Abstract image of a woman sitting on books and studying on a laptop

A Survey on Dynamic Symbolic Execution for Automatic Test Generation

Sung Kim, profile picture
Sung Kim

This document discusses dynamic symbolic execution (DSE) for automatic test generation. It summarizes various techniques for automatic test generation such as random testing, combinatorial testing, search-based testing, and symbolic execution-based testing. It then describes DSE which performs symbolic execution dynamically along concrete program executions to leverage concrete executions while overcoming limitations of symbolic execution alone, such as issues with external library calls and complex constraints. The document also discusses challenges in DSE including imprecision due to semantic differences between symbolic and concrete representations, and path explosion due to the large number of possible paths in programs.

TechnologyBusiness
1 of 65
Downloaded 118 times
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
A Survey on Dynamic Symbolic Execution for Automatic Test Generation Jan. 6 2014 PQE Hyunmin Seo 1
Motivation •  Testing is a practical way to verify software •  The cost for testing account more than 50% of total software development costs [Tassey ‘02] •  Effective, efficient and scalable automatic testing is required [Bounimova ‘13, Kim ‘12] 2
Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 3
Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 4
Random Testing •  Random Testing – Randomly generate test inputs •  Adaptive Random Testing (ART) – Spread test cases evenly over input domain [Chen ’04] – Failure-causing inputs form contiguous region [White ‘80, Chan ‘96] •  Feedback-Directed Random Testing – Randoop [Pacheco ‘07] – Unit testing 5
Random Testing Summary •  One of the most fundamental and well-studied approach [Hamlet ‘94, Loo ‘88] –  Many variations •  Pros –  Efficient, Scalable –  No source code requirement •  Cons –  Low coverage [Burnim ’08] 6
Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 7
Combinatorial Testing •  Find a subset of input parameters satisfying a certain property [Cohen ‘13] •  Mathematical property 8
Vertical Ruler Ruler Units Default View SS Navigation End with Black Always Mirror Warn Before Visible Inches Normal Pop-up Yes Yes Yes Invisible Centimeters Slide None No No No Points Outline Picas Total # of configuration Settings = 2*4*3*2*2*2 = 384 9
N-way Covering Array •  A subset including all the possible combinations from any N factors at least once [Cohen ‘13] 10
No Vertical Ruler Ruler Units Default View SS Navigation End with Black Always Mirror Warn Before 1 Visible Centimeters Outline Pop-up No No Yes 2 Invisible Inches Outline Pop-up No No No 3 Invisible Centimeters Slide None Yes Yes Yes 4 Visible Picas Outline Pop-up Yes Yes No 5 Invisible Centimeters Normal Pop-up Yes Yes No 6 Visible Points Outline None Yes No Yes 7 Invisible Points Slide Pop-up No No No 8 Invisible Picas Slide Pop-up No Yes Yes 9 Invisible Points Normal None No Yes No 10 Visible Inches Normal None Yes No Yes 11 Visible Inches Slide Pop-up No Yes Yes 12 Invisible Picas Normal None Yes No No Vertical Ruler Ruler Units Default View SS Navigation End with Black Always Mirror Warn Before Visible Inches Normal Pop-up Yes Yes Yes Invisible Centimeters Slide None No No No Points Outline Picas 2-Way Covering Array 11 CA(12;2,(25,31,41)
Combinatorial Testing Summary •  Research Direction –  How to find the minimum size array •  Greedy [Tung ‘00, Colbourn ‘04] •  Meta-heuristics [Cohen ‘03, Stardom ‘01] –  Application to different domain •  Software Product Line [McGregor ‘01, Perrouin ‘10] •  Pros –  Systematic testing with mathematical property [Cohen ‘13] –  Sample configurations to be tested [Qu 08’] •  Cons –  Too many combinations for program inputs 12
Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 13
Search-Based Testing •  A branch of SBSE in which meta heuristics are used to guide the search [McMinn ‘04] •  Typical process –  Start with a random input –  Search nearby locations for better solution –  Evaluate with fitness function –  Update the current solution with a better solution –  Search is guided by meta-heuristics 14
Meta-Heuristics Input domain (a) Hill climbing Fitnessvalue Input domain (b) Simulated Annealing Fitnessvalue Input domain (c) Genetic Algorithm Fitnessvalue 15 [McMinn ‘11]
[McMinn ’11] Input :A string count:The number of digits in the string if (count >= 4) if (count <= 10) if (checksum % 10 == checkdigit) FALSE FALSE FALSE TRUE TRUE TRUE Target π2: count = 20 π3: count = 11 π1 π2 π3 Search Based-Testing Example 16
Fitness Function •  Combination of approach level and branch distance •  Approach level –  The number of target’s control dependent node not executed by the current input •  Branch distance [Tracey ‘98] 17 Element   Value   Boolean   if  TRUE  then  0  else  K   a  =  b   if  abs(a-­‐b)  =  0  then  0  else  abs(a-­‐b)  +  K   a  ≠  b   if  abs(a-­‐b)  ≠  0  then  0  else  K   a  <  b   if  a-­‐b  <  0  then  0  else  (a-­‐b)  +  K   a  ≤  b   if  a-­‐b  ≤  0  then  0  else  (a-­‐b)  +  K   a  >  b   if  b-­‐a  <  0  then  0  else  (b-­‐a)  +  K   a  ≥  b   if  b-­‐a  ≤  0  then  0  else  (b-­‐a)  +  K   a ∨ b   min  (  cost(a),  cost(b)  )   a ∧ b   cost  (a)  +  cost  (b)   !a   move  negation  inward  and  propagate  
Search-Based Testing Summary •  A branch of SBSE –  Different search heuristics –  Different domain [Harman ’13] •  Pros –  Guide the execution toward a specific branch –  Non-functional testing (ex. longest execution time) [Wegener ’98] •  Cons –  Search space challenge –  Design of fitness functions [Arcuri ‘10] 18
Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 19
Symbolic Execution-Based Testing •  Use symbolic values to represent program variables and path conditions [King ‘76, Clarke ‘76] •  Find precise constraints for each execution path and generate test input by solving the constraints. 20
x  =  sym_input();   y  =  sym_input();   z  =  sym_input();     a  =  x  +  y     if  (z  >  a)      b  =  x  –  y   else      b  =  2  *  y     ...   Var   Value   PC:  s3>s1+s2     PC:  s3<=s1+s2     x s1   y s2   z s3   a s1 + s2   b s1 - s2   Var   Value   x s1   y s2   z s3   a s1 + s2   b 2s2   Symbolic Execution 21
π1 : PC1 π2 : PC2 π3 : PC3 . . . πn : PCn Test Generation SMT solver π1 : x = 1, y = 2, ... π2 : x = 1, y = 5, ... π3 : x = -5, y = 0,.. . . . πn : x = …, y = … Path Conditions Test Inputs 22
Symbolic Execution Based-Testing Summary •  Pros –  No redundant inputs taking the same path –  High Coverage •  Cons –  Low efficiency –  Depends on constraint solving techniques –  External library calls –  State explosion –  Imprecision 23
Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 24
Limitations of SE 25 01    void  foo(int  x,  int  y)  {   02            if  (external  (x)  ==  y)  {   03                    //  branch  1   04            }   05            else  if  (hash(x)    >  y)  {   06                  //  branch  2   07              }   08    }       è No  source  code  available       è hash()  is  complex  arithmetic  
Dynamic Symbolic Execution •  Perform symbolic execution dynamically along an execution path of a concrete input [DART ‘05, CUTE ’05, PEX ‘08] •  Apply concretization – External library calls – Complex constraints 26
DSE π1 pc1 pc2 pc3 pc4 π2 π1 π2 π1 π3 PC’ = pc1∧pc2∧!pc3 PC’’ = pc1∧!pc2 27 PC = pc1∧pc2∧pc3 … ∧pcn
Benefit •  Based on symbolic execution –  No redundant inputs taking the same path –  High coverage •  Reach deep program state by starting from well-formed user provided input •  Use concrete values to overcome limitations –  External library calls –  Complicated constraints •  Many tools –  CREST, CUTE, JCUTE, PEX, SAGE, EXE, KLEE 28
Comparison Technique Efficiency Coverage Source code Requirement ETC Random No Combinatorial No Combine with other techniques Search-Based Yes/No Non-functional Testing Symbolic Execution Yes DSE Yes Concretization 29
Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 30
Imprecision •  When the symbolic execution cannot represent the exact semantic of the program [Elkarablieh ’09] – Modeling a 4-Byte integer with a mathematical integer •  Imprecision may manifest as Divergence [Godefroid ’08] 31
Divergence pc1 pc2 pc3 pc4 pc5 pc1 ∧ pc2 ∧ ! pc3 32
Proposed solutions •  Integer size, Bit operations –  BitVector [SAGE ’08] •  Symbolic pointer dereferencing –  Array Theory of SMT solvers [Elkarablieh ‘09] •  Floating-point operations –  Combined static and dynamic analysis [Godefroid ‘10] •  Interaction with environment –  Modeling [KLEE ‘08] –  Reporting [Xiao ‘11] 33
BitVector •  Use bitvector in SMT solvers – Fixed-size integers – Bit operation on integer variables •  a & b •  a << 4 •  Slower than integer arithmetic 34
Symbolic Pointer Dereferencing •  Symbolic values are used to calculate the addresses of pointer values – Array index – a[S0] 35
01    void  single  array  (BYTE  x,  BYTE  y)  {     02        BYTE  ∗  a  =  new  BYTE[4];   03        a[0]  =  x;     04        a[1]  =  0;     05        a[2]  =  1;     06        a[3]  =  2;     07   08        if  (a[x]  ==  a[y]  +  2)     09            assert(false  );     10   11        delete  []  a;     12 }     a[x] == a[y] + 2 è 0 != 0 + 2 a[x] == a[y] + 2 è S0 != 0 + 2 a[x] == a[y] + 2 è 1 != 0 + 2 [Elkarablieh ‘09] 36 Con Sym Con x 0 S0 2 y 1 S1 1 a[0] 0 S0 2 a[1] 0 0 0 a[2] 1 1 1 a[3] 2 2 2 a[x] 0 S0 1 a[y] 0 0 0 Symbolic Pointer Dereferencing Example
01    void  single  array  (BYTE  x,  BYTE  y)  {     02        BYTE  ∗  a  =  new  BYTE[4];   03        a[0]  =  x;     04        a[1]  =  0;     05        a[2]  =  1;     06        a[3]  =  2;     07   08        if  (a[x]  ==  a[y]  +  2)     09            assert(false  );     10   11        delete  []  a;     12 }     [Elkarablieh ‘09] 37 Array Theory of SMT Solver Con Sym Con x 0 S0 2 y 1 S1 1 a[0] 0 S0 2 a[1] 0 0 0 a[2] 1 1 1 a[3] 2 2 2 a[x] 0 S0 1 a[y] 0 0 0 a[x]  :    0  ≤  x  ≤  3  ∧  a[x]      {0,1,2}   a[y]  :    0  ≤  y  ≤  3  ∧  a[y]      {0,1,2,x}  
Floating Point Operation •  [Godefroid ’10] •  FP code should only perform memory safe data-processing – Payload of an image or video file •  Non-FP code should deal with buffer allocations and memory address computations •  Lightweight local path-insensitive “may” analysis + precise “must” dynamic analysis 38
Interaction With Environment •  Modeling [KLEE ‘08] – System Calls – int  fd  =  open(argv[1],  O_RDNLY);     •  Precise Identification and Report – [Xiao ’11] 39
Imprecision Summary Reason Proposed Solutions Fixed-size Integer BitVector [SAGE ‘08] Symbolic Pointer Dereferencing Array Theory [Elkarablieh ’09] Floating-point operations Combined Static and Dynamic analysis [Godefroid ‘10] Interaction with Environment Modeling [KLEE ‘08] Precise identification and report [Xiao ’11] 40 Remaining Challenges: Precise reasoning about floating points, Interaction with Environment, External Library Calls, Concurrent programs
Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 41
Constraint Solving •  Need to solve path constraints to get the test input •  The major bottleneck – Takes long time – Cannot solve 42
Proposed Solutions •  Optimization [KLEE ‘08] – Expression rewriting – Implied value concretization – Irrelevant constraint elimination – Constraint caching •  Meta-heuristic based constraints solving [Borges ‘12, Souza ‘11, Lakhotia ‘10] •  Hybrid approach [Garg ‘13] 43
Optimization •  Irrelevant constraint elimination [KLEE ‘08] •  Constraint Caching [KLEE ‘08] 44
Meta-Heuristic Approach •  SMT solvers may not support – Non-linear constraints – Floating-Points expressions – Very complex constraints •  Use Meta-Heuristic Approaches [Borges ‘12, Souza ‘11, Lakhotia ’10] 45
Hybrid Approach [Garg ’13] •  Apply concretization first and solve it quickly with an off-the-shelf SMT solver •  If divergence occurred, use ICP (Interval Constraint Propagation) to solve the constraints 46
Constraint Solving Summary Target Proposed Solutions Time overhead Irrelevant Constraint Elimination Constraint Caching [KLEE ‘08] Complex constraints Meta-heuristic Approach [Borges ‘12, Souza ‘11, Lakhotia ‘10] Non-linear constraints ICP [Garg,‘13] 47 Remaining Challenges: Floating points, Complex constraints, Non-linear constraints
Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 48
Path Explosion •  The number of paths in a program increases exponentially with the number of branches in the program 49
Path Explosion π1 pc1 pc2 pc3 pc4 π2 π1 π2 π1 π3 pc1∧pc2∧!pc3 pc1∧!pc2 50
Proposed Solutions •  Pruning Redundant Path –  RWset [Cristian ‘08] –  Interpolation [Jaffar ’13] •  Function Summary –  Compositional [Godefroid ‘07,‘10] –  Demand-driven compositional [Anand ‘08] •  Search Heuristics –  CFG [Burnim ‘08] –  Generational [Godefroid ‘08] –  CarFast [Park ‘12] –  Hybrid [Majumdar ‘07] 51
Pruning Redundant Paths •  RWset ‘08 – If an execution reached a program point in the same state as some previous executions, then the execution will produce the same results – If two states are only differ in program values that are not subsequently read, then the two state will produce the same results 52
Pruning Redundant Paths •  Interpolant [Jaffar ’13] •  Succinctly representation of the core reason why a branch cannot be covered 53
Interpolant Example 54 UNSAT branch Full Interpolant ( x < 3z + 2) [Jaffar ’13]
Function Summary •  A function summary [Godefroid ‘07,‘10] •  prew is a conjunction of constraints of the inputs to the function •  postw , effect, is a conjunction of constraints of the outputs from the function 55
Function Summary foo(x, y) Assume foo has 10 execution paths Without Summary With Summary N paths N × 10 paths foo(x, y) N paths N paths 56
Search Heuristics •  Prioritize branches and explore relevant branches only 57
Search Heuristics (a) DFS (b) BFS (c) Heuristic Search 58
Search Heuristics •  Coverage-Optimized – CFG-directed [Burnim ‘08] – CarFast [Park ‘12] – Generational [GodeFroid ‘10] – Hybrid [Majumdar ‘07] •  Patch-Optimized – KATCH [Cadar ‘13] 59
CFG-Directed Search 60 π1 pc1 pc2 pc3 pc4 [Burnim ’08]
Limitations of Search Heuristics •  Does not consider how execution reached to branch •  Does not handle non-symbolic path constraints – pc = 3 > 0 – pc’ = !(3 > 0) = 3 ≤ 0 = UNSAT 61
Guiding Execution Toward a Branch 62 UNSAT
Path Explosion Summary Approach Proposed Solutions Pruning Redundant Paths RWset [Boonstoppel ‘08] Interpolation [Jaffar ‘13] Function Summary Compositional [Godefroid ’07,‘10] Demand-Driven Compositional [Anand ‘08] Search Heuristics CFG-Directed [Burnim ‘08] Generational [Godefroid ‘08] CarFast [Park ‘12] Hybrid [Majumdar ‘07] KATCH [Cadar ’13] 63 Remaining Challenges: Better Search Strategies, Guiding execution toward a specific branch
Conclusion •  DSE is a promising automatic test generation techniques achieving a high coverage •  DSE relies on symbolic execution and constraint solving •  Challenges – Imprecision, Constraint solving, Path explosion – GUI Application Testing, Concurrent programs, Object Creation problem 64
65 Challenges and Proposed Solutions Imprecision Integer Size BitVector [SAGE ’08] Symbolic Pointer Dereferencing Array Theory [Elkarablieh ’09] Floating-points Combined Static and Dynamic analysis [Godefroid ’10] Environments Modeling [KLEE ‘08] Precise identification and report [Xiao ’11] Constraint Solving Optimization Irrelevant Constraint Elimination Constraint Caching [KLEE ’08] Meta-Heuristics [Borges ‘12, Souza ‘11, Lakhotia ’10] Hybrid ICP [Garg,‘13] Path Explosion Pruning Redundant Paths RWset [Boonstoppel ‘08] Interpolation [Jaffar ’13] Function Summary Compositional [Godefroid ’07,‘10] Demand-Driven Compositional [Anand ’08] Search Heuristics CFG-Directed [Burnim ‘08] Generational [Godefroid ‘08] CarFast [Park ‘12] KATCH [Cadar ’13] Hybrid [Majumdar ‘07]

More Related Content

PPTX
How We Get There: A Context-Guided Search Strategy in Concolic Testing (FSE 2...
Sung Kim
 
PDF
Symbolic Reasoning and Concrete Execution - Andrii Vozniuk
Andrii Vozniuk
 
PPTX
Symbolic Execution And KLEE
Shauvik Roy Choudhary, Ph.D.
 
PPT
Crowd debugging (FSE 2015)
Sung Kim
 
ODP
Klee introduction
Georgiana T.
 
PPTX
System Verilog 2009 & 2012 enhancements
Subash John
 
PDF
Session 9 advance_verification_features
Nirav Desai
 
PDF
Session 8 assertion_based_verification_and_interfaces
Nirav Desai
 
How We Get There: A Context-Guided Search Strategy in Concolic Testing (FSE 2...
Sung Kim
 
Symbolic Reasoning and Concrete Execution - Andrii Vozniuk
Andrii Vozniuk
 
Symbolic Execution And KLEE
Shauvik Roy Choudhary, Ph.D.
 
Crowd debugging (FSE 2015)
Sung Kim
 
Klee introduction
Georgiana T.
 
System Verilog 2009 & 2012 enhancements
Subash John
 
Session 9 advance_verification_features
Nirav Desai
 
Session 8 assertion_based_verification_and_interfaces
Nirav Desai
 

What's hot (20)

PDF
Automock: Interaction-Based Mock Code Generation
Sabrina Souto
 
PPTX
Introduction to System verilog
Pushpa Yakkala
 
PDF
TDD CrashCourse Part3: TDD Techniques
David Rodenas
 
PPTX
Navigating the xDD Alphabet Soup
Dror Helper
 
PDF
Harton-Presentation
Heather Harton
 
PDF
(automatic) Testing: from business to university and back
David Rodenas
 
PDF
Coverage and Introduction to UVM
Dr. Shivananda Koteshwar
 
PDF
White Box Testing
alessandro100
 
PDF
ENKI: Access Control for Encrypted Query Processing
Mateus S. H. Cruz
 
PDF
Testing: ¿what, how, why?
David Rodenas
 
PDF
Realizing Fine-Grained and Flexible Access Control to Outsourced Data with At...
Mateus S. H. Cruz
 
PDF
Top five reasons why every DV engineer will love the latest systemverilog 201...
Srinivasan Venkataramanan
 
PPTX
ACSAC2016: Code Obfuscation Against Symbolic Execution Attacks
Sebastian Banescu
 
PDF
Control Flow Analysis
Edgar Barbosa
 
PDF
DBMask: Fine-Grained Access Control on Encrypted Relational Databases
Mateus S. H. Cruz
 
PDF
TMPA-2017: Vellvm - Verifying the LLVM
Iosif Itkin
 
DOCX
Test driven development and unit testing with examples in C++
Hong Le Van
 
PDF
Measuring maintainability; software metrics explained
Dennis de Greef
 
PPTX
NIO-ICSE2022.pptx
DavidWei89
 
PPTX
How to Profit from Static Analysis
Elena Laskavaia
 
Automock: Interaction-Based Mock Code Generation
Sabrina Souto
 
Introduction to System verilog
Pushpa Yakkala
 
TDD CrashCourse Part3: TDD Techniques
David Rodenas
 
Navigating the xDD Alphabet Soup
Dror Helper
 
Harton-Presentation
Heather Harton
 
(automatic) Testing: from business to university and back
David Rodenas
 
Coverage and Introduction to UVM
Dr. Shivananda Koteshwar
 
White Box Testing
alessandro100
 
ENKI: Access Control for Encrypted Query Processing
Mateus S. H. Cruz
 
Testing: ¿what, how, why?
David Rodenas
 
Realizing Fine-Grained and Flexible Access Control to Outsourced Data with At...
Mateus S. H. Cruz
 
Top five reasons why every DV engineer will love the latest systemverilog 201...
Srinivasan Venkataramanan
 
ACSAC2016: Code Obfuscation Against Symbolic Execution Attacks
Sebastian Banescu
 
Control Flow Analysis
Edgar Barbosa
 
DBMask: Fine-Grained Access Control on Encrypted Relational Databases
Mateus S. H. Cruz
 
TMPA-2017: Vellvm - Verifying the LLVM
Iosif Itkin
 
Test driven development and unit testing with examples in C++
Hong Le Van
 
Measuring maintainability; software metrics explained
Dennis de Greef
 
NIO-ICSE2022.pptx
DavidWei89
 
How to Profit from Static Analysis
Elena Laskavaia
 
Ad

Viewers also liked (17)

PPTX
STAR: Stack Trace based Automatic Crash Reproduction
Sung Kim
 
PPTX
A Brief Intro to CTF Contests!
M. S.
 
PDF
Covering a function using a Dynamic Symbolic Execution approach
Jonathan Salwan
 
PPTX
The Anatomy of Developer Social Networks
Sung Kim
 
PDF
How Do Software Engineers Understand Code Changes? FSE 2012
Sung Kim
 
PPTX
REMI: Defect Prediction for Efficient API Testing (

ESEC/FSE 2015, Industria...
Sung Kim
 
PPTX
Source code comprehension on evolving software
Sung Kim
 
PPTX
Automatically Generated Patches as Debugging Aids: A Human Study (FSE 2014)
Sung Kim
 
PDF
Automatic patch generation learned from human written patches
Sung Kim
 
PDF
Partitioning Composite Code Changes to Facilitate Code Review (MSR2015)
Sung Kim
 
PPTX
CrashLocator: Locating Crashing Faults Based on Crash Stacks (ISSTA 2014)
Sung Kim
 
PPTX
A Survey on Automatic Test Generation and Crash Reproduction
Sung Kim
 
PDF
Personalized Defect Prediction
Sung Kim
 
PDF
Transfer defect learning
Sung Kim
 
PDF
Tensor board
Sung Kim
 
PPTX
Survey on Software Defect Prediction
Sung Kim
 
PDF
Time series classification
Sung Kim
 
STAR: Stack Trace based Automatic Crash Reproduction
Sung Kim
 
A Brief Intro to CTF Contests!
M. S.
 
Covering a function using a Dynamic Symbolic Execution approach
Jonathan Salwan
 
The Anatomy of Developer Social Networks
Sung Kim
 
How Do Software Engineers Understand Code Changes? FSE 2012
Sung Kim
 
REMI: Defect Prediction for Efficient API Testing (

ESEC/FSE 2015, Industria...
Sung Kim
 
Source code comprehension on evolving software
Sung Kim
 
Automatically Generated Patches as Debugging Aids: A Human Study (FSE 2014)
Sung Kim
 
Automatic patch generation learned from human written patches
Sung Kim
 
Partitioning Composite Code Changes to Facilitate Code Review (MSR2015)
Sung Kim
 
CrashLocator: Locating Crashing Faults Based on Crash Stacks (ISSTA 2014)
Sung Kim
 
A Survey on Automatic Test Generation and Crash Reproduction
Sung Kim
 
Personalized Defect Prediction
Sung Kim
 
Transfer defect learning
Sung Kim
 
Tensor board
Sung Kim
 
Survey on Software Defect Prediction
Sung Kim
 
Time series classification
Sung Kim
 
Ad

Similar to A Survey on Dynamic Symbolic Execution for Automatic Test Generation (20)

DOC
Testing survey by_directions
Tao He
 
PDF
VST2022.pdf
Annibale Panichella
 
PDF
D017642026
IOSR Journals
 
PDF
Generation of Search Based Test Data on Acceptability Testing Principle
iosrjce
 
PPT
Testing foundations
Neha Singh
 
PPTX
Evolutionary Testing of Stateful Systems: a Holistic Approach
matteomiraz
 
PDF
Scalable Software Testing and Verification of Non-Functional Properties throu...
Lionel Briand
 
PDF
Software Testing:
 A Research Travelogue 
(2000–2014)
Alex Orso
 
PPT
testing(2).pptjjsieieo2i33kejjskskosowwiwk
mhuzaifasultan8
 
PDF
Transferring Software Testing Tools to Practice (AST 2017 Keynote)
Tao Xie
 
PPT
Testing 2 - Thinking Like A Tester
ArleneAndrews2
 
DOCX
Se unit 4
abdulsubhan44
 
PPTX
Advances in Unit Testing: Theory and Practice
Tao Xie
 
PPTX
White-box Unit Test Generation with Microsoft IntelliTest
Dávid Honfi
 
PDF
Enabling Automated Software Testing with Artificial Intelligence
Lionel Briand
 
PPTX
Abhik-Satish-dagstuhl
Abhik Roychoudhury
 
PDF
White-box Testing: When Quality Really Matters
TechWell
 
PPT
AutoTest.ppt
PrashanthJanakiraman
 
PPT
AutoTest.ppt
Rohit846825
 
PPT
AutoTest.ppt
CHANDUKAYALA
 
Testing survey by_directions
Tao He
 
VST2022.pdf
Annibale Panichella
 
D017642026
IOSR Journals
 
Generation of Search Based Test Data on Acceptability Testing Principle
iosrjce
 
Testing foundations
Neha Singh
 
Evolutionary Testing of Stateful Systems: a Holistic Approach
matteomiraz
 
Scalable Software Testing and Verification of Non-Functional Properties throu...
Lionel Briand
 
Software Testing:
 A Research Travelogue 
(2000–2014)
Alex Orso
 
testing(2).pptjjsieieo2i33kejjskskosowwiwk
mhuzaifasultan8
 
Transferring Software Testing Tools to Practice (AST 2017 Keynote)
Tao Xie
 
Testing 2 - Thinking Like A Tester
ArleneAndrews2
 
Se unit 4
abdulsubhan44
 
Advances in Unit Testing: Theory and Practice
Tao Xie
 
White-box Unit Test Generation with Microsoft IntelliTest
Dávid Honfi
 
Enabling Automated Software Testing with Artificial Intelligence
Lionel Briand
 
Abhik-Satish-dagstuhl
Abhik Roychoudhury
 
White-box Testing: When Quality Really Matters
TechWell
 
AutoTest.ppt
PrashanthJanakiraman
 
AutoTest.ppt
Rohit846825
 
AutoTest.ppt
CHANDUKAYALA
 

More from Sung Kim (13)

PPTX
DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning
Sung Kim
 
PPTX
Deep API Learning (FSE 2016)
Sung Kim
 
PPTX
Heterogeneous Defect Prediction (

ESEC/FSE 2015)
Sung Kim
 
PDF
A Survey on Automatic Software Evolution Techniques
Sung Kim
 
PPTX
Software Defect Prediction on Unlabeled Datasets
Sung Kim
 
PDF
MSR2014 opening
Sung Kim
 
PDF
Defect, defect, defect: PROMISE 2012 Keynote
Sung Kim
 
PPTX
Predicting Recurring Crash Stacks (ASE 2012)
Sung Kim
 
PPTX
Puzzle-Based Automatic Testing: Bringing Humans Into the Loop by Solving Puzz...
Sung Kim
 
PDF
Software Development Meets the Wisdom of Crowds
Sung Kim
 
PDF
BugTriage with Bug Tossing Graphs (ESEC/FSE 2009)
Sung Kim
 
PDF
Self-defending software: Automatically patching errors in deployed software ...
Sung Kim
 
PDF
ReCrash: Making crashes reproducible by preserving object states (ECOOP 2008)
Sung Kim
 
DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning
Sung Kim
 
Deep API Learning (FSE 2016)
Sung Kim
 
Heterogeneous Defect Prediction (

ESEC/FSE 2015)
Sung Kim
 
A Survey on Automatic Software Evolution Techniques
Sung Kim
 
Software Defect Prediction on Unlabeled Datasets
Sung Kim
 
MSR2014 opening
Sung Kim
 
Defect, defect, defect: PROMISE 2012 Keynote
Sung Kim
 
Predicting Recurring Crash Stacks (ASE 2012)
Sung Kim
 
Puzzle-Based Automatic Testing: Bringing Humans Into the Loop by Solving Puzz...
Sung Kim
 
Software Development Meets the Wisdom of Crowds
Sung Kim
 
BugTriage with Bug Tossing Graphs (ESEC/FSE 2009)
Sung Kim
 
Self-defending software: Automatically patching errors in deployed software ...
Sung Kim
 
ReCrash: Making crashes reproducible by preserving object states (ECOOP 2008)
Sung Kim
 

Recently uploaded (20)

PPTX
TEXTILE technology diploma scope and career opportunities
kriti38
 
PDF
A contest of sentiment analysis: k-nearest neighbor versus neural network
IAESIJAI
 
PDF
Five Habits of High-Impact Board Members
OnBoard
 
PDF
ENT215_Completing-a-large-scale-migration-and-modernization-with-AWS.pdf
ssuser28425f
 
PDF
A proposed approach for plagiarism detection in Myanmar Unicode text
IAESIJAI
 
PPTX
Configure Apache Mutual Authentication
Antonino Piraino
 
PDF
STKI Israel Market Study 2025 version august
Dr. Jimmy Schwarzkopf
 
PDF
Taming the Chaos: How to Turn Unstructured Data into Decisions
Safe Software
 
PDF
Improvisation in detection of pomegranate leaf disease using transfer learni...
IAESIJAI
 
PPTX
AI IN MARKETING- PRESENTED BY ANWAR KABIR 1st June 2025.pptx
HiraJay1
 
PDF
CloudStack 4.21: First Look Webinar slides
ShapeBlue
 
PPT
Galois Field Theory of Risk: A Perspective, Protocol, and Mathematical Backgr...
Melanie Swan
 
PPTX
The various Industrial Revolutions .pptx
bandileshozi3
 
PPTX
Chapter 5: Probability Theory and Statistics
RandyFin
 
PPTX
GROUP4NURSINGINFORMATICSREPORT-2 PRESENTATION
ClarisejaneSartillo1
 
PDF
1 - Historical Antecedents, Social Consideration.pdf
tuazon2030627
 
PPT
What is a Computer? Input Devices /output devices
GulJan8
 
PDF
Zenith AI: Advanced Artificial Intelligence
Biz Preneurs
 
PPTX
Modernising the Digital Integration Hub
Daniel Toomey
 
PDF
UiPath Agentic Automation session 1: RPA to Agents
suhanisingh58689
 
TEXTILE technology diploma scope and career opportunities
kriti38
 
A contest of sentiment analysis: k-nearest neighbor versus neural network
IAESIJAI
 
Five Habits of High-Impact Board Members
OnBoard
 
ENT215_Completing-a-large-scale-migration-and-modernization-with-AWS.pdf
ssuser28425f
 
A proposed approach for plagiarism detection in Myanmar Unicode text
IAESIJAI
 
Configure Apache Mutual Authentication
Antonino Piraino
 
STKI Israel Market Study 2025 version august
Dr. Jimmy Schwarzkopf
 
Taming the Chaos: How to Turn Unstructured Data into Decisions
Safe Software
 
Improvisation in detection of pomegranate leaf disease using transfer learni...
IAESIJAI
 
AI IN MARKETING- PRESENTED BY ANWAR KABIR 1st June 2025.pptx
HiraJay1
 
CloudStack 4.21: First Look Webinar slides
ShapeBlue
 
Galois Field Theory of Risk: A Perspective, Protocol, and Mathematical Backgr...
Melanie Swan
 
The various Industrial Revolutions .pptx
bandileshozi3
 
Chapter 5: Probability Theory and Statistics
RandyFin
 
GROUP4NURSINGINFORMATICSREPORT-2 PRESENTATION
ClarisejaneSartillo1
 
1 - Historical Antecedents, Social Consideration.pdf
tuazon2030627
 
What is a Computer? Input Devices /output devices
GulJan8
 
Zenith AI: Advanced Artificial Intelligence
Biz Preneurs
 
Modernising the Digital Integration Hub
Daniel Toomey
 
UiPath Agentic Automation session 1: RPA to Agents
suhanisingh58689
 

A Survey on Dynamic Symbolic Execution for Automatic Test Generation

  • 1. A Survey on Dynamic Symbolic Execution for Automatic Test Generation Jan. 6 2014 PQE Hyunmin Seo 1
  • 2. Motivation •  Testing is a practical way to verify software •  The cost for testing account more than 50% of total software development costs [Tassey ‘02] •  Effective, efficient and scalable automatic testing is required [Bounimova ‘13, Kim ‘12] 2
  • 3. Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 3
  • 4. Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 4
  • 5. Random Testing •  Random Testing – Randomly generate test inputs •  Adaptive Random Testing (ART) – Spread test cases evenly over input domain [Chen ’04] – Failure-causing inputs form contiguous region [White ‘80, Chan ‘96] •  Feedback-Directed Random Testing – Randoop [Pacheco ‘07] – Unit testing 5
  • 6. Random Testing Summary •  One of the most fundamental and well-studied approach [Hamlet ‘94, Loo ‘88] –  Many variations •  Pros –  Efficient, Scalable –  No source code requirement •  Cons –  Low coverage [Burnim ’08] 6
  • 7. Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 7
  • 8. Combinatorial Testing •  Find a subset of input parameters satisfying a certain property [Cohen ‘13] •  Mathematical property 8
  • 9. Vertical Ruler Ruler Units Default View SS Navigation End with Black Always Mirror Warn Before Visible Inches Normal Pop-up Yes Yes Yes Invisible Centimeters Slide None No No No Points Outline Picas Total # of configuration Settings = 2*4*3*2*2*2 = 384 9
  • 10. N-way Covering Array •  A subset including all the possible combinations from any N factors at least once [Cohen ‘13] 10
  • 11. No Vertical Ruler Ruler Units Default View SS Navigation End with Black Always Mirror Warn Before 1 Visible Centimeters Outline Pop-up No No Yes 2 Invisible Inches Outline Pop-up No No No 3 Invisible Centimeters Slide None Yes Yes Yes 4 Visible Picas Outline Pop-up Yes Yes No 5 Invisible Centimeters Normal Pop-up Yes Yes No 6 Visible Points Outline None Yes No Yes 7 Invisible Points Slide Pop-up No No No 8 Invisible Picas Slide Pop-up No Yes Yes 9 Invisible Points Normal None No Yes No 10 Visible Inches Normal None Yes No Yes 11 Visible Inches Slide Pop-up No Yes Yes 12 Invisible Picas Normal None Yes No No Vertical Ruler Ruler Units Default View SS Navigation End with Black Always Mirror Warn Before Visible Inches Normal Pop-up Yes Yes Yes Invisible Centimeters Slide None No No No Points Outline Picas 2-Way Covering Array 11 CA(12;2,(25,31,41)
  • 12. Combinatorial Testing Summary •  Research Direction –  How to find the minimum size array •  Greedy [Tung ‘00, Colbourn ‘04] •  Meta-heuristics [Cohen ‘03, Stardom ‘01] –  Application to different domain •  Software Product Line [McGregor ‘01, Perrouin ‘10] •  Pros –  Systematic testing with mathematical property [Cohen ‘13] –  Sample configurations to be tested [Qu 08’] •  Cons –  Too many combinations for program inputs 12
  • 13. Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 13
  • 14. Search-Based Testing •  A branch of SBSE in which meta heuristics are used to guide the search [McMinn ‘04] •  Typical process –  Start with a random input –  Search nearby locations for better solution –  Evaluate with fitness function –  Update the current solution with a better solution –  Search is guided by meta-heuristics 14
  • 15. Meta-Heuristics Input domain (a) Hill climbing Fitnessvalue Input domain (b) Simulated Annealing Fitnessvalue Input domain (c) Genetic Algorithm Fitnessvalue 15 [McMinn ‘11]
  • 16. [McMinn ’11] Input :A string count:The number of digits in the string if (count >= 4) if (count <= 10) if (checksum % 10 == checkdigit) FALSE FALSE FALSE TRUE TRUE TRUE Target π2: count = 20 π3: count = 11 π1 π2 π3 Search Based-Testing Example 16
  • 17. Fitness Function •  Combination of approach level and branch distance •  Approach level –  The number of target’s control dependent node not executed by the current input •  Branch distance [Tracey ‘98] 17 Element   Value   Boolean   if  TRUE  then  0  else  K   a  =  b   if  abs(a-­‐b)  =  0  then  0  else  abs(a-­‐b)  +  K   a  ≠  b   if  abs(a-­‐b)  ≠  0  then  0  else  K   a  <  b   if  a-­‐b  <  0  then  0  else  (a-­‐b)  +  K   a  ≤  b   if  a-­‐b  ≤  0  then  0  else  (a-­‐b)  +  K   a  >  b   if  b-­‐a  <  0  then  0  else  (b-­‐a)  +  K   a  ≥  b   if  b-­‐a  ≤  0  then  0  else  (b-­‐a)  +  K   a ∨ b   min  (  cost(a),  cost(b)  )   a ∧ b   cost  (a)  +  cost  (b)   !a   move  negation  inward  and  propagate  
  • 18. Search-Based Testing Summary •  A branch of SBSE –  Different search heuristics –  Different domain [Harman ’13] •  Pros –  Guide the execution toward a specific branch –  Non-functional testing (ex. longest execution time) [Wegener ’98] •  Cons –  Search space challenge –  Design of fitness functions [Arcuri ‘10] 18
  • 19. Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 19
  • 20. Symbolic Execution-Based Testing •  Use symbolic values to represent program variables and path conditions [King ‘76, Clarke ‘76] •  Find precise constraints for each execution path and generate test input by solving the constraints. 20
  • 21. x  =  sym_input();   y  =  sym_input();   z  =  sym_input();     a  =  x  +  y     if  (z  >  a)      b  =  x  –  y   else      b  =  2  *  y     ...   Var   Value   PC:  s3>s1+s2     PC:  s3<=s1+s2     x s1   y s2   z s3   a s1 + s2   b s1 - s2   Var   Value   x s1   y s2   z s3   a s1 + s2   b 2s2   Symbolic Execution 21
  • 22. π1 : PC1 π2 : PC2 π3 : PC3 . . . πn : PCn Test Generation SMT solver π1 : x = 1, y = 2, ... π2 : x = 1, y = 5, ... π3 : x = -5, y = 0,.. . . . πn : x = …, y = … Path Conditions Test Inputs 22
  • 23. Symbolic Execution Based-Testing Summary •  Pros –  No redundant inputs taking the same path –  High Coverage •  Cons –  Low efficiency –  Depends on constraint solving techniques –  External library calls –  State explosion –  Imprecision 23
  • 24. Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 24
  • 25. Limitations of SE 25 01    void  foo(int  x,  int  y)  {   02            if  (external  (x)  ==  y)  {   03                    //  branch  1   04            }   05            else  if  (hash(x)    >  y)  {   06                  //  branch  2   07              }   08    }       è No  source  code  available       è hash()  is  complex  arithmetic  
  • 26. Dynamic Symbolic Execution •  Perform symbolic execution dynamically along an execution path of a concrete input [DART ‘05, CUTE ’05, PEX ‘08] •  Apply concretization – External library calls – Complex constraints 26
  • 27. DSE π1 pc1 pc2 pc3 pc4 π2 π1 π2 π1 π3 PC’ = pc1∧pc2∧!pc3 PC’’ = pc1∧!pc2 27 PC = pc1∧pc2∧pc3 … ∧pcn
  • 28. Benefit •  Based on symbolic execution –  No redundant inputs taking the same path –  High coverage •  Reach deep program state by starting from well-formed user provided input •  Use concrete values to overcome limitations –  External library calls –  Complicated constraints •  Many tools –  CREST, CUTE, JCUTE, PEX, SAGE, EXE, KLEE 28
  • 29. Comparison Technique Efficiency Coverage Source code Requirement ETC Random No Combinatorial No Combine with other techniques Search-Based Yes/No Non-functional Testing Symbolic Execution Yes DSE Yes Concretization 29
  • 30. Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 30
  • 31. Imprecision •  When the symbolic execution cannot represent the exact semantic of the program [Elkarablieh ’09] – Modeling a 4-Byte integer with a mathematical integer •  Imprecision may manifest as Divergence [Godefroid ’08] 31
  • 33. Proposed solutions •  Integer size, Bit operations –  BitVector [SAGE ’08] •  Symbolic pointer dereferencing –  Array Theory of SMT solvers [Elkarablieh ‘09] •  Floating-point operations –  Combined static and dynamic analysis [Godefroid ‘10] •  Interaction with environment –  Modeling [KLEE ‘08] –  Reporting [Xiao ‘11] 33
  • 34. BitVector •  Use bitvector in SMT solvers – Fixed-size integers – Bit operation on integer variables •  a & b •  a << 4 •  Slower than integer arithmetic 34
  • 35. Symbolic Pointer Dereferencing •  Symbolic values are used to calculate the addresses of pointer values – Array index – a[S0] 35
  • 36. 01    void  single  array  (BYTE  x,  BYTE  y)  {     02        BYTE  ∗  a  =  new  BYTE[4];   03        a[0]  =  x;     04        a[1]  =  0;     05        a[2]  =  1;     06        a[3]  =  2;     07   08        if  (a[x]  ==  a[y]  +  2)     09            assert(false  );     10   11        delete  []  a;     12 }     a[x] == a[y] + 2 è 0 != 0 + 2 a[x] == a[y] + 2 è S0 != 0 + 2 a[x] == a[y] + 2 è 1 != 0 + 2 [Elkarablieh ‘09] 36 Con Sym Con x 0 S0 2 y 1 S1 1 a[0] 0 S0 2 a[1] 0 0 0 a[2] 1 1 1 a[3] 2 2 2 a[x] 0 S0 1 a[y] 0 0 0 Symbolic Pointer Dereferencing Example
  • 37. 01    void  single  array  (BYTE  x,  BYTE  y)  {     02        BYTE  ∗  a  =  new  BYTE[4];   03        a[0]  =  x;     04        a[1]  =  0;     05        a[2]  =  1;     06        a[3]  =  2;     07   08        if  (a[x]  ==  a[y]  +  2)     09            assert(false  );     10   11        delete  []  a;     12 }     [Elkarablieh ‘09] 37 Array Theory of SMT Solver Con Sym Con x 0 S0 2 y 1 S1 1 a[0] 0 S0 2 a[1] 0 0 0 a[2] 1 1 1 a[3] 2 2 2 a[x] 0 S0 1 a[y] 0 0 0 a[x]  :    0  ≤  x  ≤  3  ∧  a[x]      {0,1,2}   a[y]  :    0  ≤  y  ≤  3  ∧  a[y]      {0,1,2,x}  
  • 38. Floating Point Operation •  [Godefroid ’10] •  FP code should only perform memory safe data-processing – Payload of an image or video file •  Non-FP code should deal with buffer allocations and memory address computations •  Lightweight local path-insensitive “may” analysis + precise “must” dynamic analysis 38
  • 39. Interaction With Environment •  Modeling [KLEE ‘08] – System Calls – int  fd  =  open(argv[1],  O_RDNLY);     •  Precise Identification and Report – [Xiao ’11] 39
  • 40. Imprecision Summary Reason Proposed Solutions Fixed-size Integer BitVector [SAGE ‘08] Symbolic Pointer Dereferencing Array Theory [Elkarablieh ’09] Floating-point operations Combined Static and Dynamic analysis [Godefroid ‘10] Interaction with Environment Modeling [KLEE ‘08] Precise identification and report [Xiao ’11] 40 Remaining Challenges: Precise reasoning about floating points, Interaction with Environment, External Library Calls, Concurrent programs
  • 41. Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 41
  • 42. Constraint Solving •  Need to solve path constraints to get the test input •  The major bottleneck – Takes long time – Cannot solve 42
  • 43. Proposed Solutions •  Optimization [KLEE ‘08] – Expression rewriting – Implied value concretization – Irrelevant constraint elimination – Constraint caching •  Meta-heuristic based constraints solving [Borges ‘12, Souza ‘11, Lakhotia ‘10] •  Hybrid approach [Garg ‘13] 43
  • 44. Optimization •  Irrelevant constraint elimination [KLEE ‘08] •  Constraint Caching [KLEE ‘08] 44
  • 45. Meta-Heuristic Approach •  SMT solvers may not support – Non-linear constraints – Floating-Points expressions – Very complex constraints •  Use Meta-Heuristic Approaches [Borges ‘12, Souza ‘11, Lakhotia ’10] 45
  • 46. Hybrid Approach [Garg ’13] •  Apply concretization first and solve it quickly with an off-the-shelf SMT solver •  If divergence occurred, use ICP (Interval Constraint Propagation) to solve the constraints 46
  • 47. Constraint Solving Summary Target Proposed Solutions Time overhead Irrelevant Constraint Elimination Constraint Caching [KLEE ‘08] Complex constraints Meta-heuristic Approach [Borges ‘12, Souza ‘11, Lakhotia ‘10] Non-linear constraints ICP [Garg,‘13] 47 Remaining Challenges: Floating points, Complex constraints, Non-linear constraints
  • 48. Outline •  Automatic Test Generation –  Random Testing –  Combinatorial Testing –  Search-Based Testing –  Symbolic Execution-Based Testing –  Dynamic Symbolic Execution •  Challenges in DSE (SE) –  Imprecision –  Constraint Solving –  Path Explosion 48
  • 49. Path Explosion •  The number of paths in a program increases exponentially with the number of branches in the program 49
  • 50. Path Explosion π1 pc1 pc2 pc3 pc4 π2 π1 π2 π1 π3 pc1∧pc2∧!pc3 pc1∧!pc2 50
  • 51. Proposed Solutions •  Pruning Redundant Path –  RWset [Cristian ‘08] –  Interpolation [Jaffar ’13] •  Function Summary –  Compositional [Godefroid ‘07,‘10] –  Demand-driven compositional [Anand ‘08] •  Search Heuristics –  CFG [Burnim ‘08] –  Generational [Godefroid ‘08] –  CarFast [Park ‘12] –  Hybrid [Majumdar ‘07] 51
  • 52. Pruning Redundant Paths •  RWset ‘08 – If an execution reached a program point in the same state as some previous executions, then the execution will produce the same results – If two states are only differ in program values that are not subsequently read, then the two state will produce the same results 52
  • 53. Pruning Redundant Paths •  Interpolant [Jaffar ’13] •  Succinctly representation of the core reason why a branch cannot be covered 53
  • 54. Interpolant Example 54 UNSAT branch Full Interpolant ( x < 3z + 2) [Jaffar ’13]
  • 55. Function Summary •  A function summary [Godefroid ‘07,‘10] •  prew is a conjunction of constraints of the inputs to the function •  postw , effect, is a conjunction of constraints of the outputs from the function 55
  • 56. Function Summary foo(x, y) Assume foo has 10 execution paths Without Summary With Summary N paths N × 10 paths foo(x, y) N paths N paths 56
  • 57. Search Heuristics •  Prioritize branches and explore relevant branches only 57
  • 58. Search Heuristics (a) DFS (b) BFS (c) Heuristic Search 58
  • 59. Search Heuristics •  Coverage-Optimized – CFG-directed [Burnim ‘08] – CarFast [Park ‘12] – Generational [GodeFroid ‘10] – Hybrid [Majumdar ‘07] •  Patch-Optimized – KATCH [Cadar ‘13] 59
  • 61. Limitations of Search Heuristics •  Does not consider how execution reached to branch •  Does not handle non-symbolic path constraints – pc = 3 > 0 – pc’ = !(3 > 0) = 3 ≤ 0 = UNSAT 61
  • 62. Guiding Execution Toward a Branch 62 UNSAT
  • 63. Path Explosion Summary Approach Proposed Solutions Pruning Redundant Paths RWset [Boonstoppel ‘08] Interpolation [Jaffar ‘13] Function Summary Compositional [Godefroid ’07,‘10] Demand-Driven Compositional [Anand ‘08] Search Heuristics CFG-Directed [Burnim ‘08] Generational [Godefroid ‘08] CarFast [Park ‘12] Hybrid [Majumdar ‘07] KATCH [Cadar ’13] 63 Remaining Challenges: Better Search Strategies, Guiding execution toward a specific branch
  • 64. Conclusion •  DSE is a promising automatic test generation techniques achieving a high coverage •  DSE relies on symbolic execution and constraint solving •  Challenges – Imprecision, Constraint solving, Path explosion – GUI Application Testing, Concurrent programs, Object Creation problem 64
  • 65. 65 Challenges and Proposed Solutions Imprecision Integer Size BitVector [SAGE ’08] Symbolic Pointer Dereferencing Array Theory [Elkarablieh ’09] Floating-points Combined Static and Dynamic analysis [Godefroid ’10] Environments Modeling [KLEE ‘08] Precise identification and report [Xiao ’11] Constraint Solving Optimization Irrelevant Constraint Elimination Constraint Caching [KLEE ’08] Meta-Heuristics [Borges ‘12, Souza ‘11, Lakhotia ’10] Hybrid ICP [Garg,‘13] Path Explosion Pruning Redundant Paths RWset [Boonstoppel ‘08] Interpolation [Jaffar ’13] Function Summary Compositional [Godefroid ’07,‘10] Demand-Driven Compositional [Anand ’08] Search Heuristics CFG-Directed [Burnim ‘08] Generational [Godefroid ‘08] CarFast [Park ‘12] KATCH [Cadar ’13] Hybrid [Majumdar ‘07]