Thomas Fahringer's Advanced Symbolic Analysis for Compilers: New Techniques and PDF


By Thomas Fahringer

ISBN-10: 3540011854

ISBN-13: 9783540011859

The target of software research is to immediately verify the p- perties of a application. instruments of software program improvement, corresponding to compilers, p- formance estimators, debuggers, reverse-engineering instruments, application veri?- tion/testing/proving structures, application comprehension platforms, and software specializationtoolsarelargelydependentonprogramanalysis. Advancedp- gram research can: aid to ?nd software error; realize and music performan- severe code areas; verify assumed constraints on facts will not be violated; tailor a wide-spread application to fit a speci?c program; reverse-engineer so- ware modules, and so forth. A favourite software research process is symbolic a- lysis, which has attracted vast realization for a few years because it isn't depending on executing a software to ascertain the semantics of a application, and it could yield very stylish formulations of many analyses. in addition, the complexity of symbolic research might be mostly self sustaining of the enter info dimension of a software and of the dimensions of the desktop on which this system is being accomplished. during this e-book we current novel symbolic regulate and knowledge ?ow repres- tation concepts in addition to symbolic ideas and algorithms to investigate and optimize courses. application contexts which de?ne a brand new symbolic - scription of software semantics for regulate and information ?ow research are on the heart of our method. we've got solved a few difficulties encountered in application research through the use of software contexts. Our resolution tools are e?cient, flexible, uni?ed, and extra basic (they do something about usual and abnormal codes) than such a lot current methods.

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Additional resources for Advanced Symbolic Analysis for Compilers: New Techniques and Algorithms for Symbolic Program Analysis and Optimization

Example text

Array A is undefined (⊥100 ). After the last statement, array A has symbolic value A = ⊥100 ⊕ (1, x) ⊕ (1 − x, x + 1) ⊕ (x, x) ⊕ (1 + x, x + 1). The left-most ⊕-function relates to the first assignment of program in Ex. 1 — the right-most one to the last statement. We can give a simplified representation of A since the last two statements overwrite the values of the first two statements. After simplification, variable A has the symbolic value ⊥100 ⊕ (x, x) ⊕ (1 + x, x + 1). For simplifying ⊕-chains we need to find out if two symbolic expressions are equal or not.

Vl }. The function performs three steps: First, the boundary condition is stripped off from the program context [s, t, p] before entering the loop. s0 = {v1 = eval(v1 , [s, t, p]), . . 12) Second, in order to symbolically analyze the loop body, we compute an initial program context. This program context comprises the generic symbolic values for the recurrence variables and the loop condition. s′ = δ(s; v1 = v1 , . . 15) Function loopeval expresses recurrence variables as symbolic recurrences by µ-functions.

After the loop terminates, the values of variables I, P, hc and hp are determined by their recurrences whereby the recurrence index z is derived from the loop exit condition. Closed forms can be found for I, P, hc. The heap hp in ℓ7 symbolically describes all elements of the singly linked list. 7 depicts the dynamically allocated records. Nil-references are marked by a special symbol. 1 next: r ✲ next: r ✲ 2 item: 1 item: 2 k ........... ✲ next: r ✲ item: k z ........... ✲ next: r item: z Fig. 7.

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Advanced Symbolic Analysis for Compilers: New Techniques and Algorithms for Symbolic Program Analysis and Optimization by Thomas Fahringer

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