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AKTU 2nd Year (Sem 3) · Data Structure Lab (C/C++)

Data Structure Lab Interactive Suite & PDF Studio

Comprehensive AKTU syllabus repository featuring 23 complete C/C++ data structure experiments, full copyable source codes, viva-voce questions, and an automated generator to export your submission-ready 40-page lab manual PDF with custom Roll Number & Name footers in seconds.

AKTU BCS-351 Data Structure Lab Syllabus

23 Experiments

Sorting to Dijkstra

40 Pages Generated

Complete C/C++ Code

Automated PDF

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BCS-351 / KCS-351

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Course Code & Subject: BCS-351 / KCS-351 · Data Structures Lab
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Full 40-Page Manual: Covers all 23 official syllabus experiments with complete C/C++ source programs & outputs.
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Experiment 1: To Implement Bubble Sort.

AKTU B.Tech Practical Manual · Subject Code: BCS-351
#include <stdio.h>

int main() {
    int n, j, i, swap;
    printf("Enter number of elements\n");
    scanf("%d", &n);
    int array[n];
    /* Bubble Sort Algorithm */
    for (i = 0; i < n - 1; i++) {
        for (j = 0; j < n - i - 1; j++) {
            if (array[j] > array[j + 1]) {
                swap = array[j];
                array[j] = array[j + 1];
                array[j + 1] = swap;
            }
        }
    }
    return 0;
}

Crucial Architecture & Compilation Tips for AKTU Practical Exams

All programs in this suite are fully ANSI C99 / C++ compliant. While modern college laboratories utilize GCC / Clang on Linux or VS Code (e.g. gcc exp1.c -o exp1 && ./exp1), older lab systems may require Turbo C++. If compiling on Turbo C++, include #include <conio.h> and add clrscr(); and getch(); appropriately. In practical viva exams, examiners heavily focus on time complexity notations O(n log n) and internal pointer memory manipulation in linked lists.

Explore All 23 Laboratory Experiments

Browse full tested source code for each experiment, review time complexities, or copy source code directly to your clipboard.

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Top 10 AKTU DS Lab Viva Questions

Essential theoretical questions frequently asked by university external examiners during the Data Structure practical evaluation.

Q1 Why is Quick Sort generally preferred over Merge Sort for sorting arrays?
Quick Sort is an in-place sorting algorithm requiring only O(log n) auxiliary stack space, whereas Merge Sort requires O(n) additional memory to store merged subarrays. Furthermore, Quick Sort exhibits superior CPU cache locality during array partitioning, making its constant factors substantially lower on modern hardware.
Q2 What is the mathematical condition for Queue Overflow in a Circular Queue?
In an array of size MAX, a Circular Queue is full when (rear + 1) % MAX == front, or alternatively when front == 0 && rear == MAX - 1 or front == rear + 1. This ensures that the circular boundary condition is accurately handled without wasting the initial vacant positions.
Q3 What is the output of an Inorder Traversal of a Binary Search Tree (BST)?
An Inorder Traversal (Left Subtree → Root Node → Right Subtree) of a valid Binary Search Tree always visits elements in strictly ascending (sorted) order. This is because by definition, all nodes in the left subtree are smaller than the root, and all nodes in the right subtree are greater.
Q4 Why are Postfix and Prefix notations preferred by compilers over Infix notation?
Infix expressions require operator precedence rules, associativity parsing, and parenthesis balancing. In contrast, Postfix (Reverse Polish) and Prefix expressions are entirely parenthesis-free and unambiguous. A compiler or stack-based CPU can evaluate a postfix expression in a single linear pass in O(n) time using a simple operand stack.
Q5 Can Dijkstra’s Algorithm handle graphs with negative edge weights?
No. Dijkstra's algorithm relies on a greedy choice property: once a vertex is marked visited (relaxed), its shortest distance from the source is finalized. A negative edge encountered later could invalidate this assumption and lead to incorrect shortest path distances. For graphs with negative edge weights, the Bellman-Ford Algorithm must be used.
Q6 What is the difference between Linear Search and Binary Search?
Linear Search operates on both sorted and unsorted lists with a time complexity of O(n) by checking elements sequentially. Binary Search requires the input array to be strictly sorted beforehand, but achieves a logarithmic time complexity of O(log n) by halving the search space at each comparison.
Q7 What is the difference between Breadth-First Search (BFS) and Depth-First Search (DFS)?
BFS explores neighbor nodes level by level using a Queue (FIFO) data structure, making it ideal for finding unweighted shortest paths. DFS explores each branch as deep as possible before backtracking, utilizing a Stack (LIFO) or recursive call stack. Both have time complexity O(V + E).
Q8 What is a Minimum Spanning Tree (MST) and how do Prim’s and Kruskal’s algorithms differ?
An MST is an acyclic subgraph connecting all vertices of an edge-weighted graph with minimum total edge cost. Prim’s algorithm grows a single tree by adding the cheapest incident edge connecting an unvisited vertex (better for dense graphs). Kruskal’s algorithm sorts all edges and greedily adds the smallest non-cycle creating edge using Disjoint Set Union (better for sparse graphs).
Q9 Explain the difference between Array and Linked List in terms of memory.
Arrays use contiguous memory allocation, which allows O(1) random index access and high cache locality, but have a fixed size and require O(n) element shifting for insertions/deletions. Linked Lists allocate disjoint nodes dynamically on the heap via pointers, allowing O(1) insertion/deletion at known locations without resizing, but incur pointer memory overhead and O(n) sequential access.
Q10 What is a Max-Heap and how is it used in Heap Sort?
A Max-Heap is a complete binary tree where the key at every root node is greater than or equal to keys in its children. Heap Sort first builds a max-heap in O(n) time, then repeatedly swaps the maximum root element with the last element of the heap, reduces heap size, and calls heapify() in O(log n). This achieves guaranteed O(n log n) sorting in-place.