase uncertainty
l Navigate through the program space
l brittleness versus plasticity of software
l Failure detection
l Moving target
l Self-repair of software

Program sosie
• Given a
specification
3

Program sosie
4
• Given a
specification S
• Given a program
P that conforms
to S
correct
implementation

Program sosie
5
• Given a
specification S
• Given a program
P that conforms
to S
• A sosie of P is a
variant that also
conforms to S
a sosie

Program sosie
6
potential
failures or
breaches
failure diversity
• Given a
specification S
• Given a program
P that conforms
to S
• A sosie of P is a
variant that also
conforms to S

Specification: data and properties
fun : Function
assert abs(fun(.5) - 0.25) < 0.05
assert abs(fun(.4) - 0.16) < 0.05
assert abs(fun(.3) - 0.09) < 0.05
l The test input data specifies the input domain
l The assertions specify the level of abstraction

Sosies and Diversity
l There is a diversity of sosies
l There may be a diversity of output outside the
specified domain
l The specified input domain and the
associated level of abstraction allows more or
less diversity
The diversity is a dependent variable of
the input domain and level of abstraction

Sosies are not
l The identity / the clone
l Program equivalence (the same output for all
possible inputs)
l The same output
l on the specified input domain
l at a given level of abstraction
l Could be called "phenotypic equivalence"

Research questions
Do sosies exist?
Can we automatically synthesize them?
What are effective transformations?
10

Automatic Synthesis of Sosies
l We replace a given piece of code by another
one and see whether all assertions remain
satisfied
l Pieces of code:
l Method calls
l Methods
l Expressions
l ...

Example of sosie
12
@Override
public void report(SortedMap<String, Gauge> gauges,
SortedMap<String, Counter> counters,
SortedMap<String, Histogram> histograms,
SortedMap<String, Meter> meters,
SortedMap<String, Timer> timers) {
final long timestamp = TimeUnit.MILLISECONDS.toSeconds(clock.getTime());
for (Map.Entry<String, Gauge> entry : gauges.entrySet()) {
reportGauge(timestamp, entry.getKey(), entry.getValue());
}
for (Map.Entry<String, Counter> entry : counters.entrySet()) {
reportCounter(timestamp, entry.getKey(), entry.getValue());
}
for (Map.Entry<String, Histogram> entry : histograms.entrySet()) {
reportHistogram(timestamp, entry.getKey(), entry.getValue());
}
for (Map.Entry<String, Meter> entry : meters.entrySet()) {
reportMeter(timestamp, entry.getKey(), entry.getValue());
}
for (Map.Entry<String, Timer> entry : timers.entrySet()) {
reportTimer(timestamp, entry.getKey(), entry.getValue());
}
}

Example of sosie
13
@Override
InputContext: [long]
OutputContext: void
codeFragment: if (least >= bound)
throw new java.lang.IllegalArgumentException();
}
OutputContext: void
codeFragment: if (n <= 0)
reportCounter(timestamp, entry.getKey(), entry.getValue());
}
throw new java.lang.IllegalArgumentException("n must be
positive");
}
}
}
}

Example of sosie
14
@Override
}
if (timestamp <= 0)
throw new java.lang.IllegalArgumentException("n must be positive");
}
}
}
}
}
variable mapping: {n=timestamp}
OutputContext: void
codeFragment: if (n <= 0)
throw new java.lang.IllegalArgumentException("n must be
positive");

What is effective for sosiefication?
• Evaluate the efficiency of 9 transformations:
• Replace/Add/Delete
• CMNVM Replace/Add: context mapping but not
variable mapping
• NCMVMN Replace/Add: not context mapping but
mapping on variable name
• NCM Replace/Add: no context mapping
15

Preliminary result
NCM
Replace
NCM/
VNM
Replace
CM/
NVM
Replace
Normal
Replace
NCM
Add
NCM/
VNM
Add
CM/
NVM
Add
Add Delete
junit
#trial
#variant
#sosie
500
36
2
500
80
27
500
177
32
500
310
43
500
38
33
500
61
42
500
140
70
500
195
79
500
253
25
metrics
#trial
#variant
#sosie
"
1960
116
12
1960
282
69
1960
693
86
1960
1262
174
1960
157
108
1960
269
142
1960
700
352
1960
908
347
1960
977
110
clojure
#trial
#variant
#sosie
680
30
0
680
123
3
680
154
7
680
342
21
680
30
7
680
90
3
680
124
35
680
152
28
680
391
29

Sosiefication with reactions
#variants
#incorrect-variants
#sosies % sosies
JUnit 5265 4377 888 16.86%
Metrics 4699 4299 400 8.51%
Codec 14435 11080 3355 23.24%
Math 45517 40500 5017 11.02%
Clojure 32335 20706 11629 35.96%
bubble-sort 23 18 4 17.39%
insert-sort 18 17 1 1.26%
quick-sort 553 525 7 0.42%
merge-sort 4759 4739 20

What to do with sosies?
l Demonstrate plastic properties of software
l Functional equivalence and repair
l Functional resillience; replacement in case of bugs (ICSE'13
Gorla et al.)
l Use as gene pool
l Randomize execution to create a moving target
l Functional sense of self ("detect-fast")
The diversity of functionally equivalent code
improves the robustness and resilience
of software

Artificial software diversity: automatic synthesis of program sosies

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