1. Compiler Principles Fall 2015-2016 Compiler Principles Exercise Set 1: Lexical Analysis Roman Manevich Ben-Gurion University

2. Exercise 1 Assume the following lexical specification: R = (1-9)(0-9)*. (0-9)*(1-9) | 0. (0-9)*(1-9) D = (0-9)(0-9).(0-9)(0-9).(0-9)(0-9)(0-9)(0-9) Construct the scanner automaton and label the accepting states by the appropriate tokens Run it on the following inputs using the maximal munch algorithm 1.23.2 1.230.2 01.11.2015 2

3. Exercise 2 (2015 midterm) 3

4. Exercise 3 a)Given an example of a lexical specification R 1,…, R k and a word  in the language (R 1 | … | R k ) * for which the maximal munch policy does not yield “Success” b)[harder] Can you think of an algorithm that satisfies both properties (successfully splits any word in the language of R 1 | … | R k and produces the longest tokens)? 4

5. Exercise 4 Which of the following conditions yields worst-case linear running time for the naïve maximal munch algorithm: 1.All tokens have constant length 2.The languages of different tokens do not intersect 3.The languages of different tokens do not intersect 4.Each token starts with a unique letter and ends with a unique letter 5.The language of each token is prefix-closed (i.e., if w is accepted than each prefix of w is also accepted) Find conditions as weak as you can for which the worst-case running time of the naïve maximal munch algorithm is linear 5

6. Exercise 5 – research challenge The worst-case of Tom Reps’s algorithm is O(n  k) where n is the length of the input and k is the number of automaton states Can you find an algorithm that reduces the k constant factor to a constant? 6

7. Exercise 6 – research challenge Develop an asymptotically efficient parallel algorithm for maximal munch-based scanning 7