In order to take advantage of multi-core hardware, more and more applications are becoming multi-threaded. Unfortunately concurrent programs are prone to bugs, such as data races. Recently much work has been devoted to detecting data races in multi-threaded programs. Most tools, however, require the accurate knowledge of synchronizations in the program, and may otherwise suffer from false positives in race detection, limiting their usability. To address this problem, some tools such as Intel® Inspector provide mechanisms for suppressing false positives and/or annotating synchronizations not automatically recognized by the tools. However, they require users' input or even changes of the source code.

Computer systems often fail due to many factors such as software bugs or administrator errors. Diagnosing such production run failures is an important but challenging task since it is difficult to reproduce them in house due to various reasons: (1) unavailability of users' inputs and file content due to privacy concerns; (2) difficulty in building the exact same execution environment; and (3) non-determinism of concurrent executions on multi-processors.

Software testing is hard. The emergence of multicore architectures and the proliferation of bug-prone multithreaded software makes testing even harder. To this end, researchers have proposed methods to continue testing software after deployment, e.g., in vivo (IV) testing and delta execution (DE) patch testing. These on-line techniques typically fork new processes to hide the functional impact of testing. Unfortunately, the high overhead of fork() significantly degrades performance. To reduce the performance overhead of online testing, we adapt transactional memory (TM) to isolate the functional effects of tests with minimal impact on system performance. We propose StealthTest, an interface that exposes TM transactions as the key mechanism for executing tests. It allows online tests to work on a consistent view of memory without the overhead of creating new processes. Moreover, explicitly aborting the test transaction after it is done guarantees that its changes are invisible to the rest of the system. Thus, StealthTest promises transparent online testing. We demonstrate two test frameworks utilizing StealthTest - StealthlV and StealthDE - that improve on previously proposed fork-based in vivo testing and delta execution frameworks respectively. Implementing StealthTest on top of three software TM (STM) systems - TL2 STM, Intel STM and a pin-based STM- we demonstrate that StealthTest-based frameworks can (a) run a wide range of online tests and (b) execute many more tests with low overhead. StealthTest provides another motivation for efficient TM implementations by extending TM's applicability to a critical software engineering challenge.

Software systems are constantly changing. Patches to fix bugs and patches to add features are all too common. Every change risks breaking a previously working system. Hence administrators loathe change, and are willing to delay even critical security patches until after fully validating their correctness. Compared to off-line validation, on-line validation has clear advantages since it tests against real life workloads. Yet unfortunately it imposes restrictive overheads as it requires running the old and new versions side-by-side. Moreover, due to spurious differences (e.g. event timing, random number generation, and thread interleavings), it is difficult to compare the two for validation.

Bug reproduction is critically important for diagnosing a production-run failure. Unfortunately, reproducing a concurrency bug on multi-processors (e.g., multi-core) is challenging. Previous techniques either incur large overhead or require new non-trivial hardware extensions.