Eraser: a dynamic data race detector for multithreaded programs
ACM Transactions on Computer Systems (TOCS)
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Protocol-based data-race detection
SPDT '98 Proceedings of the SIGMETRICS symposium on Parallel and distributed tools
Deterministic replay of Java multithreaded applications
SPDT '98 Proceedings of the SIGMETRICS symposium on Parallel and distributed tools
Yesterday, my program worked. Today, it does not. Why?
ESEC/FSE-7 Proceedings of the 7th European software engineering conference held jointly with the 7th ACM SIGSOFT international symposium on Foundations of software engineering
Bandera: extracting finite-state models from Java source code
Proceedings of the 22nd international conference on Software engineering
Toward integration of data race detection in DSM systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Simplifying and Isolating Failure-Inducing Input
IEEE Transactions on Software Engineering
Isolating failure-inducing thread schedules
ISSTA '02 Proceedings of the 2002 ACM SIGSOFT international symposium on Software testing and analysis
Model-Checking Multi-threaded Distributed Java Programs
Proceedings of the 7th International SPIN Workshop on SPIN Model Checking and Software Verification
Concurrent Bug Patterns and How to Test Them
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
A Classification of Concurrency Failures in Java Components
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Empirical evaluation of the tarantula automatic fault-localization technique
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Formal verification of concurrent software: two case studies
Proceedings of the 2006 workshop on Parallel and distributed systems: testing and debugging
Noise Makers Need to Know Where to be Silent - Producing Schedules That Find Bugs
ISOLA '06 Proceedings of the Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation
Proceedings of the 2007 ACM workshop on Parallel and distributed systems: testing and debugging
AtomRace: data race and atomicity violation detector and healer
PADTAD '08 Proceedings of the 6th workshop on Parallel and distributed systems: testing, analysis, and debugging
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
A platform for search-based testing of concurrent software
Proceedings of the 8th Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging
A trace simplification technique for effective debugging of concurrent programs
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Localizing defects in multithreaded programs by mining dynamic call graphs
TAIC PART'10 Proceedings of the 5th international academic and industrial conference on Testing - practice and research techniques
Testing of concurrent programs using genetic algorithms
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Injecting mechanical faults to localize developer faults for evolving software
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
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As concurrent and distributive applications are becoming more common and debugging such applications is very difficult, practical tools for automatic debugging of concurrent applications are in demand. In previous work, we applied automatic debugging to noise-based testing of concurrent programs. The idea of noise-based testing is to increase the probability of observing the bugs by adding, using instrumentation, timing "noise" to the execution of the program. The technique of finding a small subset of points that causes the bug to manifest can be used as an automatic debugging technique. Previously, we showed that Delta Debugging can be used to pinpoint the bug location on some small programs. In the work reported in this paper, we create and evaluate two algorithms for automatically pinpointing program locations that are in the vicinity of the bugs on a number of industrial programs. We discovered that the Delta Debugging algorithms do not scale due to the non-monotonic nature of the concurrent debugging problem. Instead we decided to try a machine learning feature selection algorithm. The idea is to consider each instrumentation point as a feature, execute the program many times with different instrumentations, and correlate the features (instrumentation points) with the executions in which the bug was revealed. This idea works very well when the bug is very hard to reveal using instrumentation, correlating to the case when a very specific timing window is needed to reveal the bug. However, in the more common case, when the bugs are easy to find using instrumentation points ranked high by the feature selection algorithm is not high enough. We show that for these cases, the important value is not the absolute value of the evaluation of the feature but the derivative of that value along the program execution path. As a number of groups expressed interest in this research, we built an open infrastructure for automatic debugging algorithms for concurrent applications, based on noise injection based concurrent testing using instrumentation. The infrastructure is described in this paper.