Multicore is here to stay. To keep up with the hardware innovation, software developers must move from sequential programming to concurrent programming. However, this move is slow and challenging due to the exponential complexity in reasoning about concurrency. In particular, Heisenbugs such as data races, which are non-deterministic concurrency errors, pervasively infect concurrent software, making concurrent program debugging notoriously difficult.

In this dissertation, we develop several effective methods for debugging concurrent programs along four directions: multiprocessor deterministic replay, predictive trace analysis, trace simplification, and data sharing reduction. We first present LEAP, a lightweight record and replay system that makes Heisenbugs reproducible on mult...[ Read more ]

Multicore is here to stay. To keep up with the hardware innovation, software developers must move from sequential programming to concurrent programming. However, this move is slow and challenging due to the exponential complexity in reasoning about concurrency. In particular, Heisenbugs such as data races, which are non-deterministic concurrency errors, pervasively infect concurrent software, making concurrent program debugging notoriously difficult.

In this dissertation, we develop several effective methods for debugging concurrent programs along four directions: multiprocessor deterministic replay, predictive trace analysis, trace simplification, and data sharing reduction. We first present LEAP, a lightweight record and replay system that makes Heisenbugs reproducible on multi-core and multi-processors. Underpinned by a new local-order based replay theorem, LEAP is fast, portable, and deterministic. As long as a Heisenbug manifests once, LEAP is able to deterministically reproduce it in every subsequent execution, and more importantly, with much lower overhead compared to previous approaches.

We second present PECAN and TraceFilter, a persuasive predictive trace analysis system that predicts Heisenbugs from normal executions, and an efficient algorithm that significantly improves the scalability of predictive analysis by removing the trace redundancy. The salient feature of PECAN is that, in addition to predicting Heisenbugs, it generates a concrete execution that deterministically expose and validate the predicted bugs. With PECAN, programmers are provided with the full execution history and context information to understand the bug, which dramatically expedites the debugging process.

We third present LEAN and SimTrace, a dynamic and a static technique for simplifying concurrency bug reproduction through removing computational redundancy and validating trace equivalence. A simplified execution with fewer threads, fewer thread interleavings, and faster replay greatly reduces the debugging effort by reducing the number of places in the trace where we need to look for the cause of the bug and by speeding up the bug reproduction process.

We finally present Privateer, an execution privatization technique that soundly privatizes a subset of shared data accesses in a vast category of scheduler-oblivious concurrent programs. Underpinned by a privatization theorem, Privateer safely reduces the data sharing and isolates the erroneous thread interleavings without introducing any additional synchronization. With Privateer, many Heisenbugs are fixed and a wide range of concurrency problems are alleviated without impairing but, instead, improving the program performance.