Download Advanced Symbolic Analysis for Compilers: New Techniques and by Thomas Fahringer PDF

By Thomas Fahringer

The goal of software research is to immediately be sure the p- perties of a software. instruments of software program improvement, comparable to compilers, p- formance estimators, debuggers, reverse-engineering instruments, application veri?- tion/testing/proving platforms, application comprehension platforms, and application specializationtoolsarelargelydependentonprogramanalysis. Advancedp- gram research can: aid to ?nd software blunders; notice and song performan- serious code areas; be sure assumed constraints on info will not be violated; tailor a regularly occurring application to fit a speci?c software; reverse-engineer so- ware modules, and so forth. A well known software research process is symbolic a- lysis, which has attracted sizeable recognition for a few years because it isn't really depending on executing a application to check the semantics of a software, and it might probably yield very stylish formulations of many analyses. additionally, the complexity of symbolic research should be principally self sufficient of the enter facts dimension of a application and of the dimensions of the laptop on which this system is being achieved. during this publication we current novel symbolic keep an eye on and information ?ow repres- tation recommendations in addition to symbolic recommendations and algorithms to research and optimize courses. software contexts which de?ne a brand new symbolic - scription of software semantics for keep an eye on and information ?ow research are on the heart of our procedure. we now have solved a few difficulties encountered in application research through the use of software contexts. Our answer equipment are e?cient, flexible, uni?ed, and extra normal (they take care of average and abnormal codes) than so much latest methods.

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

Example text

E. ρ(a(z), i). The symbolic expression z is the number of loop iterations determined by the loop exit condition and i is the index of the accessed element. Furthermore, the recurrence index k is bound to 0 ≤ k ≤ z. To determine a possible ⊕-function, where the accessed element is written, a potential index set Xl (i) of the l-th ⊕-function is computed. ∀1 ≤ l ≤ m : Xl (i) = { k| βl (k) = i ∧ 0 ≤ k ≤ z} Xl (i) contains all possible βl (k), equal to the index i (0 ≤ k ≤ z). If an index set has more than one element, the array element i is written in different loop iterations by the l-th ⊕-function.

2 is an irregular code and consists of two loops. An array A is written in the first loop. The array element values are functions over the loop index variable and a loop invariant. In the second loop, A is used as an index array for array W. Statement ℓ2 assigns array A value M-I which is symbolically described by A = A ⊕ (m − I, I). The symbolic recurrence of A is described after the loop in statement ℓ3 . We rewrite the symbolic recurrence µ(A, s0 , [s′2 , t′2 , p′2 ]) in a mathematical notation as, 30 3.

Symbolic Analysis of Programs I(0) = 1 I(k + 1) = I(k) + 1, A(0) = ⊥m k≥0 A(k + 1) = A(k) ⊕ (m − I(k)), I(k)), z = min{k|I(k) > m − 1} k≥0 where the symbolic value of A is A(z) since z determines the last iteration of the recurrence. In order to resolve the indirect access of Statement ℓ4 we compute the closed form of I which is I(k) = 1 + k and replace I(K) with its closed form. Then, the index of the last iteration z can be rewritten to z = m − 1 and we now know that A(m − 1) represents the symbolic array description of array A after Statement ℓ3 .

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