Analyzing Program Similarities and Differences Across Multiple Languages
FLAGreserachlaborato
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43 slides
Sep 10, 2024
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About This Presentation
Slides for the keynote presentation given by @ncardoz at the
SQAMIA24 Workshop on Software quality, Analysis, Monitoring, Improvement, and Applications 2024 in Novi Sad
Slide Content
Analyzing Program Similarities
and Differences Across Multiple
Languages
Nicolás Cardozo
Universidad de los Andes, Bogotá - Colombia
[email protected]
@ncardoz
SQAMIA - 9/9/2024
Novi Sad, Serbia
and Differences Across Multiple
Languages
Nicolás Cardozo
Universidad de los Andes, Bogotá - Colombia
[email protected]
@ncardoz
SQAMIA - 9/9/2024
Novi Sad, Serbia
2
int foo(int[] a) {
int n = a.size;
int j = n;
bool s = true;
while (s) {
s = false;
for (int i = 1; i < j; i++) {
if (a[i] < a[i - 1]) {
int t = a[i];
a[i] = a[i - 1];
a[i - 1] = t;
s = true;
}
}
j--;
}
return 0;
}
void foo(int arr[], int l, int m, int r){
int k;
int n1 = m - l + 1;
int n2 = r - m;
int L[n1], R[n2];
for(int i = 0; i < n1; i++)
L[i] = arr[l + i];
for(int j = 0; j < n2; j++)
R[j] = arr[m + 1+ j];
int i = 0;
int j = 0;
k = l;
while (i < n1 && j < n2) {
if (L[i] <= R[j]) {
arr[k] = L[i];
i++;
}
else {
arr[k] = R[j];
j++;
}
k++;
}
while (i < n1) {
arr[k] = L[i];
i++;
k++;
}
while (j < n2) {
arr[k] = R[j];
j++;
k++;
}
}
void bar(int arr[], int l, int r) {
if (l < r) {
int m = l + (r - l) / 2;
bar(arr, l, m);
bar(arr, m + 1, r);
foo(arr, l, m, r);
}
}
?
≅
int foo(int[] a) {
int n = a.size;
int j = n;
bool s = true;
while (s) {
s = false;
for (int i = 1; i < j; i++) {
if (a[i] < a[i - 1]) {
int t = a[i];
a[i] = a[i - 1];
a[i - 1] = t;
s = true;
}
}
j--;
}
return 0;
}
void foo(int arr[], int l, int m, int r){
int k;
int n1 = m - l + 1;
int n2 = r - m;
int L[n1], R[n2];
for(int i = 0; i < n1; i++)
L[i] = arr[l + i];
for(int j = 0; j < n2; j++)
R[j] = arr[m + 1+ j];
int i = 0;
int j = 0;
k = l;
while (i < n1 && j < n2) {
if (L[i] <= R[j]) {
arr[k] = L[i];
i++;
}
else {
arr[k] = R[j];
j++;
}
k++;
}
while (i < n1) {
arr[k] = L[i];
i++;
k++;
}
while (j < n2) {
arr[k] = R[j];
j++;
k++;
}
}
void bar(int arr[], int l, int r) {
if (l < r) {
int m = l + (r - l) / 2;
bar(arr, l, m);
bar(arr, m + 1, r);
foo(arr, l, m, r);
}
}
?
≅
3
int foo(int[] a) {
int n = a.size;
int j = n;
bool s = true;
while (s) {
s = false;
for (int i = 1; i < j; i++) {
if (a[i] < a[i - 1]) {
int t = a[i];
a[i] = a[i - 1];
a[i - 1] = t;
s = true;
}
}
j--;
}
return 0;
}
foo(List<int> array) {
int lengthOfArray = array.length;
for (int i=0; i<lengthOfArray - 1; i++) {
for (int j=0; j<lengthOfArray-i-1; j++) {
if (array[j] > array[j + 1]) {
// Swapping using temporary variable
int temp = array[j];
array[j] = array[j + 1];
array[j + 1] = temp;
}
}
}
return (array);
}
?
≅
int foo(int[] a) {
int n = a.size;
int j = n;
bool s = true;
while (s) {
s = false;
for (int i = 1; i < j; i++) {
if (a[i] < a[i - 1]) {
int t = a[i];
a[i] = a[i - 1];
a[i - 1] = t;
s = true;
}
}
j--;
}
return 0;
}
foo(List<int> array) {
int lengthOfArray = array.length;
for (int i=0; i<lengthOfArray - 1; i++) {
for (int j=0; j<lengthOfArray-i-1; j++) {
if (array[j] > array[j + 1]) {
// Swapping using temporary variable
int temp = array[j];
array[j] = array[j + 1];
array[j + 1] = temp;
}
}
}
return (array);
}
?
≅
4
(define (foo list)
(cond ((null? (cdr list)) list)
((> (car list) (cadr list))
(cons (cadr list) (foo (cons (car list)
(cdr (cdr list))))))
(else (cons (car list) (foo (cdr list))))
)
)
int foo(int[] a) {
int n = a.size;
int j = n;
bool s = true;
while (s) {
s = false;
for (int i = 1; i < j; i++) {
if (a[i] < a[i - 1]) {
int t = a[i];
a[i] = a[i - 1];
a[i - 1] = t;
s = true;
}
}
j--;
}
return 0;
}
?
≅
(define (foo list)
(cond ((null? (cdr list)) list)
((> (car list) (cadr list))
(cons (cadr list) (foo (cons (car list)
(cdr (cdr list))))))
(else (cons (car list) (foo (cdr list))))
)
)
int foo(int[] a) {
int n = a.size;
int j = n;
bool s = true;
while (s) {
s = false;
for (int i = 1; i < j; i++) {
if (a[i] < a[i - 1]) {
int t = a[i];
a[i] = a[i - 1];
a[i - 1] = t;
s = true;
}
}
j--;
}
return 0;
}
?
≅
5
int foo(int[] a) {
int n = a.size;
ans = 0;
for(int i = 0; i < n; i++) {
if(a[i] % 2 != 0)
ans += a[i];
}
return 0;
}
void foo(int *a) {
int n = a.size;
for(size_t i = 1; i < n; ++i) {
int tmp = a[i];
size_t j = i;
while(j > 0 && tmp < a[j - 1]) {
a[j] = a[j - 1];
—j;
}
a[j] = tmp;
}
}
?
≅
int foo(int[] a) {
int n = a.size;
ans = 0;
for(int i = 0; i < n; i++) {
if(a[i] % 2 != 0)
ans += a[i];
}
return 0;
}
void foo(int *a) {
int n = a.size;
for(size_t i = 1; i < n; ++i) {
int tmp = a[i];
size_t j = i;
while(j > 0 && tmp < a[j - 1]) {
a[j] = a[j - 1];
—j;
}
a[j] = tmp;
}
}
?
≅
6
7
8
What constitutes the similarities or
differences between two programs?
What constitutes the similarities or
differences between two programs?
8
What constitutes the similarities or
differences between two programs?
Behavior
Syntax
Semantics
Intention of
abstractions
What constitutes the similarities or
differences between two programs?
Behavior
Syntax
Semantics
Intention of
abstractions
9
The road ahead
How to detect
similar code
How to measure
similarities/differences
How to track and
manage divergence
Created by PixelX
from Noun Project
How to manage all of this across multiple
languages, using different paradigms
The road ahead
How to detect
similar code
How to measure
similarities/differences
How to track and
manage divergence
Created by PixelX
from Noun Project
How to manage all of this across multiple
languages, using different paradigms
Similarities detection
11
Code clones
Python code snippets
i = 1 i = 1
Code clones
Python code snippets
i = 1 i = 1
11
Code clones
Type 1 clones
Python code snippets
i = 1 i = 1
Code clones
Type 1 clones
Python code snippets
i = 1 i = 1
11
Code clones
Type 1 clones
Type 2 clones
Python code snippets
i = 1
j = 2
i = 1
l = 2
Code clones
Type 1 clones
Type 2 clones
Python code snippets
i = 1
j = 2
i = 1
l = 2
11
Code clones
Type 1 clones
Type 2 clones
Type 3 clones
Python code snippets
i = 1
j = 2
i = i + 1
i = 1
l = 2
i += 1
Code clones
Type 1 clones
Type 2 clones
Type 3 clones
Python code snippets
i = 1
j = 2
i = i + 1
i = 1
l = 2
i += 1
11
Code clones
Type 1 clones
Type 2 clones
Type 3 clones
Type 4 clones
Python code snippets
i = 1
j = 2
i = i + 1
for i in range(1,10):
i += 1
i = 1
l = 2
i += 1
i = 1 + 9
Code clones
Type 1 clones
Type 2 clones
Type 3 clones
Type 4 clones
Python code snippets
i = 1
j = 2
i = i + 1
for i in range(1,10):
i += 1
i = 1
l = 2
i += 1
i = 1 + 9
11
Code clones
Type 1 clones
Type 2 clones
Type 3 clones
Type 4 clones
Python code snippets
i = 1
j = 2
i = i + 1
for i in range(1,10):
i += 1
i = 1
l = 2
i += 1
i = 1 + 9
}
Focus of clone
detection
Code clones
Type 1 clones
Type 2 clones
Type 3 clones
Type 4 clones
Python code snippets
i = 1
j = 2
i = i + 1
for i in range(1,10):
i += 1
i = 1
l = 2
i += 1
i = 1 + 9
}
Focus of clone
detection
12
How to detect clones
Token or lexicalTree Graph HybridTextual
Textual
OUT OF STEP is a hybrid approach combining a
generalized tree structure and the textual
representation of tokens
How to detect clones
Token or lexicalTree Graph HybridTextual
Textual
OUT OF STEP is a hybrid approach combining a
generalized tree structure and the textual
representation of tokens
13
Type 1 clones
Type 2 clones
Type 3 clones
Type 4 clones
Kotlin
var i = 1
val j = 2
i = i + 1
for(i in 1..10) {
i += 1
}
int i = 1;
int l = 2;
i += 1;
i = 1 + 9;
}
Focus of clone
detection
Dart
OUT OF STEP
Type 1 clones
Type 2 clones
Type 3 clones
Type 4 clones
Kotlin
var i = 1
val j = 2
i = i + 1
for(i in 1..10) {
i += 1
}
int i = 1;
int l = 2;
i += 1;
i = 1 + 9;
}
Focus of clone
detection
Dart
OUT OF STEP
14
Clone analysis process
Grammar definition
Clone analysis process
Grammar definition
14
Clone analysis process
Grammar definition
ANTLR 4
generation
Clone analysis process
Grammar definition
ANTLR 4
generation
14
Clone analysis process
Grammar definition
ANTLR 4
generation
Lexer
parser
Clone analysis process
Grammar definition
ANTLR 4
generation
Lexer
parser
14
Clone analysis process
Grammar definition
ANTLR 4
generation
sum 1
+=
int num
eCST
Lexer
parser
Clone analysis process
Grammar definition
ANTLR 4
generation
sum 1
+=
int num
eCST
Lexer
parser
14
Clone analysis process
Grammar definition
ANTLR 4
generation
sum 1
+=
int num
eCST
Lexer
parser
OutOfStep
Clone analysis process
Grammar definition
ANTLR 4
generation
sum 1
+=
int num
eCST
Lexer
parser
OutOfStep
14
Clone analysis process
Grammar definition
ANTLR 4
generation
sum 1
+=
int num
eCST
Lexer
parser
OutOfStep
Type 1
Type 2Type 3
Clone
detection
Clone analysis process
Grammar definition
ANTLR 4
generation
sum 1
+=
int num
eCST
Lexer
parser
OutOfStep
Type 1
Type 2Type 3
Clone
detection
15
Abstract Syntax Trees
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
Abstract Syntax Trees
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
15
Abstract Syntax Trees
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
File
Abstract Syntax Trees
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
File
15
Abstract Syntax Trees
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
File
fun_decl fun_body
Abstract Syntax Trees
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
File
fun_decl fun_body
15
Abstract Syntax Trees
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
sum 1
+=
int num
File
fun_decl fun_body
variablefor
literalliteral
for_parts
posfix
parameter
statement
i 1 3
Abstract Syntax Trees
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
sum 1
+=
int num
File
fun_decl fun_body
variablefor
literalliteral
for_parts
posfix
parameter
statement
i 1 3
Abstract Syntax Trees
int ten = 10;val ten = 10
int ten = 10;val ten = 10
Abstract Syntax Trees
int ten = 10;val ten = 10
int ten = 10;val ten = 10
17
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
17
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
File
Advance nodes
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
File
Advance nodes
17
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
File
fun_decl fun_body
Universal nodes Advance nodes Stop nodes
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
File
fun_decl fun_body
Universal nodes Advance nodes Stop nodes
17
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
int num
File
fun_decl fun_body
literalliteral
parameter
Universal nodes Advance nodes Stop nodes
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
int num
File
fun_decl fun_body
literalliteral
parameter
Universal nodes Advance nodes Stop nodes
17
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
int num
File
fun_decl fun_body
assignment
literalliteral
parameter
Universal nodes Advance nodes Stop nodes
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
int num
File
fun_decl fun_body
assignment
literalliteral
parameter
Universal nodes Advance nodes Stop nodes
17
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
int num
File
fun_decl fun_body
assignmentloop
literalliteral
body
parameter
Universal nodes Advance nodes Stop nodes
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
int num
File
fun_decl fun_body
assignmentloop
literalliteral
body
parameter
Universal nodes Advance nodes Stop nodes
17
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
sum 1
+=
int num
File
fun_decl fun_body
assignment
literal
loop
literal
literalliteral
body
posfix
parameter
Universal nodes Advance nodes Stop nodes
Enriched Concrete Syntax Tree (eCST)
main(int num) {
int sum = 0;
for (i in 1..num){
// Count numbers
// sum = sum + 1;
sum += 1;
}
}
sum 1
+=
int num
File
fun_decl fun_body
assignment
literal
loop
literal
literalliteral
body
posfix
parameter
Universal nodes Advance nodes Stop nodes
18
Universal nodes represent syntactic grammar rules abstracting
programming languages’ concepts
Enriched Concrete Syntax Tree (eCST)
Universal nodes represent syntactic grammar rules abstracting
programming languages’ concepts
Enriched Concrete Syntax Tree (eCST)
18
Advance nodes are used to give groupings and a more
hierarchical structure to eCSTs, easing the comparison across
language grammars
Universal nodes represent syntactic grammar rules abstracting
programming languages’ concepts
Enriched Concrete Syntax Tree (eCST)
Advance nodes are used to give groupings and a more
hierarchical structure to eCSTs, easing the comparison across
language grammars
Universal nodes represent syntactic grammar rules abstracting
programming languages’ concepts
Enriched Concrete Syntax Tree (eCST)
18
Advance nodes are used to give groupings and a more
hierarchical structure to eCSTs, easing the comparison across
language grammars
Universal nodes represent syntactic grammar rules abstracting
programming languages’ concepts
text: while
line: 3
column: 1
clone type: -
Enriched Concrete Syntax Tree (eCST)
Advance nodes are used to give groupings and a more
hierarchical structure to eCSTs, easing the comparison across
language grammars
Universal nodes represent syntactic grammar rules abstracting
programming languages’ concepts
text: while
line: 3
column: 1
clone type: -
Enriched Concrete Syntax Tree (eCST)
19
Stop nodes allow us to fragment the eCST, to compare code
snippets across different tree locations
Enriched Concrete Syntax Tree (eCST)
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
Stop nodes allow us to fragment the eCST, to compare code
snippets across different tree locations
Enriched Concrete Syntax Tree (eCST)
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
19
Stop nodes allow us to fragment the eCST, to compare code
snippets across different tree locations
Enriched Concrete Syntax Tree (eCST)
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
Stop nodes allow us to fragment the eCST, to compare code
snippets across different tree locations
Enriched Concrete Syntax Tree (eCST)
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
20
Clone detection metric
The first step in the comparison passes through a mapping of universal
nodes, where we state which nodes represent similar concepts in the
languages
Clone detection metric
The first step in the comparison passes through a mapping of universal
nodes, where we state which nodes represent similar concepts in the
languages
20
Clone detection metric
The first step in the comparison passes through a mapping of universal
nodes, where we state which nodes represent similar concepts in the
languages
type
parameter_type
function_type
...
Clone detection metric
The first step in the comparison passes through a mapping of universal
nodes, where we state which nodes represent similar concepts in the
languages
type
parameter_type
function_type
...
20
Clone detection metric
The first step in the comparison passes through a mapping of universal
nodes, where we state which nodes represent similar concepts in the
languages
type
parameter_type
function_type
...
Second, we compare the tokens’ text
text1text2=
?
Clone detection metric
The first step in the comparison passes through a mapping of universal
nodes, where we state which nodes represent similar concepts in the
languages
type
parameter_type
function_type
...
Second, we compare the tokens’ text
text1text2=
?
21
Clone detection metric
Universal node type Token comparison Clone type
Clone detection metric
Universal node type Token comparison Clone type
21
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
21
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
Same type Exactly equal Type 1 clone
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
Same type Exactly equal Type 1 clone
21
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
Same type Exactly equal Type 1 clone
Same type Not equal Type 2 clone
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
Same type Exactly equal Type 1 clone
Same type Not equal Type 2 clone
21
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
Same type Exactly equal Type 1 clone
Same type Not equal Type 2 clone
Similar type Not equal Type 3 clone
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
Same type Exactly equal Type 1 clone
Same type Not equal Type 2 clone
Similar type Not equal Type 3 clone
21
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
Same type Exactly equal Type 1 clone
Same type Not equal Type 2 clone
Similar type Not equal Type 3 clone
m=3T
1
+2T
2
+T
3
Find the most similar clone pair:
Clone detection metric
Universal node type Token comparison Clone type
Not similar Not similar No clone
Same type Exactly equal Type 1 clone
Same type Not equal Type 2 clone
Similar type Not equal Type 3 clone
m=3T
1
+2T
2
+T
3
Find the most similar clone pair:
22
Clone detection example
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
Clone detection example
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
23
Clone detection example
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
get_a get_b
get_a
Type 1
m = 3
Type 2
m = 2
get_b
Type 2
m = 2
Type 1
m = 3
Clone detection example
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
get_a get_b
get_a
Type 1
m = 3
Type 2
m = 2
get_b
Type 2
m = 2
Type 1
m = 3
24
Clone detection example
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
Clone detection example
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
25
Clone detection example
a b
b
Type 2
m = 2
Type 1
m = 3
a
Type 1
m = 3
Type 2
m = 2
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
Clone detection example
a b
b
Type 2
m = 2
Type 1
m = 3
a
Type 1
m = 3
Type 2
m = 2
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
26
Clone detection example
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
Clone detection example
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
26
Clone detection example
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
Clone detection example
a
get_bget_a
b
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
b
get_bget_a
a
File
fun_decl
fun_body
attribute attribute
fun_decl
fun_body
fun get_a() {String b = “b” }
fun get_b() {Int a = 1 }
get_a() {int a = 1; }
get_b() {String b = “b”; }
27
OUT OF STEP at work
1.Language Features
1.Variables
2.Functions
3.Conditionals
4.Loops
5.Classes
2.Sorting Algorithms
3.Mobile Apps
https://flaglab.github.io/CloneDetection/
OUT OF STEP at work
1.Language Features
1.Variables
2.Functions
3.Conditionals
4.Loops
5.Classes
2.Sorting Algorithms
3.Mobile Apps
https://flaglab.github.io/CloneDetection/
28
OUT OF STEP at work
•144 applications evaluated (72 Kotlin, 72 Dart)
•12 different application domains
•Medium to large (~ 3200LOC) applications
•Different production levels (novice, intermediate,
enterprise)
OUT OF STEP at work
•144 applications evaluated (72 Kotlin, 72 Dart)
•12 different application domains
•Medium to large (~ 3200LOC) applications
•Different production levels (novice, intermediate,
enterprise)
29
OUT OF STEP at work
[Out of step: : Code clone detection for mobile apps across different language codebases, SciCo 2024]
OUT OF STEP at work
[Out of step: : Code clone detection for mobile apps across different language codebases, SciCo 2024]
Measuring similarity
31
Similarity vs Diversity
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5;
int ans = -1;
for (int i = 1; i < n; ++i) {
if (ans==-1 || a[i] >= ans)
ans = a[i];
}
return 0;
}
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5, k = 4;
int ans = -1;
for (int i = 0; i < n && ans == -1; ++i) {
if (a[i] == k)
ans = i;
}
return 0;
}
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5;
int i = 0;
while (i < n) {
if (i == 0 || a[i - 1] <= a[i])
i++;
else {
int t = a[i-1];
a[i - 1] = a[i];
a[i] = t;
i--;
}
}
return 0;
}
Similarity vs Diversity
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5;
int ans = -1;
for (int i = 1; i < n; ++i) {
if (ans==-1 || a[i] >= ans)
ans = a[i];
}
return 0;
}
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5, k = 4;
int ans = -1;
for (int i = 0; i < n && ans == -1; ++i) {
if (a[i] == k)
ans = i;
}
return 0;
}
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5;
int i = 0;
while (i < n) {
if (i == 0 || a[i - 1] <= a[i])
i++;
else {
int t = a[i-1];
a[i - 1] = a[i];
a[i] = t;
i--;
}
}
return 0;
}
32
Control flow graph extraction
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5;
int ans = -1;
for (int i = 1; i < n; ++i) {
if (ans==-1 || a[i] >= ans)
ans = a[i];
}
return 0;
}
CFG
language specific
constructs
Control flow graph extraction
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5;
int ans = -1;
for (int i = 1; i < n; ++i) {
if (ans==-1 || a[i] >= ans)
ans = a[i];
}
return 0;
}
CFG
language specific
constructs
33
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
33
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
33
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
while lo < hi:
mid = (lo + hi) / 2
if k <= a[mid]:
if a[lo] == k:
ans = lo
return 0
hi = mid lo = mid + 1
lo<hi
lo>=hi
a[lo]==k
a[lo]!=k
k>a[mid]
k<=a[mid]
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
while lo < hi:
mid = (lo + hi) / 2
if k <= a[mid]:
if a[lo] == k:
ans = lo
return 0
hi = mid lo = mid + 1
lo<hi
lo>=hi
a[lo]==k
a[lo]!=k
k>a[mid]
k<=a[mid]
33
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
while lo < hi:
mid = (lo + hi) / 2
if k <= a[mid]:
if a[lo] == k:
ans = lo
return 0
hi = mid lo = mid + 1
lo<hi
lo>=hi
a[lo]==k
a[lo]!=k
k>a[mid]
k<=a[mid]
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
while lo < hi:
mid = (lo + hi) / 2
if k <= a[mid]:
if a[lo] == k:
ans = lo
return 0
hi = mid lo = mid + 1
lo<hi
lo>=hi
a[lo]==k
a[lo]!=k
k>a[mid]
k<=a[mid]
33
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
while lo < hi:
mid = (lo + hi) / 2
if k <= a[mid]:
if a[lo] == k:
ans = lo
return 0
hi = mid lo = mid + 1
lo<hi
lo>=hi
a[lo]==k
a[lo]!=k
k>a[mid]
k<=a[mid]
max
Linear searchBinary search
Comparing CFGs
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
for i in range(n):
return 0
if a[i] == k: ans = i
range(n)
a[i] != k
a[i]==k
a = [1,2,3,4,5]
n,ans = 5, -1
for i in range(n):
if ans == -1 or a[i] >= a[ans]
return 0
ans = i
not(ans==-1 or a[i]>=a[ans])
ans==-1 or a[i]>=a[ans]
range(n)
a = [1,2,3,4,5]
n,k = 5, 4
lo, hi, ans = 0, n-1, -1
while lo < hi:
mid = (lo + hi) / 2
if k <= a[mid]:
if a[lo] == k:
ans = lo
return 0
hi = mid lo = mid + 1
lo<hi
lo>=hi
a[lo]==k
a[lo]!=k
k>a[mid]
k<=a[mid]
max
Linear searchBinary search
34
Measuring diversity process
Code artifacts
Measuring diversity process
Code artifacts
34
Measuring diversity process
Code artifacts
Intermediate
Representation
(IR)
Measuring diversity process
Code artifacts
Intermediate
Representation
(IR)
34
Measuring diversity process
Code artifacts
(g)CFG
Intermediate
Representation
(IR)
Measuring diversity process
Code artifacts
(g)CFG
Intermediate
Representation
(IR)
34
Measuring diversity process
Code artifacts
(g)CFG
Intermediate
Representation
(IR)
Similarity matching
and
Local Relative
Entropy (LRE)
Measuring diversity process
Code artifacts
(g)CFG
Intermediate
Representation
(IR)
Similarity matching
and
Local Relative
Entropy (LRE)
34
Measuring diversity process
Code artifacts
(g)CFG
Intermediate
Representation
(IR)
Similarity matching
and
Local Relative
Entropy (LRE)
1−
r
ij
max(R)
Similarity score
Measuring diversity process
Code artifacts
(g)CFG
Intermediate
Representation
(IR)
Similarity matching
and
Local Relative
Entropy (LRE)
1−
r
ij
max(R)
Similarity score
1
%retval = alloca i32, align 4
%a = alloca [5 x i32], align 16
%n = alloca i32, align 4
%ans = alloca i32, align 4
%i = alloca i32, align 4
store i32 0, i32* %retval
%0 = bitcast [5 x i32]* %a to i8*
call void @llvm.memcpy.p0i8.p0i8.i64(i8* %0, i8* bitcast ([5 x i32]* @_ZZ4mainE1a to i8*), i64 20, i32 16, i1 false)
store i32 5, i32* %n, align 4
%1 = load i32* %n, align 4
%cmp = icmp sgt i32 %1, 0
br i1 %cmp, label %cond.true, label %cond.false
2
%arrayidx = getelementptr inbounds [5 x i32]* %a, i32 0, i64 0
%2 = load i32* %arrayidx, align 4
br label %cond.end
3
br label %cond.end
4
%cond = phi i32 [ %2, %cond.true ], [ 0, %cond.false ]
store i32 %cond, i32* %ans, align 4
store i32 1, i32* %i, align 4
br label %for.cond
5
%3 = load i32* %i, align 4
%4 = load i32* %n, align 4
%cmp1 = icmp slt i32 %3, %4
br i1 %cmp1, label %for.body, label %for.end
6
%5 = load i32* %i, align 4
%idxprom = sext i32 %5 to i64
%arrayidx2 = getelementptr inbounds [5 x i32]* %a, i32 0, i64 %idxprom
%6 = load i32* %arrayidx2, align 4
%7 = load i32* %ans, align 4
%cmp3 = icmp sgt i32 %6, %7
br i1 %cmp3, label %if.then, label %if.end
7
%8 = load i32* %i, align 4
%idxprom4 = sext i32 %8 to i64
%arrayidx5 = getelementptr inbounds [5 x i32]* %a, i32 0, i64 %idxprom4
%9 = load i32* %arrayidx5, align 4
store i32 %9, i32* %ans, align 4
br label %if.end
8
%10 = load i32* %i, align 4
%inc = add nsw i32 %10, 1
store i32 %inc, i32* %i, align 4
br label %for.cond
9
ret i32 0
35
Control flow graph extraction
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5;
int ans = -1;
for (int i = 1; i < n; ++i) {
if (ans==-1 || a[i] >= ans)
ans = a[i];
}
return 0;
}
LLVM
9
7
8
5
4
1
2
3
6
GPS
CFG
%retval = alloca i32, align 4
%a = alloca [5 x i32], align 16
%n = alloca i32, align 4
%ans = alloca i32, align 4
%i = alloca i32, align 4
store i32 0, i32* %retval
%0 = bitcast [5 x i32]* %a to i8*
call void @llvm.memcpy.p0i8.p0i8.i64(i8* %0, i8* bitcast ([5 x i32]* @_ZZ4mainE1a to i8*), i64 20, i32 16, i1 false)
store i32 5, i32* %n, align 4
%1 = load i32* %n, align 4
%cmp = icmp sgt i32 %1, 0
br i1 %cmp, label %cond.true, label %cond.false
2
%arrayidx = getelementptr inbounds [5 x i32]* %a, i32 0, i64 0
%2 = load i32* %arrayidx, align 4
br label %cond.end
3
br label %cond.end
4
%cond = phi i32 [ %2, %cond.true ], [ 0, %cond.false ]
store i32 %cond, i32* %ans, align 4
store i32 1, i32* %i, align 4
br label %for.cond
5
%3 = load i32* %i, align 4
%4 = load i32* %n, align 4
%cmp1 = icmp slt i32 %3, %4
br i1 %cmp1, label %for.body, label %for.end
6
%5 = load i32* %i, align 4
%idxprom = sext i32 %5 to i64
%arrayidx2 = getelementptr inbounds [5 x i32]* %a, i32 0, i64 %idxprom
%6 = load i32* %arrayidx2, align 4
%7 = load i32* %ans, align 4
%cmp3 = icmp sgt i32 %6, %7
br i1 %cmp3, label %if.then, label %if.end
7
%8 = load i32* %i, align 4
%idxprom4 = sext i32 %8 to i64
%arrayidx5 = getelementptr inbounds [5 x i32]* %a, i32 0, i64 %idxprom4
%9 = load i32* %arrayidx5, align 4
store i32 %9, i32* %ans, align 4
br label %if.end
8
%10 = load i32* %i, align 4
%inc = add nsw i32 %10, 1
store i32 %inc, i32* %i, align 4
br label %for.cond
9
ret i32 0
35
Control flow graph extraction
int main() {
int a[] = {1, 2, 3, 4, 5};
int n = 5;
int ans = -1;
for (int i = 1; i < n; ++i) {
if (ans==-1 || a[i] >= ans)
ans = a[i];
}
return 0;
}
LLVM
9
7
8
5
4
1
2
3
6
GPS
CFG
36
Similarity between nodes
9
7
8
5
4
1
2
3
6
7
8
5
4
1
2
3
6
in-nodes of i’s and j’s neighbors
[An Structural Approach to Program Similarity Analysis, SQAMIA’24]
out-nodes of i’s and j’s neighbors
x
k+1
ij
=
s
k+1
in(i,j)+s
k+1
out(i,j)
2
Similarity between nodes
9
7
8
5
4
1
2
3
6
7
8
5
4
1
2
3
6
in-nodes of i’s and j’s neighbors
[An Structural Approach to Program Similarity Analysis, SQAMIA’24]
out-nodes of i’s and j’s neighbors
x
k+1
ij
=
s
k+1
in(i,j)+s
k+1
out(i,j)
2
36
Similarity between nodes
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
in-nodes of i’s and j’s neighbors
[An Structural Approach to Program Similarity Analysis, SQAMIA’24]
out-nodes of i’s and j’s neighbors
x
k+1
ij
=
s
k+1
in(i,j)+s
k+1
out(i,j)
2
Similarity between nodes
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
in-nodes of i’s and j’s neighbors
[An Structural Approach to Program Similarity Analysis, SQAMIA’24]
out-nodes of i’s and j’s neighbors
x
k+1
ij
=
s
k+1
in(i,j)+s
k+1
out(i,j)
2
37
Similarity between nodes
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
[An Structural Approach to Program Similarity Analysis, SQAMIA’24]
4
max,ls
=
1+
1
2
2
=0.75
5
max,ls
=
1+1
2
=1
Similarity between nodes
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
[An Structural Approach to Program Similarity Analysis, SQAMIA’24]
4
max,ls
=
1+
1
2
2
=0.75
5
max,ls
=
1+1
2
=1
37
Similarity between nodes
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
[An Structural Approach to Program Similarity Analysis, SQAMIA’24]
4
max,ls
=
1+
1
2
2
=0.75
5
max,ls
=
1+1
2
=1
Similarity between nodes
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
[An Structural Approach to Program Similarity Analysis, SQAMIA’24]
4
max,ls
=
1+
1
2
2
=0.75
5
max,ls
=
1+1
2
=1
38
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
38
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
7
8
5
4
1
2
3
6
38
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
Binary search
7
8
5
4
1
2
3
6
9
7
8
5
41
2
3
6
Linear search
7
8
5
4
1
2
3
6
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
Binary search
7
8
5
4
1
2
3
6
9
7
8
5
41
2
3
6
Linear search
7
8
5
4
1
2
3
6
38
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
Binary search
7
8
5
4
1
2
3
6
9
7
8
5
41
2
3
6
Linear search
7
8
5
4
1
2
3
6
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
Binary search
7
8
5
4
1
2
3
6
9
7
8
5
41
2
3
6
Linear search
7
8
5
4
1
2
3
6
38
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
Binary search
7
8
5
4
1
2
3
6
9
7
8
5
41
2
3
6
Linear search
7
8
5
4
1
2
3
6
Local relative entropy - LRE
9
7
8
5
4
1
2
3
6
max
Linear search
Binary search
7
8
5
4
1
2
3
6
9
7
8
5
41
2
3
6
Linear search
7
8
5
4
1
2
3
6
39
Measuring similarity
Built a corpus of 5 programs from programming contest platforms
with 566 implementations between them
Is there similarity or diversity among the implementations to a
program?
Measuring similarity
Built a corpus of 5 programs from programming contest platforms
with 566 implementations between them
Is there similarity or diversity among the implementations to a
program?
40
558B
558B
41
All problems
All problems
Tracking differences
Created by PixelX
from Noun Project
Created by PixelX
from Noun Project
43
Divergence
APP
Divergence
APP
43
Divergence
APP
Divergence
APP
43
Divergence
APP
FEATURE DIVERGENCE
Divergence
APP
FEATURE DIVERGENCE
44
Divergence
var sum = 0;
for (i in 1..100) {
sum = sum + i
}
int sum = 0;
int i = 1;
while(i<=100) {
sum = sum + i;
i = i + 1;
}
Divergence
var sum = 0;
for (i in 1..100) {
sum = sum + i
}
int sum = 0;
int i = 1;
while(i<=100) {
sum = sum + i;
i = i + 1;
}
45
Divergence
for=
FUN
ASSIGNMENT
EXP
LOOP
BODY
sum +
sumLITTERAL BODY
sum 0
+
i
for
FUN
ASSIGNMENT
EXP
LOOP
BODY
sum i
sumLITTERAL BODY
int sum 0i
VAR
= sum 1
ASSIGNMENT
i
+ EXP
Divergence
for=
FUN
ASSIGNMENT
EXP
LOOP
BODY
sum +
sumLITTERAL BODY
sum 0
+
i
for
FUN
ASSIGNMENT
EXP
LOOP
BODY
sum i
sumLITTERAL BODY
int sum 0i
VAR
= sum 1
ASSIGNMENT
i
+ EXP
46
Divergence
var sum = 0;
for (i in 1..100) {
sum = sum + i
}
int sum = 0;
int i = 1;
while(i<=100) {
if(i%2 == 0)
sum = sum + i;
i = i + 1;
}
Divergence
var sum = 0;
for (i in 1..100) {
sum = sum + i
}
int sum = 0;
int i = 1;
while(i<=100) {
if(i%2 == 0)
sum = sum + i;
i = i + 1;
}
47
Detecting divergence
+
i
for
FUN
ASSIGNMENT
CONDITION
LOOP
BODY
sum
i
sumLITTERAL BODY
int sum 0
ASSIGNMENT
i
+ EXP
EXP
i % 2 …
Detecting divergence
+
i
for
FUN
ASSIGNMENT
CONDITION
LOOP
BODY
sum
i
sumLITTERAL BODY
int sum 0
ASSIGNMENT
i
+ EXP
EXP
i % 2 …
48
Managing divergence
+
CONDITION
sum
EXP
i % 2 …
+
CONDITION
sum
EXP
i % 2 …
MULT_OP ADD_OP
Managing divergence
+
CONDITION
sum
EXP
i % 2 …
+
CONDITION
sum
EXP
i % 2 …
MULT_OP ADD_OP
48
Managing divergence
+
CONDITION
sum
EXP
i % 2 …
+
CONDITION
sum
EXP
i % 2 …
MULT_OP ADD_OP
if(i%2==0)
sum = sum + i
L4 - L4 ++→
Managing divergence
+
CONDITION
sum
EXP
i % 2 …
+
CONDITION
sum
EXP
i % 2 …
MULT_OP ADD_OP
if(i%2==0)
sum = sum + i
L4 - L4 ++→
return 0;
50
Closing remarks
Systems are becoming more
polyglot and we need to create the
tools to manage and analyze them
Closing remarks
Systems are becoming more
polyglot and we need to create the
tools to manage and analyze them
50
Closing remarks
Abstractions are
key to associate
elements from
different
programming
languages, going
over their
syntactic
meaning.
Systems are becoming more
polyglot and we need to create the
tools to manage and analyze them
Closing remarks
Abstractions are
key to associate
elements from
different
programming
languages, going
over their
syntactic
meaning.
Systems are becoming more
polyglot and we need to create the
tools to manage and analyze them
50
Closing remarks
Abstractions are
key to associate
elements from
different
programming
languages, going
over their
syntactic
meaning.
Systems are becoming more
polyglot and we need to create the
tools to manage and analyze them
Abstracting over language
features it is possible to
detect similarities,
differences, and move
between language
implementations
Closing remarks
Abstractions are
key to associate
elements from
different
programming
languages, going
over their
syntactic
meaning.
Systems are becoming more
polyglot and we need to create the
tools to manage and analyze them
Abstracting over language
features it is possible to
detect similarities,
differences, and move
between language
implementations
FLAGL BA
AI, RL, ML,
Ontologies,
NLP
Algorithms
and Data
Structures
Bioinformatic
software and
algorithms
Programming
Language
Implementations
(post-quantum)
cryptography
AI, RL, ML,
Ontologies,
NLP
Algorithms
and Data
Structures
Bioinformatic
software and
algorithms
Programming
Language
Implementations
(post-quantum)
cryptography
@FLAGlab research
Categories
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