CUTE: A Concolic Unit Testing Engine for C
|
|
|
- Dayna Edwards
- 6 years ago
- Views:
Transcription
1 CUTE: A Concolic Unit Testing Engine for C Koushik Sen Darko Marinov Gul Agha University of Illinois Urbana-Champaign
2 Goal Automated Scalable Unit Testing of real-world C Programs Generate test inputs Execute unit under test on generated test inputs so that all reachable ments are executed Any assertion violation gets caught 2
3 Goal Automated Scalable Unit Testing of real-world C Programs Generate test inputs Execute unit under test on generated test inputs so that all reachable ments are executed Any assertion violation gets caught Our Approach: Explore all execution paths of an Unit for all possible inputs Exploring all execution paths ensure that all reachable ments are executed 3
4 Paths of a Program Can be seen as a binary tree with possibly infinite depth Computation tree 0 Each node represents the execution of a if then else ment 0 0 Each edge represents the execution of a sequence of non-conditional ments 0 Each path in the tree represents an equivalence class of inputs 0 4
5 Example of Computation Tree void test_me(int x int y) { if(2*x==y){ if(x!= y+0){ printf( I am fine here ); else { printf( I should not reach here ); ERROR; N 2*x==y N Y x!=y+0 Y ERROR 5
6 Existing Approach I Random testing generate random inputs execute the program on generated inputs Probability of reaching an error can be tiny test_me(int x){ if(x==94389){ ERROR; Probability of hitting ERROR = /2 32 6
7 Existing Approach II Symbolic use symbolic values for input variables execute the program symbolically on symbolic input values collect symbolic path constraints use theorem prover to check if a branch can be taken Does not scale for large programs test_me(int x){ if((x%0)*4!=7){ ERROR; else { ERROR; Symbolic execution will say both branches are reachable: False positive 7
8 Approach Combine concrete and symbolic execution for unit testing Concrete + Symbolic = Concolic In a nutshell Use concrete execution over a concrete input to guide symbolic execution Concrete execution helps Symbolic execution to simplify complex and unmanageable symbolic expressions by replacing symbolic values by concrete values Achieves Scalability Higher branch coverage than random testing No false positives or scalability issue like in symbolic execution based testing 8
9 Example typedef struct cell { int v; struct cell *next; cell; int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; Random Test Driver: random memory graph reachable from p random value for x Probability of reaching abort( ) is extremely low 9
10 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p NULL x=236 p=p 0 x=x 0 0
11 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p NULL x=236 p=p 0 x=x 0
12 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p NULL x=236 p=p 0 x=x 0 x 0 >0 2
13 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p x=236 p=p 0 x=x 0 x 0 >0!(p 0!=NULL) NULL 3
14 CUTE Approach typedef struct cell { int v; struct cell *next; cell; int f(int v) { return 2*v + ; Concrete concrete symbolic solve: x 0 >0 and p 0 NULL Symbolic constraints int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p x=236 p=p 0 x=x 0 x 0 >0 p 0 =NULL NULL 4
15 CUTE Approach typedef struct cell { int v; struct cell *next; cell; Concrete concrete symbolic solve: x 0 >0 and p 0 NULL Symbolic constraints int f(int v) { return 2*v + ; x 0 =236 p 0 NULL int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p x= p=p 0 x=x 0 x 0 >0 p 0 =NULL NULL 5
16 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 634 NULL x=236 p=p 0 x=x 0 p->v =v 0 p->next=n 0 6
17 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 634 NULL x=236 p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 7
18 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 634 NULL x=236 p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 8
19 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 634 NULL x=236 p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +v 0 9
20 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 634 NULL x=236 p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +v 0 20
21 CUTE Approach typedef struct cell { int v; struct cell *next; cell; int f(int v) { return 2*v + ; Concrete concrete symbolic solve: x 0 >0 and p 0 NULL and 2x 0 +=v 0 Symbolic constraints int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 634 NULL x=236 p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +v 0 2
22 CUTE Approach typedef struct cell { int v; struct cell *next; cell; Concrete concrete symbolic solve: x 0 >0 and p 0 NULL Symbolic constraints int f(int v) { return 2*v + ; and 2x 0 +=v 0 x 0 = p 0 NULL int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 634 NULL x=236 3 p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +v 0 22
23 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 NULL x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 23
24 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 NULL x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 24
25 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 NULL x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 25
26 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 NULL x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +=v 0 26
27 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 NULL x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +=v 0 n 0 p 0 27
28 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 NULL x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +=v 0 n 0 p 0 28
29 CUTE Approach typedef struct cell { int v; struct cell *next; cell; int f(int v) { return 2*v + ; concrete Concrete symbolic solve: x 0 >0 and p 0 NULL and 2x 0 +=v 0 and n 0 =p 0 Symbolic constraints int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 NULL x=. p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +=v 0 n 0 p 0 29
30 CUTE Approach typedef struct cell { int v; struct cell *next; cell; int f(int v) { return 2*v + ; concrete Concrete symbolic solve: x 0 >0 and p 0 NULL and 2x 0 +=v 0 and n 0 =p 0 Symbolic constraints int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 NULL x= x 0 = p 0 3 p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +=v 0 n 0 p 0 30
31 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 3
32 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 32
33 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 33
34 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +=v 0 34
35 CUTE Approach typedef struct cell { int v; struct cell *next; cell; concrete Concrete symbolic Symbolic constraints int f(int v) { return 2*v + ; int testme(cell *p int x) { if (x > 0) if (p!= NULL) if (f(x) == p->v) if (p->next == p) return 0; p 3 Program Error x= p=p 0 x=x 0 p->v =v 0 p->next=n 0 x 0 >0 p 0 NULL 2x 0 +=v 0 n 0 =p 0 35
36 Explicit Path (not State) Model Checking Traverse all execution paths one by one to detect errors 0 check for assertion violations 0 0 check for program crash combine with valgrind to discover memory leaks 0 detect invariants 0 37
37 Explicit Path (not State) Model Checking Traverse all execution paths one by one to detect errors 0 check for assertion violations 0 0 check for program crash combine with valgrind to discover memory leaks 0 detect invariants 0 38
38 Explicit Path (not State) Model Checking Traverse all execution paths one by one to detect errors 0 check for assertion violations 0 0 check for program crash combine with valgrind to discover memory leaks 0 detect invariants 0 39
39 Explicit Path (not State) Model Checking Traverse all execution paths one by one to detect errors 0 check for assertion violations 0 0 check for program crash combine with valgrind to discover memory leaks 0 detect invariants 0 40
40 Explicit Path (not State) Model Checking Traverse all execution paths one by one to detect errors 0 check for assertion violations 0 0 check for program crash combine with valgrind to discover memory leaks 0 detect invariants 0 4
41 Explicit Path (not State) Model Checking Traverse all execution paths one by one to detect errors 0 check for assertion violations 0 0 check for program crash combine with valgrind to discover memory leaks 0 detect invariants 0 42
42 Explicit Path (not State) Model Checking Traverse all execution paths one by one to detect errors 0 check for assertion violations 0 0 check for program crash combine with valgrind to discover memory leaks 0 detect invariants 0 43
43 Explicit Path (not State) Model Checking Traverse all execution paths one by one to detect errors 0 check for assertion violations 0 0 check for program crash combine with valgrind to discover memory leaks 0 detect invariants 0 44
44 CUTE in a Nutshell Generate concrete inputs one by one each input leads program along a different path 45
45 CUTE in a Nutshell Generate concrete inputs one by one each input leads program along a different path On each input execute program both concretely and symbolically 46
46 CUTE in a Nutshell Generate concrete inputs one by one each input leads program along a different path On each input execute program both concretely and symbolically Both cooperate with each other concrete execution guides the symbolic execution 47
47 CUTE in a Nutshell Generate concrete inputs one by one each input leads program along a different path On each input execute program both concretely and symbolically Both cooperate with each other concrete execution guides the symbolic execution concrete execution enables symbolic execution to overcome incompleteness of theorem prover replace symbolic expressions by concrete values if symbolic expressions become complex resolve aliases for pointer using concrete values handle arrays naturally 48
48 CUTE in a Nutshell Generate concrete inputs one by one each input leads program along a different path On each input execute program both concretely and symbolically Both cooperate with each other concrete execution guides the symbolic execution concrete execution enables symbolic execution to overcome incompleteness of theorem prover replace symbolic expressions by concrete values if symbolic expressions become complex resolve aliases for pointer using concrete values handle arrays naturally symbolic execution helps to generate concrete input for next execution increases coverage 49
49 Testing Data-structures of CUTE itself Unit tested several non-standard datastructures implemented for the CUTE tool cu_depend (used to determine dependency during constraint solving using graph algorithm) cu_linear (linear symbolic expressions) cu_pointer (pointer symbolic expressions) Discovered a few memory leaks and a couple of segmentation faults these errors did not show up in other uses of CUTE for memory leaks we used CUTE in conjunction with Valgrind 50
50 SGLIB: popular library for C data-structures Used in Xrefactory a commercial tool for refactoring C/C++ programs Found two bugs in sglib.0. reported them to authors fixed in sglib.0.2 Bug : doubly-linked list library Bug 2: segmentation fault occurs when a non-zero length list is concatenated with a zero-length list discovered in 40 iterations ( < second) hash-table an infinite loop in hash table is member function 93 iterations ( second) 5
51 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Let initially x = -3 and y = 7 generated by random testdriver 52
52 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Let initially x = -3 and y = 7 generated by random testdriver concrete z = 9 symbolic z = x*x*x + 3*x*x+9 take then branch with constraint x*x*x+ 3*x*x+9!= y 53
53 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Let initially x = -3 and y = 7 generated by random testdriver concrete z = 9 symbolic z = x*x*x + 3*x*x+9 take then branch with constraint x*x*x+ 3*x*x+9!= y solve x*x*x+ 3*x*x+9 = y to take else branch Don t know how to solve!! Stuck? 54
54 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Let initially x = -3 and y = 7 generated by random testdriver concrete z = 9 symbolic z = x*x*x + 3*x*x+9 take then branch with constraint x*x*x+ 3*x*x+9!= y solve x*x*x+ 3*x*x+9 = y to take else branch Don t know how to solve!! Stuck? NO : CUTE handles this smartly 55
55 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Let initially x = -3 and y = 7 generated by random testdriver 56
56 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Let initially x = -3 and y = 7 generated by random testdriver concrete z = 9 symbolic z = x*x*x + 3*x*x+9 cannot handle symbolic value of z 57
57 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Let initially x = -3 and y = 7 generated by random testdriver concrete z = 9 symbolic z = x*x*x + 3*x*x+9 cannot handle symbolic value of z make symbolic z = 9 and proceed 58
58 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Let initially x = -3 and y = 7 generated by random testdriver concrete z = 9 symbolic z = x*x*x + 3*x*x+9 cannot handle symbolic value of z make symbolic z = 9 and proceed take then branch with constraint 9!= y 59
59 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Let initially x = -3 and y = 7 generated by random testdriver concrete z = 9 symbolic z = x*x*x + 3*x*x+9 cannot handle symbolic value of z make symbolic z = 9 and proceed take then branch with constraint 9!= y solve 9 = y to take else branch execute next run with x = -3 and y= 9 got error (reaches abort) 60
60 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); Replace symbolic expression by concrete value when symbolic expression becomes unmanageable (i.e. non-linear) Let initially x = -3 and y = 7 generated by random testdriver concrete z = 9 symbolic z = x*x*x + 3*x*x+9 cannot handle symbolic value of z make symbolic z = 9 and proceed take then branch with constraint 9!= y solve 9 = y to take else branch execute next run with x = -3 and y= 9 got error (reaches abort) 6
61 Simultaneous Symbolic & Concrete void again_test_me(int xint y){ z = x*x*x + 3*x*x + 9; if(z!= y){ printf( Good branch ); else { printf( Bad branch ); void again_test_me(int xint y){ z = black_box_fun(x); if(z!= y){ printf( Good branch ); else { printf( Bad branch ); 62
62 Related Work DART: Directed Automated Random Testing by Patrice Godefroid Nils Klarlund and Koushik Sen (PLDI 05) handles only arithmetic constraints CUTE Supports C with pointers data-structures Highly efficient constraint solver times faster arithmetic pointers Provides Bounded Depth-First Search and Random Search strategies Publicly available tool that works on ALL C programs 63
63 Discussion CUTE is dynamic analysis (compare with static analysis) ensures no false alarms concrete execution and symbolic execution run simultaneously symbolic execution consults concrete execution whenever dynamic analysis becomes intractable real tool that works on all C programs Requires actual code that can be fully compiled Can (sometimes) reduce to Random Testing System calls (I/O) in tests not supported Supporting concurrency is hard Complementary to Static Analysis Tools 64
DART: Directed Automated Random Testing
DART: Directed Automated Random Testing Patrice Godefroid Nils Klarlund Koushik Sen Bell Labs Bell Labs UIUC Presented by Wei Fang January 22, 2015 PLDI 2005 Page 1 June 2005 Motivation Software testing:
CUTE: A Concolic Unit Testing Engine for C
CUTE: A Concolic Unit Testing Engine for C Koushik Sen, Darko Marinov, Gul Agha Department of Computer Science University of Illinois at Urbana-Champaign {ksen,marinov,agha}@cs.uiuc.edu ABSTRACT In unit
DART: Directed Automated Random Testing. CUTE: Concolic Unit Testing Engine. Slide Source: Koushik Sen from Berkeley
DAR: Directed Automated Random esting CUE: Concolic Unit esting Engine Slide Source: Koushik Sen from Berkeley Verification and esting We would like to prove programs correct Verification and esting We
Automated Software Analysis Techniques For High Reliability: A Concolic Testing Approach. Moonzoo Kim
Automated Software Analysis Techniques For High Reliability: A Concolic Testing Approach Moonzoo Kim Contents Automated Software Analysis Techniques Background Concolic testing process Example of concolic
Applications of Logic in Software Engineering. CS402, Spring 2016 Shin Yoo
Applications of Logic in Software Engineering CS402, Spring 2016 Shin Yoo Acknowledgements I borrow slides from: Moonzoo Kim Theo C. Ruys (http://spinroot.com/spin/doc/ SpinTutorial.pdf) CBMC & Daniel
Software Testing CS 408. Lecture 6: Dynamic Symbolic Execution and Concolic Testing 1/30/18
Software Testing CS 408 Lecture 6: Dynamic Symbolic Execution and Concolic Testing 1/30/18 Relevant Papers CUTE: A Concolic Unit Testing Engine for C Koushik Sen, Darko Marinov, Gul Agha Department of
Three Important Testing Questions
Testing Part 2 1 Three Important Testing Questions How shall we generate/select test cases? Did this test execution succeed or fail? How do we know when we ve tested enough? 65 1. How do we know when we
Symbolic Execution, Dynamic Analysis
Symbolic Execution, Dynamic Analysis http://d3s.mff.cuni.cz Pavel Parízek CHARLES UNIVERSITY IN PRAGUE faculty of mathematics and physics Symbolic execution Pavel Parízek Symbolic Execution, Dynamic Analysis
Concolic Testing of Sequential and Concurrent Programs
Concolic Testing of Sequential and Concurrent Programs Koushik Sen University of California, Berkeley ksen@cs.berkeley.edu and Darko Marinov University of Illinois at Urbana Champaign marinov@cs.uiuc.edu
Emblematic Execution for Software Testing based on DART AND CUTE
Emblematic Execution for Software Testing based on DART AND CUTE K. K. V. D. Hari Prasad 1, Ch. V. Phani Krishna and V. Samson Deva Kumar 3 1 Student, IV/IV B.Tech, Department of Computer Science Engineering,
Automatic Software Verification
Automatic Software Verification Instructor: Mooly Sagiv TA: Oded Padon Slides from Eran Yahav and the Noun Project, Wikipedia Course Requirements Summarize one lecture 10% one lecture notes 45% homework
Symbolic and Concolic Execution of Programs
Symbolic and Concolic Execution of Programs Information Security, CS 526 Omar Chowdhury 10/7/2015 Information Security, CS 526 1 Reading for this lecture Symbolic execution and program testing - James
Automated Testing of Cloud Applications
Automated Testing of Cloud Applications Linghao Zhang, Tao Xie, Nikolai Tillmann, Peli de Halleux, Xiaoxing Ma, Jian lv {lzhang25, txie}@ncsu.edu, {nikolait, jhalleux}@microsoft.com, {xxm, lj}@nju.edu.cn
DART: Directed Automated Random Testing
DART: Directed Automated Random Testing Patrice Godefroid Nils Klarlund Bell Laboratories, Lucent Technologies {god,klarlund@bell-labs.com Koushik Sen Computer Science Department University of Illinois
From Symbolic Execution to Concolic Testing. Daniel Paqué
From Symbolic Execution to Concolic Testing Daniel Paqué Structure Symbolic Execution Concolic Testing Execution Generated Testing Concurrency in Concolic Testing 2 Motivation Software Testing usually
Bullseye: A System for Targeted Test Generation
Bullseye: A System for Targeted Test Generation Brandon Streiff bstreiff@cs.utexas.edu 02 May 2008 Undergraduate Honors Thesis Supervisor: Dr. Calvin Lin Abstract This paper describes the architecture
Analysis/Bug-finding/Verification for Security
Analysis/Bug-finding/Verification for Security VIJAY GANESH University of Waterloo Winter 2013 Analysis/Test/Verify for Security Instrument code for testing Heap memory: Purify Perl tainting (information
Symbolic Evaluation/Execution
Symbolic Evaluation/Execution Reading Assignment *R.W. Floyd, "Assigning Meaning to Programs, Symposium on Applied Mathematics, 1967, pp. 19-32 (Appeared as volume 19 of Mathematical Aspects of Computer
Comp Sci 1MD3 Mid-Term II 2003 answers
Comp Sci 1MD3 Mid-Term II 2003 answers Dr. Jacques Carette Name: Student No.: Duration : 50 minutes This midterm contains 19 questions on 4 double-sided pages (including this one). This midterm will be
Automated Software Testing in the Absence of Specifications
Automated Software Testing in the Absence of Specifications Tao Xie North Carolina State University Department of Computer Science Nov 2005 http://www.csc.ncsu.edu/faculty/xie/ Why Automate Testing? Software
Symbolic Execution. Wei Le April
Symbolic Execution Wei Le 2016 April Agenda What is symbolic execution? Applications History Interal Design: The three challenges Path explosion Modeling statements and environments Constraint solving
Dynamic Test Generation to Find Bugs in Web Application
Dynamic Test Generation to Find Bugs in Web Application C.SathyaPriya 1 and S.Thiruvenkatasamy 2 1 Department of IT, Shree Venkateshwara Hi-Tech Engineering College, Gobi, Tamilnadu, India. 2 Department
Reading Assignment. Symbolic Evaluation/Execution. Move from Dynamic Analysis to Static Analysis. Move from Dynamic Analysis to Static Analysis
Reading Assignment Symbolic Evaluation/Execution *R.W. Floyd, "Assigning Meaning to Programs, Symposium on Applied Mathematics, 1967, pp. 19-32 (Appeared as volume 19 of Mathematical Aspects of Computer
Advanced Programming Methods. Introduction in program analysis
Advanced Programming Methods Introduction in program analysis What is Program Analysis? Very broad topic, but generally speaking, automated analysis of program behavior Program analysis is about developing
Lecture Notes on Memory Layout
Lecture Notes on Memory Layout 15-122: Principles of Imperative Computation Frank Pfenning André Platzer Lecture 11 1 Introduction In order to understand how programs work, we can consider the functions,
CS558 Programming Languages
CS558 Programming Languages Winter 2017 Lecture 4a Andrew Tolmach Portland State University 1994-2017 Semantics and Erroneous Programs Important part of language specification is distinguishing valid from
Automated Test-Input Generation
Automated Test-Input Generation Tao Xie North Carolina State University Department of Computer Science Nov 2005 http://www.csc.ncsu.edu/faculty/xie/ Why Automate Testing? Software testing is important
Automated Unit Testing of Large Industrial Embedded Software using Concolic Testing
Automated Unit Testing of Large Industrial Embedded Software using Concolic Testing, Moonzoo Kim SW Testing & Verification Group KAIST, South Korea Youil Kim, Taeksu Kim, Gunwoo Lee, Yoonkyu Jang Samsung
A Gentle Introduction to Program Analysis
A Gentle Introduction to Program Analysis Işıl Dillig University of Texas, Austin January 21, 2014 Programming Languages Mentoring Workshop 1 / 24 What is Program Analysis? Very broad topic, but generally
CSCE 548 Building Secure Software Software Analysis Basics
CSCE 548 Building Secure Software Software Analysis Basics Professor Lisa Luo Spring 2018 Previous Class Ø Android Background Ø Two Android Security Problems: 1. Android App Repackaging o Very easy to
Combining Model Checking and Testing
Combining Model Checking and Testing Patrice Godefroid Abstract Model checking and testing have a lot in common. Over the last two decades, significant progress has been made on how to broaden the scope
TNT. Runtime Errors. Proving Non-Termination. Proving Non-Termination. Non-Termination Errors. TNT: Testing Non-Termination
Runtime Errors Proving Non-Termination Ashutosh K. Gupta Thomas A. Henzinger Rupak Majumdar MPI-SWS EPFL UCLA Andrey Rybalchenko Ru-Gang Xu MPI-SWS UCLA 2 Non-Termination Errors Proving Non-Termination...
CS 241 Data Organization Binary Trees
CS 241 Data Organization Binary Trees Brooke Chenoweth University of New Mexico Fall 2017 Binary Tree: Kernighan and Ritchie 6.5 Read a file and count the occurrences of each word. now is the time for
: Principles of Imperative Computation Victor Adamchik. Practice Exam - I
15-122 Practice Exam - I Page 1 of 10 15-122 : Principles of Imperative Computation Victor Adamchik Practice Exam - I Name: Andrew ID: Answer the questions in the space provided following each question.
Lecture Notes on Binary Search Trees
Lecture Notes on Binary Search Trees 15-122: Principles of Imperative Computation Frank Pfenning Lecture 17 1 Introduction In the previous two lectures we have seen how to exploit the structure of binary
Lecture Notes on Binary Search Trees
Lecture Notes on Binary Search Trees 15-122: Principles of Imperative Computation Frank Pfenning André Platzer Lecture 15 March 6, 2014 1 Introduction In this lecture, we will continue considering associative
Static Analysis and Bugfinding
Static Analysis and Bugfinding Alex Kantchelian 09/12/2011 Last week we talked about runtime checking methods: tools for detecting vulnerabilities being exploited in deployment. So far, these tools have
Having a BLAST with SLAM
Having a BLAST with SLAM # #2 Topic: Software Model Checking via Counter-Example Guided Abstraction Refinement There are easily two dozen SLAM/BLAST/MAGIC papers; I will skim. #3 SLAM Overview INPUT: Program
Testing. ECE/CS 5780/6780: Embedded System Design. Why is testing so hard? Why do testing?
Testing ECE/CS 5780/6780: Embedded System Design Scott R. Little Lecture 24: Introduction to Software Testing and Verification What is software testing? Running a program in order to find bugs (faults,
Timing Analysis of Event-Driven Programs with Directed Testing
iming Analysis of Event-Driven Programs with Directed esting Mahdi Eslamimehr Hesam Samimi {eslamimehr, samimi@ucla.edu Communications Design Group, SAP Labs alk Outline Introduction oyota UA Case Problem
Programm- & Systemverifikation
Programm- & Systemverifikation Assignment 1 Georg Weissenbacher 184.741 Part 1 of Assignment 1 Your task: Implement a balanced (AVL) binary search tree. Part 1 of Assignment 1 Your task: Implement a balanced
CS 430 Spring Mike Lam, Professor. Data Types and Type Checking
CS 430 Spring 2015 Mike Lam, Professor Data Types and Type Checking Type Systems Type system Rules about valid types, type compatibility, and how data values can be used Benefits of a robust type system
Lecture 15 Notes Binary Search Trees
Lecture 15 Notes Binary Search Trees 15-122: Principles of Imperative Computation (Spring 2016) Frank Pfenning, André Platzer, Rob Simmons 1 Introduction In this lecture, we will continue considering ways
Lecture 15 Binary Search Trees
Lecture 15 Binary Search Trees 15-122: Principles of Imperative Computation (Fall 2017) Frank Pfenning, André Platzer, Rob Simmons, Iliano Cervesato In this lecture, we will continue considering ways to
Directed Random Testing*
Directed Random Testing* Wolfram Schulte Microsoft Research Soqua 11/2006 Wasformerlyanouncedas: ChalengeProblemsinTesting 1 What my team does Static program verification & language design Verifying multi-threaded
Seminar in Software Engineering Presented by Dima Pavlov, November 2010
Seminar in Software Engineering-236800 Presented by Dima Pavlov, November 2010 1. Introduction 2. Overview CBMC and SAT 3. CBMC Loop Unwinding 4. Running CBMC 5. Lets Compare 6. How does it work? 7. Conclusions
CSE 565 Computer Security Fall 2018
CSE 565 Computer Security Fall 2018 Lecture 16: Building Secure Software Department of Computer Science and Engineering University at Buffalo 1 Review A large number of software vulnerabilities various
Lecture 10 Notes Linked Lists
Lecture 10 Notes Linked Lists 15-122: Principles of Imperative Computation (Spring 2016) Frank Pfenning, Rob Simmons, André Platzer 1 Introduction In this lecture we discuss the use of linked lists to
Automated Whitebox Fuzz Testing. by - Patrice Godefroid, - Michael Y. Levin and - David Molnar
Automated Whitebox Fuzz Testing by - Patrice Godefroid, - Michael Y. Levin and - David Molnar OUTLINE Introduction Methods Experiments Results Conclusion Introduction Fuzz testing is an effective Software
Lecture 8: Pointer Arithmetic (review) Endianness Functions and pointers
CSE 30: Computer Organization and Systems Programming Lecture 8: Pointer Arithmetic (review) Endianness Functions and pointers Diba Mirza University of California, San Diego 1 Q: Which of the assignment
Static Program Analysis Part 1 the TIP language
Static Program Analysis Part 1 the TIP language http://cs.au.dk/~amoeller/spa/ Anders Møller & Michael I. Schwartzbach Computer Science, Aarhus University Questions about programs Does the program terminate
Software Testing CS 408. Lecture 11: Review 2/20/18
Software Testing CS 408 Lecture 11: Review 2/20/18 Lecture 1: Basics 2 Two Views Verification: Prove the absence, and conjecture the presence, of bugs Ex: types: Not all ill-typed programs are wrong But,
Model Checking with Abstract State Matching
Model Checking with Abstract State Matching Corina Păsăreanu QSS, NASA Ames Research Center Joint work with Saswat Anand (Georgia Institute of Technology) Radek Pelánek (Masaryk University) Willem Visser
From Program to Logic: An Introduction
From Program to Logic: An Introduction Patrice Godefroid and Shuvendu K. Lahiri Microsoft Research, Redmond, WA, USA Abstract. We review, compare and discuss several approaches for representing programs
Verification & Validation of Open Source
Verification & Validation of Open Source 2011 WORKSHOP ON SPACECRAFT FLIGHT SOFTWARE Gordon Uchenick Coverity, Inc Open Source is Ubiquitous Most commercial and proprietary software systems have some open
Copyright 2008 CS655 System Modeling and Analysis. Korea Advanced Institute of Science and Technology
The Spin Model Checker : Part I Copyright 2008 CS655 System Korea Advanced Institute of Science and Technology System Spec. In Promela Req. Spec. In LTL Overview of the Spin Architecture Spin Model pan.c
Testing & Symbolic Execution
Testing & Symbolic Execution Software Testing The most common way of measuring & ensuring correctness Input 2 Software Testing The most common way of measuring & ensuring correctness Input Observed Behavior
Comp Sci 1MD3 Mid-Term II 2004 Dr. Jacques Carette
Comp Sci 1MD3 Mid-Term II 2004 Dr. Jacques Carette Name: Student No.: Duration : 50 minutes This midterm contains 18 questions on 4 pages This midterm will be marked out of 50. There are 60 total marks
Profile-Guided Program Simplification for Effective Testing and Analysis
Profile-Guided Program Simplification for Effective Testing and Analysis Lingxiao Jiang Zhendong Su Program Execution Profiles A profile is a set of information about an execution, either succeeded or
Software Model Checking. Xiangyu Zhang
Software Model Checking Xiangyu Zhang Symbolic Software Model Checking CS510 S o f t w a r e E n g i n e e r i n g Symbolic analysis explicitly explores individual paths, encodes and resolves path conditions
Implementing an abstract datatype. Linked lists and queues
Computer Programming Implementing an abstract datatype. Linked lists and queues Marius Minea marius@cs.upt.ro 19 December 2016 Review: compilation basics Briefly: Compiler translates source code to executable
Symbolic Execu.on. Suman Jana
Symbolic Execu.on Suman Jana Acknowledgement: Baishakhi Ray (Uva), Omar Chowdhury (Purdue), Saswat Anand (GA Tech), Rupak Majumdar (UCLA), Koushik Sen (UCB) What is the goal? Tes.ng Tes%ng approaches are
A CutEr Tool. Kostis Sagonas
A CutEr Tool Kostis Sagonas Overview Testing demo: unit, property-based, and concolic testing in Erlang Concolic execution for Erlang demo Support for type specifications short demo CutEr: A Concolic Unit
n HW7 due in about ten days n HW8 will be optional n No CLASS or office hours on Tuesday n I will catch up on grading next week!
Announcements SMT Solvers, Symbolic Execution n HW7 due in about ten days n HW8 will be optional n No CLASS or office hours on Tuesday n I will catch up on grading next week! n Presentations n Some of
CMSC 330: Organization of Programming Languages. OCaml Imperative Programming
CMSC 330: Organization of Programming Languages OCaml Imperative Programming CMSC330 Fall 2017 1 So Far, Only Functional Programming We haven t given you any way so far to change something in memory All
CS32 Discussion Week 3
CS32 Discussion Week 3 Muhao Chen muhaochen@ucla.edu http://yellowstone.cs.ucla.edu/~muhao/ 1 Outline Doubly Linked List Sorted Linked List Reverse a Linked List 2 Doubly Linked List A linked list where
Lecture 10 Notes Linked Lists
Lecture 10 Notes Linked Lists 15-122: Principles of Imperative Computation (Summer 1 2015) Frank Pfenning, Rob Simmons, André Platzer 1 Introduction In this lecture we discuss the use of linked lists to
C Review. MaxMSP Developers Workshop Summer 2009 CNMAT
C Review MaxMSP Developers Workshop Summer 2009 CNMAT C Syntax Program control (loops, branches): Function calls Math: +, -, *, /, ++, -- Variables, types, structures, assignment Pointers and memory (***
Research on Fuzz Testing Framework based on Concolic Execution
017 International Conference on Computer Science and Application Engineering (CSAE 017) ISBN: 978-1-60595-505-6 Research on uzz Testing ramework based on Concolic Execution Xiong Xie and Yuhang Chen *
Symbolic Execution for Bug Detection and Automated Exploit Generation
Symbolic Execution for Bug Detection and Automated Exploit Generation Daniele Cono D Elia Credits: Emilio Coppa SEASON Lab season-lab.github.io May 27, 2016 1 / 29 Daniele Cono D Elia Symbolic Execution
Scalable Test Generation by Interleaving Concrete and Symbolic Execution
Scalable Test Generation by Interleaving Concrete and Symbolic Execution Xiaoke Qin and Prabhat Mishra Department of Computer and Information Science and Engineering University of Florida, Gainesville
CS61C Machine Structures. Lecture 3 Introduction to the C Programming Language. 1/23/2006 John Wawrzynek. www-inst.eecs.berkeley.
CS61C Machine Structures Lecture 3 Introduction to the C Programming Language 1/23/2006 John Wawrzynek (www.cs.berkeley.edu/~johnw) www-inst.eecs.berkeley.edu/~cs61c/ CS 61C L03 Introduction to C (1) Administrivia
Symbolic Execution for Software Testing: Three Decades Later
doi:10.1145/2408776.2408795 The challenges and great promise of modern symbolic execution techniques, and the tools to help implement them. By Cristian Cadar and Koushik Sen Symbolic Execution for Software
Applications of Program analysis in Model-Based Design
Applications of Program analysis in Model-Based Design Prahlad Sampath (Prahlad.Sampath@mathworks.com) 2018 by The MathWorks, Inc., MATLAB, Simulink, Stateflow, are registered trademarks of The MathWorks,
1 A Brief Introduction To GDB
1 A Brief Introduction To GDB GDB, the GNU Project debugger, allows you to see what is going on inside another program while it executes or what another program was doing at the moment it crashed. GDB
MEMORY MANAGEMENT TEST-CASE GENERATION OF C PROGRAMS USING BOUNDED MODEL CHECKING
FEDERAL UNIVERSITY OF AMAZONAS INSTITUTE OF COMPUTING GRADUATE PROGRAM IN COMPUTER SCIENCE MEMORY MANAGEMENT TEST-CASE GENERATION OF C PROGRAMS USING BOUNDED MODEL CHECKING Herbert Rocha, Raimundo Barreto,
speller.c dictionary contains valid words, one per line 1. calls load on the dictionary file
mispellings speller.c 1. calls load on the dictionary file dictionary contains valid words, one per line 2. calls check on each word in the text file and prints all misspelled words 3. calls size to determine
CS558 Programming Languages
CS558 Programming Languages Fall 2016 Lecture 3a Andrew Tolmach Portland State University 1994-2016 Formal Semantics Goal: rigorous and unambiguous definition in terms of a wellunderstood formalism (e.g.
Low level security. Andrew Ruef
Low level security Andrew Ruef What s going on Stuff is getting hacked all the time We re writing tons of software Often with little regard to reliability let alone security The regulatory environment
CSCI 171 Chapter Outlines
Contents CSCI 171 Chapter 1 Overview... 2 CSCI 171 Chapter 2 Programming Components... 3 CSCI 171 Chapter 3 (Sections 1 4) Selection Structures... 5 CSCI 171 Chapter 3 (Sections 5 & 6) Iteration Structures
(Section : Computer Science)
(Section : Computer Science) 26. What will happen if we compile and execute the following program? static int count = 20; int main(void) { int i = 0; static int count = 6; while (i < count) { i++; count--;
Targeted Test Input Generation Using Symbolic Concrete Backward Execution
Targeted Test Input Generation Using Symbolic Concrete Backward Execution [Extended Abstract] ABSTRACT Peter Dinges University of Illinois Urbana Champaign, USA pdinges@acm.org Knowing inputs that cover
CSCI-243 Exam 1 Review February 22, 2015 Presented by the RIT Computer Science Community
CSCI-243 Exam 1 Review February 22, 2015 Presented by the RIT Computer Science Community http://csc.cs.rit.edu History and Evolution of Programming Languages 1. Explain the relationship between machine
Lecture Notes on Binary Search Trees
Lecture Notes on Binary Search Trees 15-122: Principles of Imperative Computation Frank Pfenning André Platzer Lecture 16 March 17, 2015 1 Introduction In this lecture, we will continue considering ways
CS61, Fall 2012 Section 2 Notes
CS61, Fall 2012 Section 2 Notes (Week of 9/24-9/28) 0. Get source code for section [optional] 1: Variable Duration 2: Memory Errors Common Errors with memory and pointers Valgrind + GDB Common Memory Errors
CONTENTS. PART 1 Structured Programming 1. 1 Getting started 3. 2 Basic programming elements 17
List of Programs xxv List of Figures xxix List of Tables xxxiii Preface to second version xxxv PART 1 Structured Programming 1 1 Getting started 3 1.1 Programming 3 1.2 Editing source code 5 Source code
Bounded Model Checking Of C Programs: CBMC Tool Overview
Workshop on Formal Verification and Analysis Tools, CFDVS, IIT-Bombay - Feb 21,2017 Bounded Model Checking Of C Programs: CBMC Tool Overview Prateek Saxena CBMC Developed and Maintained by Dr Daniel Kröning
Agenda. Peer Instruction Question 1. Peer Instruction Answer 1. Peer Instruction Question 2 6/22/2011
CS 61C: Great Ideas in Computer Architecture (Machine Structures) Introduction to C (Part II) Instructors: Randy H. Katz David A. Patterson http://inst.eecs.berkeley.edu/~cs61c/sp11 Spring 2011 -- Lecture
THE GOOD, BAD AND UGLY ABOUT POINTERS. Problem Solving with Computers-I
THE GOOD, BAD AND UGLY ABOUT POINTERS Problem Solving with Computers-I The good: Pointers pass data around efficiently Pointers and arrays 100 104 108 112 116 ar 20 30 50 80 90 ar is like a pointer to
// Initially NULL, points to the dynamically allocated array of bytes. uint8_t *data;
Creating a Data Type in C Bytes For this assignment, you will use the struct mechanism in C to implement a data type that represents an array of bytes. This data structure could be used kind of like a
CMSC 330: Organization of Programming Languages. OCaml Imperative Programming
CMSC 330: Organization of Programming Languages OCaml Imperative Programming CMSC330 Spring 2018 1 So Far, Only Functional Programming We haven t given you any way so far to change something in memory
Recitation: Cache Lab & C
15-213 Recitation: Cache Lab & C Jack Biggs 16 Feb 2015 Agenda Buffer Lab! C Exercises! C Conventions! C Debugging! Version Control! Compilation! Buffer Lab... Is due soon. So maybe do it soon Agenda Buffer
Having a BLAST with SLAM
Having a BLAST with SLAM # #2 Topic: Software Model Checking via Counter-Example Guided Abstraction Refinement There are easily two dozen SLAM/BLAST/MAGIC papers; I will skim. #3 SLAM Overview INPUT: Program
Software Security: Vulnerability Analysis
Computer Security Course. Software Security: Vulnerability Analysis Program Verification Program Verification How to prove a program free of buffer overflows? Precondition Postcondition Loop invariants
CS 11 C track: lecture 5
CS 11 C track: lecture 5 Last week: pointers This week: Pointer arithmetic Arrays and pointers Dynamic memory allocation The stack and the heap Pointers (from last week) Address: location where data stored
Types and Type Inference
CS 242 2012 Types and Type Inference Notes modified from John Mitchell and Kathleen Fisher Reading: Concepts in Programming Languages, Revised Chapter 6 - handout on Web!! Outline General discussion of
Lecture Notes on Contracts
Lecture Notes on Contracts 15-122: Principles of Imperative Computation Frank Pfenning Lecture 2 August 30, 2012 1 Introduction For an overview the course goals and the mechanics and schedule of the course,
for (i=1; i<=100000; i++) { x = sqrt (y); // square root function cout << x+i << endl; }
Ex: The difference between Compiler and Interpreter The interpreter actually carries out the computations specified in the source program. In other words, the output of a compiler is a program, whereas
Static Analysis in C/C++ code with Polyspace
1 Static Analysis in C/C++ code with Polyspace Yongchool Ryu Application Engineer gary.ryu@mathworks.com 2016 The MathWorks, Inc. 2 Agenda Efficient way to find problems in Software Category of Static
Static program checking and verification
Chair of Software Engineering Software Engineering Prof. Dr. Bertrand Meyer March 2007 June 2007 Slides: Based on KSE06 With kind permission of Peter Müller Static program checking and verification Correctness