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1	Dynamic Dead Variable Analysis Lewis, Micah S. 18 August 2005 (has links) (PDF) Dynamic dead variable analysis (DDVA) extends traditional static dead variable analysis (SDVA) in the context of model checking through the use of run-time information. The analysis is run multiple times during the course of model checking to create a more precise set of dead variables. The DDVA is evaluated based on the amount of memory used to complete model checking relative to SDVA while considering the extra overhead required to implement DDVA. On several models with a complex control flow graph, DDVA reduces the amount of memory needed by 38-88MB compared to SDVA with a cost of 36 bytes of memory verhead. On several models with loops, DDVA achieved no additional reduction compared to SDVA while requiring no more memory than SDVA. static analysis model checking abstraction live variable analysis dead variable analysis verification Computer Sciences
2	On-the-Fly Dynamic Dead Variable Analysis Self, Joel P. 22 March 2007 (has links) (PDF) State explosion in model checking continues to be the primary obstacle to widespread use of software model checking. The large input ranges of variables used in software is the main cause of state explosion. As software grows in size and complexity the problem only becomes worse. As such, model checking research into data abstraction as a way of mitigating state explosion has become more and more important. Data abstractions aim to reduce the effect of large input ranges. This work focuses on a static program analysis technique called dead variable analysis. The goal of dead variable analysis is to discover variable assignments that are not used. When applied to model checking, this allows us to ignore the entire input range of dead variables and thus reduce the size of the explored state space. Prior research into dead variable analysis for model checking does not make full use of dynamic run-time information that is present during model checking. We present an algorithm for intraprocedural dead variable analysis that uses dynamic run-time information to find more dead variables on-the-fly and further reduce the size of the explored state space. We introduce a definition for the maximal state space reduction possible through an on-the-fly dead variable analysis and then show that our algorithm produces a maximal reduction in the absence of non-determinism. model checking computer science static static analysis dynamic analysis analysis program program analysis Estes GDB dead variable analysis dead variable live variable live variable analysis CFG abstraction data abstraction on-the-fly static program analysis control flow graph software model checking state space depth first search state explosion state explosion problem Computer Sciences
3	Vylepšení analýzy živých proměnných pomocí points-to analýzy / Improvement of Live Variable Analysis Using Points-to Analysis Raiskup, Pavel January 2012 (has links) Languages such as C use pointers very heavily. Implementation of operations on dynamically linked structures is, however, quite difficult. This can cause the programmer to make more mistakes than usual. One method for dealing with this situation is to use the static analysis tools. This thesis elaborates on the extension to the Code Listener architecture which is an interface for building static analysis tools. Code Listener is able to construct a call-graph or a control flow graph for a given source code and send it to the analyzing tool. One ability of the architecture is that it can conduct the live variable analysis internally. It detects places in the control flow graph where some subset of variables may be killed. The problem was that every variable for which a pointer address was assigned could not been killed, before. This decision had been made because there was no assurance that the variable could never been used through the pointer. So the goal of this work was to design and incorporate a points-to analysis which is able to exclude some references from the set of considered pointers to improve the live variable analysis.

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