Reversible debuggers have been developed at least since 1970. Such a feature is useful when the cause of a bug is close in time to the bug manifestation. When the cause is far back in time, one resorts to setting appropriate breakpoints in the debugger and beginning a new debugging session. For these cases when the cause of a bug is far in time from its manifestation, bug diagnosis requires a series of debugging sessions with which to narrow down the cause of the bug. For such "difficult" bugs, this work presents an automated tool to search through the process lifetime and locate the cause. As an example, the bug could be related to a program invariant failing. A binary search through the process lifetime suffices, since the invariant expression is true at the beginning of the program execution, and false when the bug is encountered. An algorithm for such a binary search is presented within the FReD (Fast Reversible Debugger) software. It is based on the ability to checkpoint, restart and deterministically replay the multiple processes of a debugging session. It is based on GDB (a debugger), DMTCP (for checkpoint-restart), and a custom deterministic record-replay plugin for DMTCP. FReD supports complex, real-world multithreaded programs, such as MySQL and Firefox. Further, the binary search is robust. It operates on multi-threaded programs, and takes advantage of multi-core architectures during replay.
Both IGOR @cite_21 and the work by Boothe @cite_14 support a primitive type of reverse expression watchpoint for single-threaded applications of the form x>0 , where the left-hand side of the expression is a variable and the right-hand side is a constant. x is also a monotone variable. On the other hand, FReD supports general expressions.
@cite_14 @cite_21
1523041988 2046137117 2162899660 2083878525
A traditional counterexample to a linear-time safety property shows the values of all signals at all times prior to the error. However, some signals may not be critical to causing the failure. A succinct explanation may help human understanding as well as speed up algorithms that have to analyze many such traces. In Bounded Model Checking (BMC), a counterexample is constructed from a satisfying assignment to a Boolean formula, typically in CNF. Modern SAT solvers usually assign values to all variables when the input formula is satisfiable. Deriving minimal satisfying assignments from such complete assignments does not lead to concise explanations of counterexamples because of how CNF formulae are derived from the models. Hence, we formulate the extraction of a succinct counterexample as the problem of finding a minimal assignment that, together with the Boolean formula describing the model, implies an objective. We present a two-stage algorithm for this problem, such that the result of each stage contributes to identify the “interesting” events that cause the failure. We demonstrate the effectiveness of our approach with an example and with experimental results. We present a new approach to programming languages for parallel computers that uses an effect system to discover expression scheduling constraints. This effect system is part of a 'kinded' type system with three base kinds: types , which describe the value that an expression may return; effects , which describe the side-effects that an expression may have; and regions , which describe the area of the store in which side-effects may occur. Types, effects and regions are collectively called descriptions . Expressions can be abstracted over any kind of description variable -- this permits type, effect and region polymorphism. Unobservable side-effects can be masked by the effect system; an effect soundness property guarantees that the effects computed statically by the effect system are a conservative approximation of the actual side-effects that a given expression may have. The effect system we describe performs certain kinds of side-effect analysis that were not previously feasible. Experimental data from the programming language FX indicate that an effect system can be used effectively to compile programs for parallel computers. This paper deals with termination proofs for Higher-Order Rewrite Systems (HRSs), introduced in [12]. This formalism combines the computational aspects of term rewriting and simply typed lambda calculus. The result is a proof technique for the termination of a HRS, similar to the proof technique “Termination by interpretation in a well-founded monotone algebra”, described in [8, 19]. The resulting technique is as follows: Choose a higher-order algebra with operations for each function symbol in the HRS, equipped with some well-founded partial ordering. The operations must be strictly monotonic in this ordering. This choice generates a model for the HRS. If the choice can be made in such a way that for each rule the interpretation of the left hand side is greater than the interpretation of the right hand side, then the HRS is terminating. At the end of the paper some applications of this technique are given, which show that this technique is natural and can easily be applied. In standard control-flow analyses for higher-order languages, a single abstract binding for a variable represents a set of exact bindings, and a single abstract reference cell represents a set of exact reference cells. While such analyses provide useful may-alias information, they are unable to answer mustalias questions about variables and cells, as these questions ask about equality of specific bindings and references.In this paper, we present a novel program analysis for higher-order languages that answers must-alias questions. At every program point, the analysis associates with each variable and abstract cell a cardinality, which is either single or multiple. If variable x is single at program point p, then all bindings for x in the heap reachable from the environment at p hold the same value. If abstract cell r is single at p, then at most one exact cell corresponding to r is reachable from the environment at p.Must-alias information facilitates various program optimizations such as lightweight closure conversion [19]. In addition, must-alias information permits analyses to perform strong updates [3] on abstract reference cells known to be single. Strong updates improve analysis precision for programs that make significant use of state.A prototype implementation of our analysis yields encouraging results. Over a range of benchmarks, our analysis classifies a large majority of the variables as single.