BIFER: a biphasic trace filter approach to scalable prediction of concurrency errors

Abstract

Predictive trace analysis (PTA), a static trace analysis technique for concurrent programs, can offer powerful capability support for finding concurrency errors unseen in a previous program execution. Existing PTA techniques always face considerable challenges in scaling to large traces which contain numerous critical events. One main reason is that an analyzed trace includes not only redundant memory accessing events and threads that cannot contribute to discovering any additional errors different from the found candidate ones, but also many residual synchronization events which still affect PTA to check whether these candidate ones are feasible or not even after removing the redundant events. Removing them from the trace can significantly improve the scalability of PTA without affecting the quality of the PTA results. In this paper, we propose a biphasic trace filter approach, BIFER in short, to filter these redundant events and residual events for improving the scalability of PTA to expose general concurrency errors. In addition, we design a model which indicates the lock history and the happens-before history of each thread with two kinds of ways to achieve the efficient filtering. We implement a prototypical tool BIFER for Java programs on the basis of a predictive trace analysis framework. Experiments show that BIFER can improve the scalability of PTA during the process of analyzing all of the traces.

Keywords

predictive trace analysis / concurrency errors / scalability

References

[1] Sen K, Rosu G, Agha G. Detecting errors in multithreaded programs by generalized predictive analysis of executions. In: Proceedings of the 7th IFIP WG6.1 International Conference on Formal Methods for Open Object-based Distributed Systems. 2005, 211−226

[2] Wang L, Stoller S. Accurate and efficient runtime detection of atomicity errors in concurrent programs. In: Proceedings of ACM International Conference on Principles and Practice of Parallel Programming. 2006, 137−146

[3] Farzan A, Madhusudan P, Sorrentino F. Meta-analysis for atomicity violations under nested locking. In: Proceedings of the 21st International Conference on Computer Aided Verification. 2009, 248−262

[4] Chen F, Serbanuta T F, Rosu G. jPredictor: a predictive runtime analysis tool for Java. In: Proceedings of the 30th International Conference on Software Engineering. 2008, 221−230

[5] Wang C, Kundu S, Ganai M K, Gupta A. Symbolic predictive analysis for concurrent programs. In: Proceedings of International Symposium on Formal Methods. 2009, 256−272

[6] Wang C, Limaye R, Ganai M K, Gupta A. Trace-based symbolic analysis for atomicity violations. In: Proceedings of the 16th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. 2010, 328−342

[7] Kahlon V, Wang C. Universal causality graphs: a precise happensbefore model for detecting bugs in concurrent programs. In: Proceedings of the 22nd International Conference on Computer Aided Verification. 2010, 434−449

[8] Zhang W, Sun C, Lu S. Conmem: detecting severe concurrency bugs through an effect-oriented approach. ACM SIGPLAN Notices, 2010, 45(3): 179−192

[9] Vaziri M, Tip F, Dolby J. Associating synchronization constraints with data in an object-oriented language. ACM SIGPLAN Notices, 2006, 41(1): 334−345

[10] Huang J, Zhou J G, Zhang C. Scaling predictive analysis of concurrent programs by removing trace redundancy. ACM Transactions on Software Engineering and Methodology, 2013, 22(1): 8

[11] Lu S, Park S, Seo E, Zhou Y Y. Learning from mistakes: a comprehensive study on real world concurrency bug characteristics. ACM SIGPLAN Notices, 2008, 43(3): 329−339

[12] Lucia B, Wood B P, Ceze L. Isolating and understanding concurrency errors using reconstructed execution fragments. ACM SIGPLAN Notices, 2011, 46(6): 378−388

[13] Shi Y, Park S, Yin Z N, Lu S, Zhou Y Y, Chen W G, Zheng W M. Do I use the wrong definition?: DeFuse: definition-use invariants for detecting concurrency and sequential bugs. ACM SIGPLAN Notices, 2010, 45(10): 160−174

[14] Wang C, Said M, Gupta A. Coverage guided systematic concurrency testing. In: Proceedings of the 33rd International Conference on Software Engineering. 2011, 221−230

[15] Lai Z, Cheung S C, Chan W K. Detecting atomic-set serializability violations in multithreaded programs through active randomized testing. In: Proceedings of the 32nd International Conference on Software Engineering. 2010, 235−244

[16] Huang J, Zhang C. Persuasive prediction of concurrency access anomalies. In: Proceedings of the 2011 International Symposium on Software Testing and Analysis. 2011, 144−154

[17] Savage S, Burrows M, Nelson G, Sobalvarro P, Anderson T E. Eraser: a dynamic data race detector for multithreaded programs. ACM Transactions on Computer Systems, 1997, 15(4): 391−411

[18] Sen K. Race directed random testing of concurrent programs. ACM SIGPLAN Notices, 2008, 43(6): 11−21

[19] Farchi E, Nir Y, Ur S. Concurrent bug patterns and how to test them. In: Proceedings of IEEE International Parallel and Distributed Processing Symposium. 2003

[20] Flanagan C, Godefroid P. Dynamic partial-order reduction for model checking software. ACM SIGPLAN Notices, 2005, 40(1): 110−121

[21] Sinha N, Wang C. Staged concurrent program analysis. In: Proceedings of the 18th ACM SIGSOFT International Symposium on the Foundations of Software Engineering. 2010, 47−56

[22] Serebryany K, Potapenko A, Iskhodzhanov T, Vyukov D. Dynamic race detection with LLVM compiler. In: Proceedings of the 2nd International Conference on Runtime Verification. 2011, 110−114

[23] Sack P, Bliss B E, Ma Z Q, Petersen P, Torrellas J. Accurate and efficient filtering for the intel thread checker race detector. In: Proceedings of the 1st Workshop on Architectural and System Support for Improving Software Dependability. 2006, 34−41

[24] Marino D, Musuvathi M, Narayanasamy S. Literace: effective sampling for lightweight data-race detection. ACM SIGPLAN Notices, 2009, 44(6): 134−143

[25] Bond M D, Coons K E, McKinley K S. Pacer: proportional detection of data races. ACM SIGPLAN Notices, 2010, 45(6): 255−268

[26] Yu Y, Rodeheffer T, W C. Racetrack: efficient detection of data race conditions via adaptive tracking. ACM SIGOPS Operating Systems Review, 2005, 39(5): 221−234

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg