CS 211: Assignment 2: Graph Algorithms + Data Representation (150 points)

Introduction

In this assignment, you will get more experience with C programming by implementing several classical graph algorithms. You will also solidify your understanding of data representation with this assignment.

In the rst set of 6 programs, you will get more experience with allocating graph data structures and processing them. You will start by representing a graph data structure in C. Then, you will learn how to represent undirected and weighted directed graphs. Subsequently, you will implement two simple graph traversal algorithms, breadth- rst (BFS) search, and depth- rst (DFS) search. Using the implementation of DFS, you will write a program to perform single-source shortest path computation in directed acyclic graphs (DAGs). Subsequently, you will implement Dijkstra’s shortest path algorithm that is not limited to DAGs and can be used in all directed graphs with no negative edge weights.

In the last 4 programs, you will improve your understanding of data representation by writing programs to represent two’s complement values and oating point values.

Note that your program must follow the input-output guidelines listed in each section exactly, with no additional or missing output.

No cheating or copying will be tolerated in this class. Your assignments will be automatically checked with plagiarism detection tools that are pretty powerful. Hence, you should not look at your friend’s code. See CS department’s academic integrity policy at: http://nbacademicintegrity.rutgers.edu/


First: Undirected Graph Representation (10 Points)

A graph is a way of representing relations between a set of objects, called vertices. Each pairwise connection between vertices is called an edge. In computer science, graphs have many applications in modeling maps, computer networks, neural networks, circuits, etc.

For example, consider the graph in Figure 1(a) used to model the roads between di erent intersec-tions in a city. In this example, a vertex represents an intersection. An edge models a road between a pair of intersections. In graph terminology, a pair of vertices are adjacent if an edge connects them. For example, in Figure 1(a) the the vertex pair (A,B) are adjacent. Further, the degree of a vertex V is de ned as the number of vertices adjacent to it. For example, in Figure 1(a), vertex


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A        B



    • D


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C

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    • /


    • /

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/


Figure 1: (a) An undirected graph G (b) Adjacency list representation of graph G.


E has a degree of 2 since its only adjacent to vertex C and D.

There exist several standard ways to represent a graph on a computer. For this programming assignment, we are going to use the adjacency list representation. In the adjacency list representa-tion, each vertex stores a linked list of its adjacent vertices. Figure 1(b) illustrates the adjacency list representation of the undirected graph from Figure 1(a).

In this part, you write a program that will read an undirected graph from a le. Store it in an adjacency list representation and then answer simple graph queries.

Input-Output format: Your program will take two le names as its command-line input. The rst le includes the undirected graph. Your program reads the contents of this le and constructs the graph data structure. The rst line in this le provides the number of vertices in the graph. Next, each line contains the name of each vertex in the graph. Afterwards, each following line includes information about an edge in the graph. Each edge is described by the name of its pair of vertices, separated by a space.

The second le includes queries on the constructed graph. Each line contains a separate query that starts with the query type and a vertex, separated by a space. There are two query types. Degree queries start with the letter ’d’, followed by a space and the vertex’s name, which is a string. Upon processing a degree query, your program must print out the queried vertex’s degree, followed by a newline character. Adjacency queries start with the letter ’a’, followed by a space and the vertex’s name. Upon processing an adjacency query, your program must print out the vertices adjacent to the queried vertex, each vertex separated by a space and nally, a newline character. When you print the results of the adjacency query, the results have to be sorted lexicographically.

Example Execution:

Let’s assume we have the following graph and query    le:

graph.txt

5

A

B

C

D


2


10

A    B

12
8    5

9
C    D

5
7    3
E


(a)




0

1

2

3

4




A


B

C

D

E




C
8







A
10







E
5
/






C
9
/






C
7





(b)




D    12    /


D    5    /




D    3    /



Figure 2: (a) A weighted directed graph G (b) Adjacency list representation of graph G.

E

    • B

    • C

    • D B D C D C E D E

query.txt:

    • E a C
    • A a A

Then the result will be:

$./first graph.txt query.txt

2

A D E

3

B C D

We will not give you improperly formatted les. You can assume all your input les will be in proper format, as stated above.

Note: A vertex can be any valid identi er (i.e., a string consisting of characters [a-z] and [0-9] that is terminated by a space).


Second: Weighted Directed Graph Representation (10 Points)

Consider the example graph in Figure 1(a). While this graph models the existence of a road between a pair of intersections, it doesn’t capture the distance between them. Further, some roads

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may not be bidirectional, which cannot be modeled using an undirected graph. In this part, we write a program to store and query a weighted directed graph. In a weighted graph, we attribute a numeric value to each edge. Figure 2(a) shows a weighted directed graph. As shown in Figure 2(a), note that in a a directed graph, the inclusion of edge (A,C) does not imply the existence of edge (C,A). Figure 2(b) visualizes the adjacency list representation of the weighted directed graph in Figure 2(a).

In this part, you write a program that will read a weighted directed graph from a le. Store it in an adjacency list representation and then answer simple graph queries.

Input-Output format: Your program will take two le names as its command-line input. The rst le includes the weighted directed graph. Your program reads the contents of this le and constructs the graph data structure. The rst line in this le provides the number of vertices in the graph. Next, each line contains the name of each vertex in the graph. Afterwards, each following line includes information about a weighted directed edge in the graph. Each weighted edge is described by the name of its pair of vertices, followed by the edge weight, separated by a space. For example, B A 10 de nes a directed edge from vertex B to vertex A with an edge weight of 10.

The second le includes queries on the constructed graph. Each line contains a separate query that starts with the query type and a vertex, separated by a space. There are three query types. Out-degree queries start with the letter ’o’, followed by a space and the vertex’s name. Upon processing an out-degree query, your program must print out the queried vertex’s out-degree 1, followed by a newline character. In degree queries start with the letter ’i’, followed by a space and the vertex’s name. Upon processing an in degree query, your program must print out the queried vertex’s in degree 2, followed by a newline character. Adjacency queries start with the letter ’a’, followed by a space and the vertex’s name. Upon processing an adjacency query, your program must print out the vertices adjacent to the queried vertex, each vertex separated by a space and nally, a newline character. When you print the results of the adjacency query, the results have to be sorted lexicographically.


Example Execution:

Let’s assume we have the following graph and query    le:

graph.txt

5

A

B

C

D

E

BA10

A C 8

AD12

B D 5

C E 5


1The number of edges directed out of a vertex in a directed graph.
2The number of edges directed towards a vertex in a directed graph.


4

A       B



    C D



E


F


B

(a)

A       B




    C D



E


F


B    D    A

(b)

A       B




    C D



E


F


B    D    A    E    C

(c)

A       B




    C D



E


F


B    D    A    E    C    F

(d)


Figure 3: This gure illustrates an undirected graph and the BFS traversal steps of the graph starting from source vertex B.

D C 9

E C 7

E D 3

query.txt:

    • E a C i E a A a E

Then the result will be:

$./second graph.txt query.txt

2

E

1

    • D

    • D

We will not give you improperly formatted les. You can assume all your input les will be in proper format, as stated above.


Third: Breadth- rst Search (BFS) (15 points)

In this part, you will implement the bread- rst search (BFS) graph traversal algorithm. For a given input graph G=(V,E) and a source vertex s, BFS starts exploring the edges of G until it discovers all vertices reachable from the source vertex. During a BFS search, we start by visiting the adjacent

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vertices to the source vertex, processing them, and subsequently exploring vertices in order of edge distance (i.e., the smallest number of edges) from it. Figure 3 shows an example graph and its BFS traversal starting from source vertex B. Note that the vertices are processed in order of their distance from the source3.

You will write a program that will read an undirected graph from a le using your implementation from part 1 and perform BFS starting from di erent source vertices.

Input-Output format: Your program will take two le names as its command-line input. The rst le includes the undirected graph. This le is similar to the graph le from part 1. Your program reads the contents of this le and constructs the graph data structure. The rst line in this le provides the number of vertices in the graph. Next, each line contains the name of each vertex in the graph. Afterwards, each following line includes information about an edge in the graph. Each edge is described by the name of its pair of vertices, separated by a space.

The second le includes BFS queries on the constructed graph. Each line contains a di erent BFS query specifying a source vertex for the BFS. Your program must read the source vertex, perform a BFS traversal on the constructed graph using the chosen source vertex, and print out the graph vertices in order of BFS processing. Each vertex is separated by a space and nally, a newline character.


Example Execution:

Let’s assume we have the following graph and query    le:

graph.txt

6

A

B

C

D

E

F

    • B

    • C

    • D B D C D C E D E E F

query.txt:

B

E

Then the result will be:


    • For more information on BFS and example pseudocode, see https://en.wikipedia.org/wiki/Breadth-first_ search



6


A    B



C    D


E


A

(a)


A    B




C    D


E


A    C

(b)


A    B




C    D


E


A    C    E

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A    B




C    D


E


A    C    E    D

(d)


A    B




C    D


E


A    C    E    D    B

(e)


Figure 4: This gure illustrates a directed graph and its DFS traversal steps starting from vertex A.

$./third graph.txt query.txt

BADCEF

ECDFAB



We will not give you improperly formatted les. You can assume all your input les will be in proper format, as stated above.

You have traverse the immediate children of a node in a lexicographic order. For example, if A node has three edges to nodes B, C, and D, then the BFS will process B rst, then C, and nally D. This requirement ensures that every graph has a single unique BFS traversal for a given source vertex. You can easily satisfy this requirement by maintaining the adjacency list in a lexicographically sorted order.


Fourth: Depth- rst Search (DFS) (15 points)

In this part, you will implement the depth- rst search (DFS) graph traversal algorithm. For a given input graph G=(V,E), DFS visits an unvisited vertex v. At each step in DFS, we choose an unvisited vertex adjacent to the most recently discovered vertex. We continue this process until all vertices reachable from v are discovered. If any undiscovered vertices remain, we choose one of them and repeat the above process until all vertices are discovered. For example, Figure 4 illustrates a DFS traversal of the example graph in Figure 2(a) 4.

In this part, you write a program that will read a directed graph from a le using your implemen-tation from part 2 and perform a DFS traversal, printing out the graph vertices in order of DFS vertex visit. When you are choosing a vertex to visit next among the adjacent children, you have to pick the vertex that is not visited yet and occurs rst in the lexicographic order.

Input-Output format: Your program will take a le name as its command-line input. This le includes a directed graph, and it follows the same format from part 2. Your program reads the


    • For more information on DFS and example pseudocode, see https://en.wikipedia.org/wiki/Depth-first_ search



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contents of this le and constructs the graph data structure. The rst line in this le provides the number of vertices in the graph. Each following line includes information about a weighted directed edge in the graph. Each weighted edge is described by the name of its pair of vertices, followed by the edge weight, separated by a space. Your program must read and construct this graph, perform a DFS traversal, and print out the graph vertices in order of DFS visitation. Each vertex is separated by a space and, nally, a newline character.


Example Execution:

Let’s assume we have the following graph input    le:

graph.txt

5

A

B

C

D

E

BA10

A C 8

AD12

B D 5

C E 5

D C 9

E C 7

E D 3

Then the result will be:

$./fourth graph.txt

ACEDB

We will not give you improperly formatted les. You can assume all your input les will be in proper format, as stated above.

Hints and Suggestions

    • In a DFS traversal, each vertex is processed at most once. A DFS traversal will visit all graph vertices even when the graph is disconnected. Make sure your program works correctly in such cases.

    • You may have noticed that your program can safely ignore the graph edge weights in this part. However, this part’s solution is going to be used in part 5, which requires reading graph weights. Hence reusing part 2’s solution for weighted directed graphs is recommended in programming this part of the assignment.

    • When visiting graph vertices, visit them based on lexicographical ordering.




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Fifth: Single-source Shortest Path in a Directed Acyclic Graph (DAG) (20 points)

Given a weighted directed graph G=(V,E) and the source vertex s, the single-source shortest path problem’s goal is to identify to shortest path from the source vertex to all vertices in the graph. For example, nding the shortest path from our home to di erent adjacent cities can be modeled as a single-source shortest path problem with our home being the source vertex.

Depending on the input graphs type, a single-source shortest path problem can be solved using di erent algorithms that vary in asymptotic running time and complexity. For example, the BFS algorithm from part 3 is su cient to solve the single-source shortest path problem for unweighted graphs. However, we need other algorithms to solve the single-source shortest path problem in weighted graphs5.

In this part, your task is to write a program to solve the single-source shortest path problem for a type of directed called directed acyclic graphs (DAG). A DAG is a directed graph with no cycles6.

The single-source shortest problem in DAGs can be solved by visiting the DAG’s vertices in a topologically sorted order and updating the shortest path of the visited vertex’s adjacent vertices. You must use the DFS traversal from part 4 to topologically sort the DAG.

Algorithm 1 shows the steps to compute the single source path for the graph G and source vertex src. The algorithm maintains a distance array that is initially set to in nity for all vertices except the source vertex. distance[u] contains the shortest path from the source vertex to vertexu at the end of the algorithm or in nity if no path between src and u exists.

A topological sorting of the a DAG G(V,E) is an ordering of its vertices, T such that for every directed (u,v), vertex u appears before vertex v in its topological ordering. For example, Fig-ure 5(a) shows an example DAG and its topological sort. Figure 5(b-f) illustrates the steps taken by the DFS inspired algorithm7 to compute the topological ordering of the DAG by using a stack. Lastly, Figure 5(g) shows updates to the distance array by using Algorithm 1 on the DAG from Figure 5(a).

Input-Output format: Your program will take two le names as its command-line input. This rst le includes a DAG, and it follows the same format from parts 2 and 4. Your program reads the contents of this le and constructs the graph data structure. The rst line in this le provides the number of vertices in the DAG. Each following line includes information about a weighted directed edge in the DAG. Each weighted edge is described by the name of its pair of vertices, followed by the edge weight, separated by a space. Your program must read and construct this DAG,

The second le includes single source shortest path queries on the constructed DAG. Each line contains a di erent single-source shortest path query by specifying a source vertex. Your program must read the source vertex, perform the single source shortest path algorithm using the provided source vertex, and print out each of vertex in the DAG in lexicographic ordering, followed by the length of the shortest path to that vertex, and a newline character. Note that an additional newline character follows the last vertex in DAG. Further, your program must detect if the input graph is not a DAG. In such cases, your program simply prints out CYCLE, followed by a newline character.


    • For more information on graph shortest path problems, see https://en.wikipedia.org/wiki/Shortest_path_ problem

6For more information on DAGs, see https://en.wikipedia.org/wiki/Directed_acyclic_graph 7see https://en.wikipedia.org/wiki/Topological_sorting for pseudocode



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Distance from source


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Figure 5: This gure illustrates a directed graph and the steps involved in identifying its topological sort
















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Algorithm 1: Single-source shortest path algorithm for DAGs


    • procedure DAG-SSP(G; src)

    • T   T opologicalSort(G)

    • foreach vertex v in Graph G do

4
distance[v]
inf



    • end

6
distance[src]
0

    • foreach vertex u in topologically sorted order T do

8foreach vertex v 2 u:Adjacent do

    • if distance[v] > distance[u] + weight(u; v) then

    10 distance[v]   distance[u] + weight(u; v)

    11 end

    12 end



Example Execution:

Let’s assume we have the following graph input    le:

graph.txt

7

A

B

C

D

E

F

G

AD10

A C 5

B D 7

C D 3

D E 5

E F 1

CF10

EG10

F G 5

query.txt

A

G

Then the result will be:

$./fifth graph.txt query.txt

    • 0

B INF C 5

11

    • 8

E 13 F 14 G 19

    • INF B INF C INF D INF E INF F INF G 0

For the scenario when the input graph is not a DAG:

not_dag.txt

2

CA

NJ

NJ CA 3000

CA NJ 3100

query.txt

CA

Then the result will be:

$./fifth not_dag.txt query.txt

CYCLE

We will not give you improperly formatted les. You can assume all your input les will be in proper format, as stated above.

Hints and Suggestions

    • The shortest path from the source vertex to itself is 0.

    • The shortest path from the source vertex to any unreachable vertex is in nity. In the output, we represent in nity with INF, as shown in the example execution.

    • You can use MAX INT from <limits.h> to represent in nity in your program. The input DAGs will not contain edges with weights larger than MAX INT. Further, you may safely assume that the shortest paths do not over ow.

    • Edge weights can be negative numbers.



Sixth: Dijkstra’s Shortest Path Algorithm (20 points)

In this part, we implement Dijkstra’s Algorithm. This algorithm solves the single-source shortest path problem for graphs with nonnegative edge weights. The key idea behind Dijkstra’s algorithm

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is to maintain a set of vertices whose nal shortest path from the source vertex has been determined. This set starts empty, and at each iteration of the algorithm, we add the next vertex by extracting it from a min-priority queue of vertices8.

Input-Output format: Your program will take two le names as its command-line input. This rst le includes a weighted directed graph, and it follows the same format from parts 2 and 4. Your program reads the contents of this le and constructs the graph data structure. The rst line in this le provides the number of vertices in the graph. Each following line includes information about a weighted directed edge in the graph. Each weighted edge is described by the name of its pair of vertices, followed by the edge weight, separated by a space. Your program must read and construct this graph,

The second le includes single source shortest path queries on the constructed each. Each line contains a di erent single-source shortest path query by specifying a source vertex. Your program must read the source vertex, perform Dijkstra’s single-source shortest path algorithm using the provided source vertex, and print out each of vertex in the graph in lexicographical ordering, followed by the length of the shortest path to that vertex, and a newline character. Note that an additional newline character follows the last vertex in DAG.


Example Execution:

Let’s assume we have the following graph input    le:

graph.txt

5

A

B

C

D

E

BA10

A C 8

AD12

B D 5

C E 5

D C 9

E C 7

E D 3

query.txt:

A

E

Then the result will be:

$./sixth graph.txt query.txt

A 0


    • See Dijkstra’s shortest path algorithm at https://en.wikipedia.org/wiki/Dijkstra%27s algorithm



13

    • INF C 8 D 12 E 13

    • INF B INF C 7 D 3 E 0

We will not give you improperly formatted les. You can assume all your input les will be in proper format, as stated above. Further, all input graphs edges are nonnegative for this part.

Hints and Suggestions

    • Depending on the priority queue’s implementation, the asymptotic running time of Dijkstra’s algorithm varies. For the this programming assignment, using a simple array-based priority queue is acceptable.


Seventh: Conversion to Two’s Complement Representation (10 points)



In this part, your task is to write a C program that prints the two’s complement binary represen-tation of a number with a speci c number of bits. The argument to the program is an input le, whose format is described in the input format. If a given number is not representable with a given number of bits because the number is greater than largest positive value possible with the given number of bits, then you should print the representation for the largest positive value with the given number of bits in the two’s complement representation. If a given number is not representable with a given number of bits because is smaller than smallest negative value with the given number of bits, then you should print the representation for the smallest negative with the given number of bits in the two’s complement representation.

Input-Output format: Your program will take one le name as its command-line input. Each line in the input le will have two integers separated by a space: an integer that you want to represent in binary and the number of bits to use for the representation. For each line in the input, you should print out the binary representation of the number followed by a newline character.

Example Execution:

Let’s assume we have the following input    le:

input.txt

    42 7

    16 4 -9 4

When you execute the program, the result should be:


14

$./seventh input.text

0101010

0111

1000

We will not give you improperly formatted les. You can assume all your input les will be in proper format, as stated above.


Eighth: Decimal Fraction Input to a Canonical Binary Fraction (15 points)



You will write a program to convert a decimal fraction to a binary fraction in the canonical rep-resentation (i.e., ( 1)s M 2E ). For this program, M lies between [1; 2). You do not have to perform any rounding for this part. You are required to print as many digits after the decimal point as speci ed by the input.

Input-Output format: Your program will take one le name as its command-line input. Each line in the input le will have a decimal fraction (use a double type to read it) and the number of bits to show in the canonical binary representation separate by space. For each line in the input, you should print the M value and E value in the canonical representation separated by space. Add a newline character after printing the output for each input.

Example Execution:

Let’s assume we have the following input    le:

input.txt

6.25 6

12.5 3

The result should be:

$./eighth input.text

1.100100 2

1.100 3

We will not give you improperly formatted les. You can assume all your input les will be in proper format, as stated above. Further, we will provide only positive fractions for this part of the assignment (i.e., no negative numbers).


Ninth: Decimal to IEEE-754 FP with Rounding (20 points)

Your task is to write a program to convert a decimal fraction to the IEEE-754 FP representation in a given con guration with the rounding to nearest with ties-to-even rounding mode.

Input-Output format: Your program will take one le name as its command-line input. Each line in the input le will have a decimal fraction (use a double type to read it), the number of


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the bits (n) in the IEEE-754 FP representation, number of bits for the exponent, and number of fraction bits. These numbers on a given line are separated by a space. For each line in the input, you should the IEEE-754 representation with n-bits followed by a new line.

Example Execution:

Let’s assume we have the following input    le:

6.5843

.0546875 8 4 3

.013671875 8 4 3

6.375 8 4 3

8.5843

9.5843

Then the result will be:

$./ninth input.text

01001101

00010110

00000111

01001101

01010000

01010010

We will not give you improperly formatted les. You can assume all your input les will be in proper format, as stated above. You can assume that input will not have NaNs and any value will not round up or down to in nities.


Tenth: Hexadecimal Bit-pattern in the IEEE-FP Format to Deci-mal Fraction (15 points)

Your task is to write a program that takes a hexadecimal bit-pattern and prints the decimal fractional value of the number.

Input-Output format: Your program will take one le name as its command-line input. Each line in the input le will have the total number of bits, the number of bits for the exponent, number of bits for the fraction, the hexadecimal bit-pattern, and the number of precision bits after the decimal point in the decimal fraction. These numbers on a given line are separated by a space. For each line in the input, you should print out the decimal fraction value with the speci ed number of precision bits followed by a new line.

Example Execution:

Let’s assume we have the following input    le:

input.txt

8 4 3 0x4d 2

8 4 3 0x16 7

16

Then the output should be:

$./tenth input.text

6.50

.0546875


Structure of your submission folder

All les must be included in the pa2 folder. The pa2 directory in your tar le must contain 10 subdirectories, one each for each of the parts. The name of the directories should be named rst through tenth (in lower case). Each directory should contain a c source le, a header le (if you use it) and a Make le. For example, the subdirectory rst will contain, rst.c, and any additional .c or .h (if you create one) and Make le (the names are case sensitive).

pa2

|- first

|-- first.c

|-- <additional .c .h files> (if used)

|-- Makefile

|- second

|-- second.c

|-- <additional .c .h files> (if used)

|-- Makefile

|- third

|-- third.c

|-- <additional .c .h files> (if used)

|-- Makefile

|- fourth

|-- fourth.c

|-- <additional .c .h files> (if used)

|-- Makefile

|- fifth

|-- fifth.c

|-- <additional .c .h files> (if used)

|-- Makefile

|- sixth

|-- sixth.c

|-- <additional .c .h files> (if used)

|-- Makefile

|- seventh

|-- seventh.c

|-- <additional .c .h files> (if used)

|-- Makefile

|- eighth

|-- eighth.c

|-- <additional .c .h files> (if used)

|-- Makefile

|- ninth

|-- ninth.c

|-- <additional .c .h files> (if used)

|-- Makefile

|- tenth

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|-- tenth.c

|-- <additional .c .h files> (if used)

|-- Makefile



Submission

You have to e-submit the assignment using Canvas. Your submission should be a tar le named pa2.tar. To create this le, put everything that you are submitting into a directory (folder) named pa2. Then, cd into the directory containing pa2 (that is, pa2’s parent directory) and run the following command:

tar cvf pa2.tar pa2

To check that you have correctly created the tar le, you should copy it (pa2.tar) into an empty directory and run the following command:

tar xvf pa2.tar

This should create a directory named pa2 in the (previously) empty directory.

The pa2 directory in your tar le must contain 10 subdirectories, one each for each of the parts. The name of the directories should be named rst through ninth (in lower case). Each directory should contain a c source le, a header le and a make le. For example, the subdirectory rst will contain, rst.c, rst.h and Make le (the names are case sensitive).


AutoGrader

We provide the auto grader (pa2 autograder.tar.gz) to test your assignment. Executing the follow-ing command will extract the pa2 autograder.


tar -zxvf pa2_autograder.tar.gz

There are two modes available for testing your assignment with the autograder


First mode

Testing when you are writing code with a pa2 folder

    (1) Lets say you have a pa2 folder with the directory structure as described in the assignment.

    (2) Copy the folder to the directory of the pa2 autograder folder

    (3) Run the auto grader with the following command


python pa2_auto_grader.py

It will run your programs and print your scores.

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Second mode

This mode is to test your    nal submission (i.e, pa2.tar)

    (1) Copy pa2.tar to the pa2 autograder folder

    (2) Run the auto grader with pa2.tar as the argument. The command line is


python pa2_auto_grader.py pa2.tar


Scoring

The autograder will print out information about the compilation and the testing process. At the end, if your assignment is completely correct, the score will something similar to what is given below.

You scored

7.5    in    tenth

5.0    in    second

7.5    in    fourth

7.5    in    third

10.0    in    sixth

10.0    in    ninth

5.0    in    seventh

7.5    in    eighth

10.0    in    fifth

5.0    in    first

Your TOTAL SCORE =    75.0 /75

Your assignment will be graded for another 75 points with test cases not given to you


Grading Guidelines

This is a large class so that necessarily the most signi cant part of your grade will be based on programmatic checking of your program. That is, we will build the binary using the Make le and source code that you submitted, and then test the binary for correct functionality against a set of inputs. Thus:

    • You should not see or use your friend’s code either partially or fully. We will run state of the art plagiarism detectors. We will report everything caught by the tool to O ce of Student Conduct.

    • You should make sure that we can build your program by just running make.

    • Your compilation command with gcc should include the following ags: -Wall -Werror -fsanitize=address -std=c11

    • You should test your code as thoroughly as you can. For example, programs should not crash with memory errors.

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    • Your program should produce the output following the example format shown in previous sections. Any variation in the output format can result in up to 100% penalty. Be especially careful to not add extra whitespace or newlines. That means you will probably not get any credit if you forgot to comment out some debugging message.

    • Your folder names in the path should have not have any spaces. Autograder will not work if any of the folder names have spaces.

Be careful to follow all instructions. If something doesn’t seem right, ask on Canvas discussion forums or contact the TAs during o ce hours.





















































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