Bug isolation via remote program sampling

We propose a low-overhead sampling infrastructure for gathering information from the executions experienced by a program's user community. Several example applications illustrate ways to use sampled instrumentation to isolate bugs. Assertion-dense code can be transformed to share the cost of assertions among many users. Lacking assertions, broad guesses can be made about predicates that predict program errors and a process of elimination used to whittle these down to the true bug. Finally, even for non-deterministic bugs such as memory corruption, statistical modeling based on logistic regression allows us to identify program behaviors that are strongly correlated with failure and are therefore likely places to look for the error.

[1]  Samuel B. Williams,et al.  ASSOCIATION FOR COMPUTING MACHINERY , 2000 .

[2]  Barton P. Miller,et al.  Fuzz Revisited: A Re-examination of the Reliability of UNIX Utilities and Services , 1995 .

[3]  Martin C. Carlisle,et al.  Olden: parallelizing programs with dynamic data structures on distributed-memory machines , 1996 .

[4]  Geoffrey Smith,et al.  A Type-Based Approach to Program Security , 1997, TAPSOFT.

[5]  Lance M. Berc,et al.  Continuous profiling: where have all the cycles gone? , 1997, ACM Trans. Comput. Syst..

[6]  Michael D. Smith,et al.  Ephemeral Instrumentation for Lightweight Program Profiling , 1997 .

[7]  William G. Griswold,et al.  Dynamically discovering likely program invariants to support program evolution , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[8]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[9]  Chrysanthos Dellarocas,et al.  Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior , 2000, EC '00.

[10]  John Whaley,et al.  A portable sampling-based profiler for Java virtual machines , 2000, JAVA '00.

[11]  W. E. Weihl,et al.  Efficient and flexible value sampling , 2000, SIGP.

[12]  Matthew Arnold,et al.  A framework for reducing the cost of instrumented code , 2001, PLDI '01.

[13]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[14]  Andrew C. Myers,et al.  Untrusted hosts and confidentiality , 2001, SOSP.

[15]  Steven P. Reiss,et al.  Encoding program executions , 2001, Proceedings of the 23rd International Conference on Software Engineering. ICSE 2001.

[16]  Martin Hirzel,et al.  Bursty Tracing: A Framework for Low-Overhead Temporal Profiling , 2001 .

[17]  Alessandro Orso,et al.  Monitoring deployed software using software tomography , 2002, PASTE '02.

[18]  George C. Necula,et al.  CCured: type-safe retrofitting of legacy code , 2002, POPL '02.

[19]  John F. Canny,et al.  Collaborative filtering with privacy , 2002, Proceedings 2002 IEEE Symposium on Security and Privacy.

[20]  Sudheendra Hangal,et al.  Tracking down software bugs using automatic anomaly detection , 2002, ICSE '02.

[21]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Martin C. Rinard,et al.  Role-based exploration of object-oriented programs , 2002, ICSE '02.

[23]  Peter M. Broadwell,et al.  Scrash: A System for Generating Secure Crash Information , 2003, USENIX Security Symposium.