A number of possible enhancements to the reduction techniques presented here have been identified throughout the development of this work. They have not been made part of this work because of time constraints. However, are presented in this section in order to show some the possible extensions to these static state space reduction techniques, and which are still unexplored.
• Abstract interpretation. Throughout the definitions of the static analyzes presented in this work, it has been mentioned that no abstract interpretation of the byte-code is performed, thus limiting the scope of the analysis to a purely static approach.
Indeed, an abstract interpretation of the byte-code, including constant propagation and constant folding, may permit more accurate results in these analyzes and cor-respondingly, better state space reductions. For this, it would be useful to create a basic block representation of the control-flow graph as described in [ASU86]. Also, it would be required to apply this analysis to the values of the variables, registers and the operand stack.
• Determination of exclusive access of global variables. Consider a global variable which is used exclusively by one process type. Furthermore, assume that this pro-cess is instantiated only once during the execution of the system. Then, the global variable is really accessed as if it were a local variable of this process. An analysis that is able to detect this situation would allow dead variable reduction to be ap-plied to this variable even in the case of a concurrent system. Likewise, any accesses to this variable need not be considered as external operations when applying path reduction. Thus, both reductions would benefit from this analysis, which would need to determine which global variables are used by which process types, and also, if a given process type may be instantiated multiple times or not.
• Channel analysis. In [MT99], an analysis of Promela is described which determines statically which channel variables may be aliased among each other in any point of the execution of the program. Although this analysis is intended for the slicing of Promela models, the analyzes presented in this work may also benefit from such an analysis, as it offers more precise information about the relationship between channels and variables.
• Elimination of write-only and unused variables. In [Hol99], it is described how Spin detects write-only global variables as well as unused variables and completely re-moves them from the representation of each state. Whereas dead variable reduction causes every variable which is temporarily dead to have a default valuation, this reduction completely eliminates variables which are always dead. Since NIPS is likely to be used as a back-end for compilers, this task will commonly be done by the compiler itself. However, it is possible to implement this optimization directly in NIPS byte-code and thus permit its reuse among all tools which target NIPS.
• Program slicing. A program slicing technique for reducing the size of a Promela model with respect to a given temporal logic specification has been presented in [MT98, MT99]. It is therefore feasible to adapt this same technique to NIPS, which is straightforwardly implementable as a code transformation like the ones presented in this work.
• Partial order reduction. Partial order reduction is the best known state space reduction technique used in software model checking. A mostly-static version of partial order reduction like the one presented in [KLM+98] can be applied to NIPS.
This requires, however, an adaptation of the virtual machine so that it allows the exploration of successor states to be guided by a component that computes the ample sets dynamically, based on statically gathered information.
Thus far, the possibilities for state space reduction in NIPS are very promising, con-sidering the empirical results of this work. The development of future and enhanced
reductions based on the static analysis of NIPS byte-code will support the creation of novel model checking applications which make use of the NIPS virtual machine for the generation of state-transition systems.
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