Efficient Dynamic Analysis for Detecting and Tolerating Program Anomalies

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Businesses, transportation, economies, and science all increasingly depend on software. To manage these demands, programmers have turned to managed languages such as Java, Ruby, and C#, because they help produce higher quality software faster due to garbage collection and type safety. TOIBE fnds that most new software is using Java or other managed languages. Although these languages reduce errors, programs still contain memory and semantic errors. NIST estimates that software errors cost the US economy $59 billion a year. Because most prior tools for finding bugs slow programs down by factors and/or add substantial memory overheads, they are typically employed only during testing or after a crash, not during production.This research will explore new approaches for detecting, reporting, and sometimes tolerating errors that are efficient enough to use in deployed software. The techniques include new, low overhead compiler analyses, runtime instrumentation, and runtime algorithms. A key feature will be very low memory and modest time overheads that make them appealing for use in deployed software, especially in multicore where processes are competing for memory resources. A unique aspect will be that the garbage collector will summarize data structures in the heap to discover program patterns and anomalies. The proposed work will detect, report, and tolerate memory leaks that would otherwise crash the program. The research impact from this project will be higher quality software with fewer bugs that keeps executing correctly in spite of some errors, tools that help users by keeping software running, and tools that help developers find, diagnose, and fix errors. The investigator will make the tools publicly available, train graduate students, and mentor to increase minority participation in computer science.