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CS265: Program Analysis, Testing, and Debugging

CS265: Program Analysis, Testing, and Debugging. Lecture 1 Koushik Sen EECS, UC Berkeley. My Background. Assistant Professor in CS since Fall 2006 Office: Parlab (581 Soda Hall) Email: ksen@cs Ph.D. and M.S. in Computer Science University of Illinois at Urbana Champaign (2001-2006)

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CS265: Program Analysis, Testing, and Debugging

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  1. CS265: Program Analysis, Testing, and Debugging Lecture 1 KoushikSen EECS, UC Berkeley

  2. My Background • Assistant Professor in CS since Fall 2006 • Office:Parlab (581 Soda Hall) • Email: ksen@cs • Ph.D. and M.S. in Computer Science • University of Illinois at Urbana Champaign (2001-2006) • Spent 1 year in software industry as a software developer • B-Tech • IIT Kanpur (1995-1999) • Research Interests: • Software Engineering, • Programming Languages, and • Formal Methods • Verification, Testing, Model Checking, Program Analysis

  3. Please introduce yourselves

  4. Logistics

  5. Course Goals • To help students start research in the area of program analysis, model checking, testing, and debugging of sequential, concurrent, and distributed systems. • To help students to apply the techniques learned in this course in their ongoing research in other areas such as operating systems, computer networks, security, and database systems. • Should meet your Th+Sys prelims requirement

  6. Course Communication • All class materials will be on the website • http://fa09.pbwiki.com/ • See website for announcements • Class meets on Monday and Wednesday, 1:00 PM – 2:30 PM at 320 Soda Hall • Office hour by appointment

  7. Course Structure • We will study papers • You will read a paper and write a review of the paper before each class • There will be some guest lectures • You need to do 2-3 programming assignments • Test generation • Dynamic analysis of concurrent programs • Points-to analysis • Project in teams of 1-2.

  8. Course Grading • Reviews and class participation: 30% • 2-3 homework assignments: 30% • Project: 40% • A project must involve new research • Some sample projects will be posted online • Choose a project topic by 25th September, 2009 • 1-2 page project proposal due by 9th October, 2009 • A 5-7 minute mid-semester project presentation • Final project demo or presentation • 6 page project report in ACM SIGPLAN conference format

  9. About the course • This course is about software reliability • Why is software reliability important? • As society becomes more dependent on software, the consequences of software failures are non-trivial. • Money lost. • Lives lost. • Market share lost. • Clients lost. • Jobs lost.

  10. Software Bug => Space Disaster • Ariane 5 Space mission • $7, 000, 000, 000 • 10 Years in the making • 40 seconds after take off the rocket exploded

  11. Software Bug => Space Disaster Attempt to cram a 64-floating point number to a 16-bit integer failed

  12. On January 15, 1990, one of AT&T's #4ESS toll switching systems in New York City experienced an intermittent failure that caused a major service outage. Wrong BREAK statement in C-Code Complete coverage could have revealed this bug during testing /* ``C'' Fragment to Illustrate AT&T Defect */ do { switch expression { ... case (value): if (logical) { … break; } else { … } case (value2): … } … } while (expression) AT&T long distance service failed for 9 hours

  13. Software Bugs: Cause of Deaths • Several deaths of cancer patients were due to overdoses of radiation resulting from a race condition between concurrent tasks in the Therac-25 software.

  14. 180 Degree Bug Torpedoes, that deviate more than 90 degree, explode to avoid self destruction of the ship. Once upon a time a ship fired a torpedo but the torpedo was jammed in the tube. Then the captain gave the command: Let's turn around and return to the harbour! What happened next is no mystery.

  15. Cost of Failure • Software failures were estimated to cost the US economy about $60 billion annually [NIST 2002] • Improvements in software testing infrastructure may save one-third of this cost • Testing accounts for an estimated 50%-80% of the cost of software development [Beizer 1990]

  16. Methods for Building Reliable Software Safe Programming Languages and Type systems Testing Model Checking and Theorem Proving Static Program Analysis Dynamic Program Analysis Model Based Software Development and Analysis

  17. Methods for Building Reliable Software Safe Programming Languages and Type systems Testing Model Checking and Theorem Proving Static Program Analysis Dynamic Program Analysis Model Based Software Development and Analysis

  18. Course Contents • Automated test generation • Software model checking and various theoretical results that form the foundation of software model checking • Concurrent program analysis • Abstract interpretation and points-to analysis

  19. Course Contents • Automated test generation • Software model checking and various theoretical results that form the foundation of software model checking • Concurrent program analysis • Abstract interpretation and points-to analysis

  20. Automated Test Generation • Korat: • Reading assignment: Korat: Automated Testing Based on Java Predicates. Chandrasekhar Boyapati, SarfrazKhurshid, DarkoMarinov (ISSTA 2002) • Must submit review to the course wiki by 9/8 11:59 PM • Concolic Testing: Homework 1

  21. Automated Test Generation • Generate test inputs • To reveal bugs: assertion violation, crashes, wrong output • Improve software reliability • Often cannot prove program correct • Need to check program for all possible inputs • Input domain is often infinite • Pick inputs to satisfy certain coverage criteria • Generate all legal inputs of bounded size • Generate all legal inputs for full branch coverage

  22. Course Contents • Automated test generation • Software model checking and various theoretical results that form the foundation of software model checking • Concurrent program analysis • Abstract interpretation and points-to analysis

  23. Software Model Checking • Attempt to prove programs correct • Abstract domain • Create an abstraction of the program • Show that the abstraction does not contain a “bad state” • Predicate abstraction and boolean programs • Successfully used for model checking device drivers

  24. Software Model Checking • BLAST: predicate abstraction • Decidability results for various boolean program models • Forms theoretical foundation of various software model checking algorithms • Pushdown systems • Pushdown systems with multiset • Petri-Nets • Multi-pushdown systems with bounded context switch

  25. Course Contents • Automated test generation • Software model checking and various theoretical results that form the foundation of software model checking • Concurrent program analysis • Abstract interpretation and points-to analysis

  26. Concurrent Program Analysis • Bugs due to concurrency are notorious • Intermittent and hard to reproduce • Common causes: data race, atomicity violations, deadlocks, and other synchronization issues • Much more difficult to analyze than sequential programs • Need to check program • all schedules • all inputs • Pushdown system with stacks => undecidable

  27. Concurrent Program Analysis • Classic dynamic race detection algorithms • Atomicity checking and deadlock analysis • Explicit State Model Checking • Partial Order Reduction • Iterative preemption bounded model checking • Homework 2

  28. Course Contents • Automated test generation • Software model checking and various theoretical results that form the foundation of software model checking • Concurrent program analysis • Abstract interpretation and points-to analysis

  29. Abstract Interpretation and Points-to Analysis • Abstract interpretation • Note software model checking is an instantiation of abstract interpretation • forms basis of most static program analyses • MayurNaik on Points-to analysis • The most important analysis • Anderson’s points-to analysis • BDDs (Binary Decision Diagrams) for scalable points-to analysis • And a homework

  30. Course Contents • Automated test generation • Software model checking and various theoretical results that form the foundation of software model checking • Concurrent program analysis • Abstract interpretation and points-to analysis

  31. Summary • 3 homework assignments will give you hands-on experience of program analysis • In my opinion, this is quite useful to get a deep understanding of the subject, and • to jump start research in program analysis

  32. Symbolic Execution • Use symbolic values for input variables • Execute the program symbolically on symbolic input values • Collect symbolic path constraints • Use constraint solver to generate test inputs for each execution path

  33. 1 0 1 0 1 0 0 1 1 1 0 1 Computation Tree: Execution Paths of a Program • Can be seen as a binary tree with possibly infinite depth • Computation tree • Each node represents the execution of a “if then else” statement • Each edge represents the execution of a sequence of non-conditional statements • Each path in the tree represents an equivalence class of inputs

  34. Example of Computation Tree void test_me(int x, int y) { if(2*x==y){ if(x != y+10){ printf(“I am fine here”); } else { printf(“I should not reach here”); ERROR; } } } 2*x==y N Y x!=y+10 N Y ERROR

  35. intx = read(); inty = read(); intt; t = x; x = x + y; if (x > y) { y = 2*t; if (x+1 == y) { assert(false); } } Concrete Execution

  36. intx = read(); inty = read(); intt; t = x; x = x + y; if (x > y) { y = 2*t; if (x+1 == y) { assert(false); } } x = 4 y = 9 t = 4 x = 13 (13 > 9) y = 8 (14 == 8) END Concrete Execution

  37. intx = read(); inty = read(); intt; t = x; x = x + y; if (x > y) { y = 2 * t; if (x+1 == y) { assert(false); } } Symbolic Execution (true | x = x0 ) (true | x = x0,y=y0) (true | x = x0,y=y0,t=x0) (true | x = x0+y0,y=y0,t=x0) x0+y0 > y0 (x0>0 | x = x0+y0,y=2x0,t=x0) (x0 <= 0 | x = x0+y0,y=y0,t=x0) x0+y0 + 1== 2x0 (x0 > 0 && y0+ 1  x0 | x = x0+y0,y=2x0,t=x0) (x0 > 0 && y0+ 1 == x0 | x = x0+y0,y=2x0,t=x0)

  38. int x = read(); int y = read(); int t; t = x; x = x + y; if (x > y) { y = 2 * t; if (x+1 == y) { assert(false); } } Test Generation (true | x = x0 ) Solve: x0 > 0 && y0+1 = x0 Solution: x = 2, y=1 (true | x = x0,y=y0) (true | x = x0,y=y0,t=x0) Solve: x0<= 0 Solution: x = 0, y=0 (true | x = x0+y0,y=y0,t=x0) x0+y0 > y0 (x0 <= 0 | x = x0+y0,y=y0,t=x0) (x0 > 0 | x = x0+y0,y=2x0,t=x0) x0+y0 + 1== 2x0 Solve: x0 > 0 && y0+1  x0 Solution: x = 1, y=9 (x0 > 0 && y0+ 1  x0 | x = x0+y0,y=2x0,t=x0) (x0 > 0 && y0+ 1 == x0 | x = x0+y0,y=2x0,t=x0)

  39. Abstract Interpretation: Test to Proof Example ( ) { int new = read(); lock = 0; 1: do{ lock = 1; old = new; b = read(); 2: if (b){ 3: lock=0; new ++; } 4:} while(new != old); 5: assert (lock==1); }

  40. Abstract Interpretation: Test to Proof Example ( ) { int new = read(); lock = 0; 1: do{ lock = 1; old = new; b = read(); 2: if (b){ 3: lock=0; new ++; } 4:} while(new != old); 5: assert (lock==1); } Predicates:LOCK, new==old

  41. Abstract Interpretation: Test to Proof Example ( ) { int new = read(); lock = 0; 1: do{ lock = 1; old = new; b = read(); 2: if (b){ 3: lock=0; new ++; } 4:} while(new != old); 5: assert (lock==1); } 1 : LOCK 2 LOCK , new==old 3 LOCK , new==old 4 4 LOCK , new=old : LOCK , : new = old 1 5 : LOCK, : new == old Predicates:LOCK, new==old Reachability Tree

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