AI: a lightweight system for tolerating concurrency bugs

Abstract

Concurrency bugs are notoriously difficult to eradicate during software testing because of their non-deterministic nature. Moreover, fixing concurrency bugs is time-consuming and error-prone. Thus, tolerating concurrency bugs during production runs is an attractive complementary approach to bug detection and testing. Unfortunately, existing bug-tolerating tools are usually either 1) constrained in types of bugs they can handle or 2) requiring roll-back mechanism, which can hitherto not be fully achieved efficiently without hardware supports.This paper presents a novel program invariant, called Anticipating Invariant (AI), which can help anticipate bugs before any irreversible changes are made. Benefiting from this ability of anticipating bugs beforehand, our software-only system is able to forestall the failures with a simple thread stalling technique, which does not rely on execution roll-back and hence has good performance Experiments with 35 real-world concurrency bugs demonstrate that AI is capable of detecting and tolerating most types of concurrency bugs, including both atomicity and order violations. Two new bugs have been detected and confirmed by the corresponding developers. Performance evaluation with 6 representative parallel programs shows that AI incurs negligible overhead (< 1%) for many nontrivial desktop and server applications.

Efficiently detecting concurrency bugs in persistent memory programs

“…Non-deadlock bugs include two major categories: atomicity-violation bugs and order-violation bugs [38]. Researchers have developed many tools to detect [26,39,55] or fix [64,66] concurrency bugs based on the two patterns. However, concurrency bugs for PM are substantially different from conventional DRAMbased concurrency bugs due to persistency dimension and crashconsistency models (e.g., durable linearizability [23]).…”

“…However, both branch coverage and PM path coverage fail to represent the context changes of thread interleavings. In order to explore different thread interleavings, there are three categories of techniques: multiple runs with random scheduler, delay injection [33,47,60,66], and interleaving enumeration [18,25]. Unlike existing schemes, PMRace leverages the crash-consistency patterns in PM programs and focuses on the buggy read-after-write interleavings, thus significantly reducing the search space of interleavings.…”

Repairing Event Race Errors by Controlling Nondeterminism

“…Other techniques steer away from nondeterministic errors by postponing selected actions, much like our approach but for multi-threaded programs. The AI technique [32] attempts to stall threads where manifestation of a concurrency bug is about to become deterministic. Kivati [6] uses static analysis and hardware watchpoints to detect atomicity violations and then dynamically reorders the relevant instructions.…”

Concurrency Bug Avoiding Based on Optimized Software Transactional Memory

“…e currently ubiquitous multicore architecture necessitates concurrent programming, especially multithreaded programming [1][2][3]. Unfortunately, writing concurrent programs is challenging: multithreaded programs often suffer from concurrency bugs such as deadlocks, data races, atomicity violations, and order violations [4], which are notoriously difficult to expose [5,6], detect [6][7][8], debug [8], and repair [9,10] because of their nondeterminism nature.…”