# WIX1002 Fundamentals Of Programming Assignment Sample, UM, Malaysia

WIX1002 Fundamentals of Programming at Universiti Malaya (UM) introduces students to essential programming concepts and techniques. Through hands-on practice, students learn fundamental programming constructs, algorithm design, and problem-solving skills. The WIX1002 course covers key programming paradigms and languages, fostering proficiency in coding and logic.

Emphasizing real-world applications, it equips students with the foundational knowledge necessary for further studies in computer science and related fields. WIX1002 provides a solid grounding in programming principles, preparing students for future challenges in software development and computational thinking.

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### Assignment Task 1:Analyze the time complexity of two sorting algorithms (e.g., bubble sort and merge sort)

#### Bubble Sort:

Bubble Sort is a simple sorting algorithm that repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. The pass through the list is repeated until the list is sorted.

#### Time Complexity:

In the worst-case scenario, when the array is sorted in reverse order, Bubble Sort will require swapping at each comparison. For an array of size n, Bubble Sort performs

(n−1)+(n−2)+…+1 comparisons, which is a sum of the first n−1 positive integers.

This sum can be expressed as 2/ n(n−1) , which gives us a time complexity of O(n2). This is because for each element in the array, Bubble Sort may need to traverse the entire list to ensure that element is in its correct position.

#### Merge Sort:

Merge Sort is a divide and conquer algorithm that divides the input array into two halves, recursively sorts the two halves, and then merges the sorted halves.

#### Time Complexity:

• Merge Sort divides the array into halves recursively until each sub-array contains only one element, which is inherently
• O(logn). After that, it merges the sub-arrays, which takes linear time proportional to the size of the merged sub-arrays. Therefore, the time complexity of Merge Sort is
• O(nlogn). This is because at each level of recursion, the entire array of size
• n is divided into two sub-arrays until they contain only one element each, and the merging step takes
• O(n) time in each level.

#### In summary:

• Bubble Sort has a time complexity of
• O(n2), making it inefficient for large arrays.
• Merge Sort has a time complexity of
• O(nlogn), making it more efficient, especially for large datasets, due to its divide and conquer strategy.

#### Arrays:

• Random Access: Arrays provide constant-time access to elements based on their indices, making it easy to access elements at both ends of the stack (top and bottom).
• Memory Efficiency: Arrays typically use contiguous memory blocks, leading to better cache locality and reduced memory overhead compared to linked lists.
• Simplicity: Implementing a stack with arrays is often simpler and requires less overhead in terms of memory management compared to linked lists.

• Fixed Size: Arrays have a fixed size, which means they may run out of space if the maximum size is exceeded. Resizing arrays can be costly as it involves copying elements to a new array.
• Insertion and Deletion Overhead: Inserting or deleting elements in the middle of an array (not just at the top) requires shifting subsequent elements, resulting in a higher time complexity compared to linked lists.
• Dynamic Memory Allocation: If the array size needs to be dynamically adjusted, it may lead to overhead due to dynamic memory allocation and deallocation.

• Dynamic Size: Linked lists can dynamically grow or shrink as needed, allowing for efficient memory usage and avoiding the fixed-size limitation of arrays.
• Efficient Insertion and Deletion: Inserting or deleting elements in a linked list, even in the middle, can be done with constant time complexity, as it only involves changing pointers.
• No Memory Fragmentation: Linked lists avoid memory fragmentation issues that may arise with arrays, especially when resizing.

• Sequential Access: Unlike arrays, linked lists do not provide direct access to elements based on their indices. Accessing elements at the middle or end of the list requires traversing through the list, resulting in linear time complexity.
• Cache Inefficiency: Linked lists may not exhibit good cache locality due to scattered memory allocations, which can result in slower performance compared to arrays, especially for large lists.

### Assignment Task 3: Discuss best practices for error handling and resource management when reading from and writing to files in a programming language of your choice.

Best practices for error handling and resource management when reading from and writing to files:

#### Error Handling:

• Use Exception Handling: Utilize language-specific exception handling mechanisms to catch and handle file-related errors gracefully.
• Check Return Values: Always check the return values of file-related functions (e.g., fopen, fread, fwrite) for errors and handle them appropriately.
• Provide Descriptive Error Messages: When encountering errors, provide informative error messages that help users understand the nature of the problem.
• Cleanup Resources: Ensure that resources such as file handles are properly closed in both success and failure scenarios to prevent resource leaks.

#### Resource Management:

• RAII (Resource Acquisition Is Initialization): Utilize RAII or similar techniques to ensure that resources (e.g., file handles) are automatically managed and released when they go out of scope.
• Close Files Properly: Always close files explicitly after they are no longer needed to free up system resources and prevent potential resource contention.
• Use Scoped Guards: Implement scoped guards or wrappers to manage file operations within limited scopes, ensuring that resources are released even in the presence of exceptions.
• Avoid Unclosed Handles: Make sure that every file opened for reading or writing is closed properly, even in error scenarios, to avoid resource leaks and potential file corruption.

By following these best practices, developers can ensure robust error handling and efficient resource management when dealing with file I/O operations in their programs.

### Assignment Task 4: Analyze the efficiency of nested conditional statements versus using switch-case statements for implementing a menu-driven program.

Efficiency of nested conditional statements versus using switch-case statements for implementing a menu-driven program:

#### Nested Conditional Statements:

• Simplicity: Nested conditional statements (if-else) are straightforward to implement and understand, especially for programmers familiar with the concept.
• Flexibility: Nested conditionals can handle complex logic by nesting multiple conditions within each other, allowing for greater flexibility in decision-making.

• Readability: As the number of conditions increases, nested conditionals can become difficult to read and maintain, leading to potential code complexity and confusion.
• Performance: With a large number of conditions, nested conditionals may result in slower performance due to the need to evaluate each condition sequentially.

#### Switch-Case Statements:

• Readability: Switch-case statements provide a more structured and readable way to implement menu-driven programs, especially when dealing with multiple options.
• Efficiency: Switch-case statements can offer better performance compared to nested conditionals, especially when the number of options is large, as they typically involve a direct jump to the appropriate case.

• Limited Expressiveness: Switch-case statements are less flexible than nested conditionals and cannot handle complex conditions or expressions within cases.
• Requirement of Constants: Switch-case statements require constants as case labels, limiting their usage in scenarios where dynamic values are involved.

Efficiency Analysis:

• For a small number of options, the difference in efficiency between nested conditionals and switch-case statements may not be significant.
• However, as the number of options increases, switch-case statements tend to offer better performance due to their direct jump mechanism, avoiding the need to evaluate each condition sequentially.

#### Conclusion:

• In general, for implementing a menu-driven program with a limited number of options, both nested conditionals and switch-case statements can be suitable.
• For programs with a large number of options, or where performance is a critical factor, switch-case statements may be preferred due to their better efficiency and readability.

### Assignment Task 5: Discuss scenarios where recursion might be more suitable and situations where iterative approaches are preferred.

Scenarios where recursion might be more suitable and situations where iterative approaches are preferred:

#### Recursion:

Suitable Scenarios:

• Tree-based Problems: Recursion is well-suited for problems involving tree-like structures, such as traversing binary trees, calculating factorial, or generating permutations.
• Divide and Conquer: Problems that can be divided into smaller sub-problems, where the solution to the larger problem depends on solutions to the smaller sub-problems, are often efficiently solved using recursion.
• Backtracking: Recursion is commonly used in backtracking algorithms, such as solving puzzles like Sudoku or finding all possible solutions to a problem.

• Simplicity: Recursive solutions often offer concise and elegant implementations, making them easier to understand and maintain.
• Readability: Recursion can mirror the problem’s natural recursive structure, leading to more readable and intuitive code.

#### Iterative:

Suitable Scenarios:

• Efficiency: In scenarios where performance is critical or memory usage needs to be optimized, iterative approaches are often preferred over recursion due to the overhead associated with function calls and stack management in recursion.
• Iterative Algorithms: Some algorithms naturally lend themselves to iterative implementations, such as iterative depth-first search, iterative binary search, or algorithms that require explicit control flow.

• Performance: Iterative solutions typically offer better performance and consume less memory compared to recursive solutions, especially for large inputs, as they avoid the overhead of function calls and stack management.
• Tail Recursion Optimization: Not all programming languages optimize tail recursion, leading to potential stack overflow issues with deeply recursive functions, which can be mitigated with iterative approaches.

#### Conclusion:

• Recursion is suitable for problems with a natural recursive structure or when simplicity and readability are prioritized.
• Iterative approaches are preferred for scenarios where performance, memory usage, or language constraints are critical factors, or when the problem can be efficiently solved iteratively without sacrificing readability.

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