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Mining Behavior Graphs for Backtrace of Noncrashing Bugs Chao Liu, Xifeng Yan, Hwanjo Yu, Jiawei Han University of Illinois at Urbana-Champaign Philip.

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Presentation on theme: "Mining Behavior Graphs for Backtrace of Noncrashing Bugs Chao Liu, Xifeng Yan, Hwanjo Yu, Jiawei Han University of Illinois at Urbana-Champaign Philip."— Presentation transcript:

1 Mining Behavior Graphs for Backtrace of Noncrashing Bugs Chao Liu, Xifeng Yan, Hwanjo Yu, Jiawei Han University of Illinois at Urbana-Champaign Philip S. Yu IBM T. J. Watson Research Presented by: Chao Liu

2 Outline Motivations Related Work Classification of Program Executions Extract Backtrace from Classification Dynamics Case Study Conclusions

3 Motivations Software is full of bugs –Windows 2000, 35M LOC 63,000 known bugs at the time of release, 2 per 1000 lines Software failure costs –Ariane 5 explosion is due to errors in the software of the inertial reference system (Ariaen-5 flight 501 inquiry board report –A study by the National Institute of Standards and Technology found that software errors cost the U.S. economy about $59.5 billion annually Testing and debugging are laborious and expensive –50% of my company employees are testers, and the rest spends 50% of their time testing! --Bill Gates, in 1995 Courtesy to CNN.com

4 Bug Localization Automatically circle out the most suspicious places Two kinds of bugs w.r.t. symptoms –Crashing bugs Typical symptoms: segmentation faults Reasons: memory access violations –Noncrashing bugs Typical symptoms: smooth executions but unexpected outputs Reasons: logic or semantic errors An example

5 Running Example Subject program –replace: perform regular expression matching and substitutions –563 lines of C code –17 functions are involved Execution behaviors –130 out of 5542 test cases fail to give correct outputs –No incorrect executions incur segmentation faults Debug method –Step-by-step tracing void subline(char *lin, char *pat, char *sub) { int i, lastm, m; lastm = -1; i = 0; while((lin[i] != ENDSTR)) { m = amatch(lin, i, pat, 0); if (m >= 0){ putsub(lin, i, m, sub); lastm = m; } if ((m == -1) || (m == i)){ fputc(lin[i], stdout); i = i + 1; } else i = m; } void subline(char *lin, char *pat, char *sub) { int i, lastm, m; lastm = -1; i = 0; while((lin[i] != ENDSTR)) { m = amatch(lin, i, pat, 0); if ((m >= 0) && (lastm != m) ){ putsub(lin, i, m, sub); lastm = m; } if ((m == -1) || (m == i)){ fputc(lin[i], stdout); i = i + 1; } else i = m; }

6 Debugging Crashes

7 Bug Localization via Backtrace Backtrace for noncrashing bugs? Major challenges –No abnormality is visible on the surface. –When and where the abnormality happens.

8 Outline Motivations Related Work Classification of Program Executions Extract Backtrace from Classification Dynamics Case Study Conclusions

9 Related Work Crashing bugs –Memory access monitoring Purify [HJ92], Valgrind [SN00], GDB … Noncrashing bugs –Static program analysis –Traditional model checking –Model checking source code

10 Static Program Analysis Methodology –Examine source code directly –Enumerate all the possible execution paths without running the program –Check user-specified properties, e.g. free(p) …… (*p) lock(res) …… unlock(res) receive_ack() … … send_data() Strengths –Check all possible execution paths Problems –Shallow semantics –Properties should be directly mapped to source code structure Tools –ESC [DRL+98], LCLint [EGH+94], ESP [DLS02], MC Checker [ECC00] … ×

11 Traditional Model Checking Methodology –Model program computation as finite state machines –It is described with a particular description language –Exhaustively explore all the reachable states in checking desired or undesired properties Strengths –Model deeper semantics –Naturally fit in checking event-driven systems, like protocols Problems –Significant amount of manual efforts in modeling –State space explosion Tools –SMV [M93], SPIN [H97], Murphi [DDH+92] …

12 Model Checking Source Code Methodology –Execute the real program in a sandbox (e.g., virtual machine) –Manipulate event happenings, e.g., Message incomings Return value of memory allocation Strengths –Less significant manual specification Problems –Application restrictions, e.g., Event-driven programs (still) Clear mapping between source code and logic event Tools –CMC [MPC+02], Verisoft [G97], Java PathFinder [BHP+-00] …

13 Summary of Related Work In summary, –Semantic inputs are necessary Program model Properties to be checked (all three methods) –Restricted application domain Event-driven model Properties are also event-related.

14 void subline(char *lin, char *pat, char *sub) { int i, lastm, m; lastm = -1; i = 0; while((lin[i] != ENDSTR)) { m = amatch(lin, i, pat, 0); if (m > 0){ putsub(lin, i, m, sub); lastm = m; } if ((m == -1) || (m == i)){ fputc(lin[i], stdout); i = i + 1; } else i = m; } void subline(char *lin, char *pat, char *sub) { int i, lastm, m; lastm = -1; i = 0; while((lin[i] != ENDSTR)) { m = amatch(lin, i, pat, 0); if (m >= 0){ putsub(lin, i, m, sub); lastm = m; } if ((m == -1) || (m == i)){ fputc(lin[i], stdout); i = i + 1; } else i = m; } Example Revisited void subline(char *lin, char *pat, char *sub) { int i, lastm, m; lastm = -1; i = 0; while((lin[i] != ENDSTR)) { m = amatch(lin, i, pat, 0); if (m >= 0){ putsub(lin, i, m, sub); lastm = m; } if ((m == -1) || (m == i)){ fputc(lin[i], stdout); i = i + 1; } else i = m; } void subline(char *lin, char *pat, char *sub) { int i, lastm, m; lastm = -1; i = 0; while((lin[i] != ENDSTR)) { m = amatch(lin, i, pat, 0); if ((m >= 0) && (lastm != m) ){ putsub(lin, i, m, sub); lastm = m; } if ((m == -1) || (m == i)){ fputc(lin[i], stdout); i = i + 1; } else i = m; } No memory violations Not event-driven program No explicit error properties

15 Outline Motivations Related Work Classification of Program Executions Extract Backtrace from Classification Dynamics Case Study Conclusions

16 Synopsis of Program Execution Program behavior graphs –Function-level abstraction of program behaviors –Function calls and transitions –First-order sequential information about function interactions int main(){... A();... B(); } int A(){... } int B(){... C()... } int C(){... }

17 Identification of Incorrect Executions A two-class classification problem –Every execution gives one behavior graph –Edges and closed frequent subgraphs as features Is classification useful? –Classification itself does not work for bug localization Classifier only labels each run as either correct or incorrect as a whole It does not tell when and where abnormality happens Observations –Good classifiers know the differences between correct and incorrect execution Difference, a kind of abnormality? –Where and when does abnormality happens? Incremental classification ?

18 Outline Motivations Related Work Classification of Program Executions Extract Backtrace from Classification Dynamics Case Study Conclusions

19 Incremental Classification Classification works only when instances from two classes are different. Precision as a measure of the difference. Incremental classification Observe accuracy dynamics

20 Illustration: Precision Boost main AA BC D B C D One Correct ExecutionOne Incorrect Execution EE F G F G H

21 Bug Relevance Precision boost –For each function F: Precision boost = Exit precision - Entrance precision. –Intuition & heuristics Differences take place within the execution of F Abnormality happens while F is in the stack The larger the boost, the more likely F is relevant to the bug Bug-relevant function

22 Outline Related Work Classification of Program Executions Extract Backtrace from Classification Dynamics Case Study Conclusions

23 Case Study Subject program –replace: perform regular expression matching and substitutions –563 lines of C code –17 functions are involved Execution behaviors –130 out of 5542 test cases fail to give correct outputs –No incorrect executions incur segmentation faults Task –Can we circle out the backtrace for this bug? void subline(char *lin, char *pat, char *sub) { int i, lastm, m; lastm = -1; i = 0; while((lin[i] != ENDSTR)) { m = amatch(lin, i, pat, 0); if (m >= 0){ putsub(lin, i, m, sub); lastm = m; } if ((m == -1) || (m == i)){ fputc(lin[i], stdout); i = i + 1; } else i = m; } void subline(char *lin, char *pat, char *sub) { int i, lastm, m; lastm = -1; i = 0; while((lin[i] != ENDSTR)) { m = amatch(lin, i, pat, 0); if ((m >= 0) && (lastm != m) ){ putsub(lin, i, m, sub); lastm = m; } if ((m == -1) || (m == i)){ fputc(lin[i], stdout); i = i + 1; } else i = m; }

24 Precision Pairs

25 Backtrace for Noncrashing Bugs

26 Outline Motivations Related Work Classification of Program Executions Extract Backtrace from Classification Dynamics Case Study Conclusions

27 Identify incorrect executions from program runtime behaviors. Classification dynamics can give away backtrace for noncrashing bugs without any semantic inputs. Data mining can contribute to software engineering and system researches in general. Mining into Software and Systems?

28 References [DRL+98] David L. Detlefs, K. Rustan, M. Leino, Greg Nelson and James B. Saxe. Extended static checking, 1998 [EGH+94] David Evans, John Guttag, James Horning, and Yang Meng Tan. LCLint: A tool for using specifications to check code. In Proceedings of the ACM SIG-SOFT '94 Symposium on the Foundations of Software Engineering, pages 87-96, [DLS02] Manuvir Das, Sorin Lerner, and Mark Seigle. Esp: Path-sensitive program verication in polynomial time. In Conference on Programming Language Design and Implementation, [ECC00] D.R. Engler, B. Chelf, A. Chou, and S. Hallem. Checking system rules using system-specic, programmer-written compiler extensions. In Proceedings of the Fourth Symposium on Operating Systems Design and Implementation, October [M93] Ken McMillan. Symbolic Model Checking. Kluwer Academic Publishers, 1993 [H97] Gerard J. Holzmann. The model checker SPIN. Software Engineering, 23(5): , [DDH+92] David L. Dill, Andreas J. Drexler, Alan J. Hu, and C. Han Yang. Protocol verication as a hardware design aid. In IEEE International Conference on Computer Design: VLSI in Computers and Processors, pages , [MPC+02] Madanlal Musuvathi, David Y.W. Park, Andy Chou, Dawson R. Engler and David L. Dill. CMC: A Pragmatic Approach to Model Checking Real Code. In Proceedings of the fifth Symposium on Operating Systems Design and Implementation, 2002.

29 References (contd) [G97] P. Godefroid. Model Checking for Programming Languages using VeriSoft. In Proceedings of the 24th ACM Symposium on Principles of Programming Languages, 1997 [BHP+-00] G. Brat, K. Havelund, S. Park, and W. Visser. Model checking programs. In IEEE International Conference on Automated Software Engineering (ASE), [HJ92] R. Hastings and B. Joyce. Purify: Fast Detection of Memory Leaks and Access Errors in Proceeding of the fthe Winter 1992 USENIX Conference, pages San Francisco, California [SN00] Julian Seward and Nick Nethercote. Valgrind, an open-source memory debugger for x86-GNU/Linux [LLM+04] Zhenmin Li, Shan Lu, Suvda Myagmar, Yuanyuan Zhou. CP-Miner: A Tool for Finding Copy-paste and Related Bugs in Operating System Code, in Proceeding of the 6th Symposium of Operating Systems Design and Implementation, 2004 [LCS+04] Zhenmin Li, Zhifeng Chen, Sudarshan M. Srinivasan, Yuanyuan Zhou. C-Miner: Mining Block Correlations in Storage Systems. In proceeding of the 3rd usenix conferences on file and storage technologies, 2004

30 Q & A Thank You!


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