1. Consider the method m1() in Question 2. Consider the AOR[1] and the ROR[2] mutationoperators.a. What is the minimum number of mutants necessary for coverage comparable to the strongestgraph coverage criteria? Justify.b. List such minimum set of mutants.c. Pick one of your mutants and provide the reachability conditions, infection conditions,propagation conditions, and test case values to kill that mutant.1. public int m1() {2.3.outVal = 1;4.5.6.7.8.9.10.}11.12.}13.14.02 = new C2();GPA 02.m2(); (stub)if GPA > 3.5 {outVal outVal * 10 + 10%;wwwwwwwwwwwwwif major "ICS" {outVal = outVal * 2;wwwwwwwwwoutVal outVal / 2;wwwwwwwwwwwwwwwwwwwwelsif GPA 2.5 {wwwwwwoutVal = 60 30 ✶ outVal;wwwwwww-wwwwwwwww15.}16.else {17.18.}19.outVal = 20 outVal + 20;wwwwwwwwreturn outVal;wwwwwwwwwwwwwwww20. }

a) To achieve coverage comparable to the strongest graph coverage criteria, you would need at least…

Answered: 1. Consider the method m1() in Question…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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3. Consider the following network b) Describe in detail how the Kernighan-Lin algorithm works and apply it, step by step, to the network above, starting with the two subsets (a, c, f) and (b, d, e).
Define a fully connected network with two hidden layers with Nh1 and Nh2 features, respectively. The hidden layers should use relu activation and the final layer should not have any activation. a linear network with no hidden layer. We will use this network to study the benefit of depth, or equivalently using a non-linear network instead of a linear network. The final layer should not have any activation class NeuralNet(N.Module): # YOUR CODE HERE class LinearNet(N.Module): #YOUR CODE HERE
As a matter of fact, the Wiener filter is most popular filter and is used for restoration. The main limitation of the earlier discussed methods viz., Inverse filtering and Pseudo-Inverse filtering is that they are sensitive to noise (The Wiener filter exploits the statistical properties of the image and can be used to restore images in the presence of blur as well as noise. Let f(x, y), g(x, y) and f(x, y) be zero mean random sequences. Zero-mean sequences imply E[f(x, y)] = 0, E[g(x, y)] = 0 and E[f(x, y)] = 0. Similarly, stationary sequences can be defined in terms of correlation as under: E[ƒ (x, y).ƒ (i, j)]=rf ( x − i, y − 1) | E[g (x, y).g (i, j)] = rgg (x-i, y-j) f 88 → Auto correlation Elf (x, y). g(i, j)] = g(x-i, yj) → cross correlation The zero mean image model is given by the following expression. List the major drawbacks of Wieners filter.
3. You are given a code-under-test, Converter; two sets of test cases generated using two different techniques which are Technique A and Technique B; and two mutants. Analyse the given information using mutation testing analysis and determine which technique is the most effective one. For the technique that less effective, generate additional test case to enhance the effectiveness. 1 public void Converter (int num) { if (num =1) { 2 3 System.out.println("A two digit number"); 4 } else if (num =100) { System.out.println("A three digit number"); } else if (num =1000) { System.out.println("A four digit number"); } else if (num =10000) { System.out.println("A five digit number"); } else { System.out.println("Number is not between 1 & 99999"); } Mutant# Line # Original Code Mutated Code Mutant 1 8 if (num =1000) System.out.println("A four digit number"); if (num 1000) System.out.println("A five. digit number"); Mutant 2 9 Test cases were generated from two different techniques: Technique A…
Consider the problem of searching for genes in DNA sequences using Boyer-Moore algorithm. A DNA sequence is represented by a text on the alphabet {A, C, G, T}, and the gene segment is the pattern. Choose the problem instance for your group. (Choose the text and the pattern for your group.) If you choose a different problem instance, your answer will not be accepted and you will get zero for your solution.Construct the bad-symbol shift (?1) table and the good-suffix shift (?2) table.
Problem 6. Let ? = {?, ?, ?, ?}. Define ? on ? as follows: ? = {(?, ?), (?, ?), (?, ?), (?, ?), (?, ?)}. (b) Find the symmetric closure of R.
Create an SVM to calculate the XOR function. Use the values +1 / -1 for inputs and outputs. Map the input [x1,x2] to the space with coordinates [x1,x2x1]. Draw the four possible points of the training set and their maximum linear separator in this space. What is its edge?
Use Python code

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