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A new style of temporal debugging is proposed. The new URDB debugger can employ such techniques as temporal search for finding an underlying fault that is causing a bug. This improves on the standard iterative debugging style, which iteratively re-executes a program under debugger control in the search for the underlying fault. URDB acts as a meta-debugger, with current support for four widely used debuggers: gdb, MATLAB, python, and perl. Support for a new debugger can be added in a few hours. Among its points of novelty are: (i) the first reversible debuggers for MATLAB, python, and perl; (ii) support for todays multi-core architectures; (iii) reversible debugging of multi-process and distributed computations; and (iv) temporal search on changes in program expressions. URDB gains its reversibility and temporal abilities through the fast checkpoint-restart capability of DMTCP (Distributed MultiThreaded CheckPointing). The recently enhanced DMTCP also adds ptrace support, enabling one to freeze, migrate, and replicate debugging sessions.
Runtime nondeterminism is a fact of life in modern database applications. Previous research has shown that nondeterminism can cause applications to intermittently crash, become unresponsive, or experience data corruption. We propose Adaptive Interven
The development of many highly dynamic environments, like pervasive environments, introduces the possibility to use geographically close-related services. Dynamically integrating and unintegrating these services in running applications is a key chall
We develop a practical solution to the problem of automatic verification of the interface between device drivers and the OS. Our solution relies on a combination of improved driver architecture and verification tools. It supports drivers written in C
Evolution behaves like a tinkerer (Francois Jacob, Science, 1977). Software systems provide a unique opportunity to understand biological processes using concepts from network theory. The Debian GNU/Linux operating system allows us to explore the evo
Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and perfo