| In the era of multi-core systems, concurrent application development will most likely become the main archetype in software applications; and despite the effort and complexity required to develop concurrent software, utilizing the processing power of the rising rate of multi-core hardware will mandate that transition. Nevertheless, concurrent software is susceptible to concurrency problems and defects that can escape traditional testing and debugging techniques.;This research introduces practical tools and methodologies to exploit runtime execution traces to simplify the process of testing and debugging concurrent applications. First, we propose a coverage-guided systematic testing framework that uses runtime execution traces to learn ordering constraints over shared object accesses and select only high-risk interleaving in future tests. Thus, it can increase the coverage of important concurrency scenarios with a reasonable cost and detect most of the concurrency bugs in practice. Second, we utilize symbolic analysis, based on SMT solvers, to introduce two effective execution-replay techniques: (1) record and replay a shared-memory multi-threaded program for multi-processor execution; and (2) predictive framework to generate concurrent program schedules with data race defects. More specifically, the record and replay solution can construct shared-memory dependencies between threads off-line during replay. The proposed solution significantly reduces the complexity of hardware support required for enabling replay. Our prototype shows that constructing shared-memory dependencies off-line reduces the recording overhead to 1%. In the predictive framework, the tool searches for a concrete thread schedule with a data race in non-erroneous execution trace of a program. If such schedule exists, it will generate a complete execution schedule, called witness, which later can be used to trigger the data race by deterministically replaying the execution. The proposed framework eliminates the need to re-run and track the execution until a data race manifests which is usually required to capture a trace containing such defect. Evaluation results show that our analysis is scalable enough for a post-mortem analysis to help programmers better understand the data races. |
