Linear search examines each element of a list sequentially, one by one, and checks if it is the target value. It has a time complexity of O(n) as it requires searching through each element in the worst case. While simple to implement, linear search is inefficient for large lists as other algorithms like binary search require fewer comparisons.
Linked list using Dynamic Memory Allocationkiran Patel
A linked list is a linear data structure where each node contains a link to the next node. There are several types of linked lists including singly-linked, doubly-linked, circular, and circular doubly-linked lists. Linked lists allow for efficient insertion and removal of nodes and can grow and shrink dynamically. Common operations on linked lists include creation, insertion, deletion, traversal, searching, and concatenation.
The document provides an overview of data structures and algorithms. It discusses key topics like:
1) Different types of data structures including primitive, linear, non-linear, and arrays.
2) The importance of algorithms and how to write them using steps, comments, variables, and control structures.
3) Common operations on data structures like insertion, deletion, searching, and sorting.
4) Different looping and selection statements that can be used in algorithms like for, while, and if-then-else.
5) How arrays can be used as a structure to store multiple values in a contiguous block of memory.
The document provides information about data structures and algorithms. It defines key terms like data, information, data structure, algorithm and different types of algorithms. It discusses linear data structures like arrays, operations on arrays like traversing, searching, inserting and deleting elements. It also covers recursive functions, recursion concept, searching algorithms like sequential search and binary search along with their algorithms and examples.
Linear search examines each element of a list sequentially, one by one, and checks if it is the target value. It has a time complexity of O(n) as it requires searching through each element in the worst case. While simple to implement, linear search is inefficient for large lists as other algorithms like binary search require fewer comparisons.
Linked list using Dynamic Memory Allocationkiran Patel
A linked list is a linear data structure where each node contains a link to the next node. There are several types of linked lists including singly-linked, doubly-linked, circular, and circular doubly-linked lists. Linked lists allow for efficient insertion and removal of nodes and can grow and shrink dynamically. Common operations on linked lists include creation, insertion, deletion, traversal, searching, and concatenation.
The document provides an overview of data structures and algorithms. It discusses key topics like:
1) Different types of data structures including primitive, linear, non-linear, and arrays.
2) The importance of algorithms and how to write them using steps, comments, variables, and control structures.
3) Common operations on data structures like insertion, deletion, searching, and sorting.
4) Different looping and selection statements that can be used in algorithms like for, while, and if-then-else.
5) How arrays can be used as a structure to store multiple values in a contiguous block of memory.
The document provides information about data structures and algorithms. It defines key terms like data, information, data structure, algorithm and different types of algorithms. It discusses linear data structures like arrays, operations on arrays like traversing, searching, inserting and deleting elements. It also covers recursive functions, recursion concept, searching algorithms like sequential search and binary search along with their algorithms and examples.
This document provides an introduction to data structures and algorithms. It defines data as quantities, characters, or symbols operated on by a computer. Data structures are described as organized ways to store and access data efficiently. Common data structures include arrays, linked lists, trees, stacks, and queues. Algorithms are sets of instructions to solve problems, taking input and producing output. Good algorithms are correct, unambiguous, and efficient. Examples demonstrate data structures like arrays and graphs, as well as a simple maximum-finding algorithm. The conclusion emphasizes the importance of data structures.
1. Data structures organize data in memory for efficient access and processing. They represent relationships between data values through placement and linking of the values.
2. Algorithms are finite sets of instructions that take inputs, produce outputs, and terminate after a finite number of unambiguous steps. Common data structures and algorithms are analyzed based on their time and space complexity.
3. Data structures can be linear, with sequential elements, or non-linear, with branching elements. Abstract data types define operations on values independently of implementation through inheritance and polymorphism.
This document introduces key concepts in data structures and algorithms. It defines an algorithm as a set of instructions to accomplish a task, and discusses how algorithms are evaluated based on their time and space complexity. It then defines data structures as organized data with relationships and permitted operations, and lists common examples like arrays, stacks and queues. The document also introduces abstract data types as mathematical models with defined operations.
This document provides an introduction to data structures and algorithms. It discusses key concepts like abstract data types (ADTs), different types of data structures including linear and non-linear structures, analyzing algorithms to assess efficiency, and selecting appropriate data structures based on required operations and resource constraints. The document also covers topics like classifying data structures, properties of algorithms, analyzing time and space complexity, and examples of iterative and recursive algorithms and their complexity analysis.
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
The document discusses data structures and algorithms. It defines arrays as a series of objects of the same size and type, where each object is an element that can be accessed via an index. Algorithms are described as finite sequences of instructions to solve problems, with analysis of algorithms determining the resources like time and storage required.
This document provides an overview of a course on data structures and algorithm analysis. It introduces some key data structures like arrays, linked lists, stacks, and queues. It also discusses algorithm strategies such as brute force, greedy, and divide-and-conquer algorithms. The course contents are described, including reviewing programming concepts, fundamental data structures and algorithms, recursion, and more. Assessment includes assignments, quizzes, class tests, and a final exam. Common operations on data structures like traversal, insertion, deletion, and searching are explained.
This document provides an overview of data structures and algorithms. It introduces common linear data structures like stacks, queues, and linked lists. It discusses the need for abstract data types and different data types. It also covers implementing stacks as a linked list and common stack operations. Key applications of stacks include function call stacks which use a LIFO structure to remember the order of function calls and returns.
- The document discusses one-dimensional and two-dimensional arrays in C. It defines arrays, provides examples of declaring and initializing arrays, and describes common array operations like insertion, deletion, modification, and traversal. It also discusses two-dimensional arrays and provides an example program to add two matrices using a 2D array. The key topics covered are the basic concepts of arrays, declaring and initializing single and multi-dimensional arrays, and common operations performed on arrays.
This document discusses various applications of common data structures like linked lists, stacks, queues, and trees. It provides examples of how linked lists are used to implement queues and stacks, and in web browsers to store browsing history. It also gives examples of how stacks can be used for reversing words, undo/redo functions, matching parentheses in compilers, and modeling real-world examples like plates in a cupboard. Applications of queues include asynchronous data transfer and resource sharing. Trees are used in operating systems to represent folder structures, in HTML for the document object model, for network routing, syntax trees in compilers, and modeling game moves in AI.
Introduction to datastructure and algorithmPratik Mota
Introduction to data structure and algorithm
-Basics of Data Structure and Algorithm
-Practical Examples of where Data Structure Algorithms is used
-Asymptotic Notations [ O(n), o(n), θ(n), Ω(n), ω(n) ]
-Calculation of Time and Space Complexity
-GNU gprof basic
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
An algorithm is a finite set of instructions to accomplish a predefined task. Performance of an algorithm is measured by its time and space complexity, with common metrics being big O, big Omega, and big Theta notation. Common data structures include arrays, linked lists, stacks, queues, trees and graphs. Key concepts are asymptotic analysis of algorithms, recursion, and analyzing complexity classes like constant, linear, quadratic and logarithmic time.
1. The Python if statement is used to execute code conditionally based on the evaluation of an expression. It can be used with simple conditions, if-else statements, and nested if statements.
2. Python if-else statements allow executing one block of code if a condition is true and another block if the condition is false.
3. Nested if statements in Python allow checking multiple conditions, with inner if statements executing only if the outer conditions are met.
This document provides an overview of data structures and algorithms analysis. It discusses big-O notation and how it is used to analyze computational complexity and asymptotic complexity of algorithms. Various growth functions like O(n), O(n^2), O(log n) are explained. Experimental and theoretical analysis methods are described and limitations of experimental analysis are highlighted. Key aspects like analyzing loop executions and nested loops are covered. The document also provides examples of analyzing algorithms and comparing their efficiency using big-O notation.
This document provides an overview of data structures and algorithms. It discusses key concepts like interfaces, implementations, time complexity, space complexity, asymptotic analysis, and common control structures. Some key points:
- A data structure organizes data to allow for efficient operations. It has an interface defining operations and an implementation defining internal representation.
- Algorithm analysis considers best, average, and worst case time complexities using asymptotic notations like Big O. Space complexity also measures memory usage.
- Common control structures include sequential, conditional (if/else), and repetitive (loops) structures that control program flow based on conditions.
The document discusses data structures and algorithms. It defines a data structure as a particular way of organizing data in a computer so that it can be used efficiently. Common data structures include arrays, stacks, queues, linked lists, trees, heaps, and hash tables. An algorithm is defined as a finite set of instructions to accomplish a particular task. Analyzing algorithms involves determining how resources like time and storage change with input size. Key considerations in algorithm design include requirements, analysis, data objects, operations, refinement, coding, verification, and testing.
The document discusses strings, arrays, pointers, and sorting algorithms in C programming. It provides definitions and examples of:
1) Strings as null-terminated character arrays. It demonstrates initializing and printing a string.
2) One-dimensional and two-dimensional arrays. It shows how to declare, initialize, access, and print multi-dimensional arrays.
3) Pointers as variables that store memory addresses. It explains pointer declaration and dereferencing pointers using asterisk (*) operator.
4) Bubble sort algorithm that iterates through an array and swaps adjacent elements if out of order, putting largest elements at the end of the array in each iteration.
- A linked list is a data structure where elements are linked using pointers. Each element points to the next element.
- Linked lists allow dynamic memory allocation as elements can be added or removed without relocating the entire structure. However, accessing a specific element takes linear time.
- Arrays provide constant time access to elements but require fixed block allocation. Adding or removing elements may require allocating new memory and copying the entire array.
Describe about the heap memory management such as memory allocation & deallocation. Explained the Memory manager functionality and fragmentation issues.
This document provides an introduction to data structures and algorithms. It defines data as quantities, characters, or symbols operated on by a computer. Data structures are described as organized ways to store and access data efficiently. Common data structures include arrays, linked lists, trees, stacks, and queues. Algorithms are sets of instructions to solve problems, taking input and producing output. Good algorithms are correct, unambiguous, and efficient. Examples demonstrate data structures like arrays and graphs, as well as a simple maximum-finding algorithm. The conclusion emphasizes the importance of data structures.
1. Data structures organize data in memory for efficient access and processing. They represent relationships between data values through placement and linking of the values.
2. Algorithms are finite sets of instructions that take inputs, produce outputs, and terminate after a finite number of unambiguous steps. Common data structures and algorithms are analyzed based on their time and space complexity.
3. Data structures can be linear, with sequential elements, or non-linear, with branching elements. Abstract data types define operations on values independently of implementation through inheritance and polymorphism.
This document introduces key concepts in data structures and algorithms. It defines an algorithm as a set of instructions to accomplish a task, and discusses how algorithms are evaluated based on their time and space complexity. It then defines data structures as organized data with relationships and permitted operations, and lists common examples like arrays, stacks and queues. The document also introduces abstract data types as mathematical models with defined operations.
This document provides an introduction to data structures and algorithms. It discusses key concepts like abstract data types (ADTs), different types of data structures including linear and non-linear structures, analyzing algorithms to assess efficiency, and selecting appropriate data structures based on required operations and resource constraints. The document also covers topics like classifying data structures, properties of algorithms, analyzing time and space complexity, and examples of iterative and recursive algorithms and their complexity analysis.
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
The document discusses data structures and algorithms. It defines arrays as a series of objects of the same size and type, where each object is an element that can be accessed via an index. Algorithms are described as finite sequences of instructions to solve problems, with analysis of algorithms determining the resources like time and storage required.
This document provides an overview of a course on data structures and algorithm analysis. It introduces some key data structures like arrays, linked lists, stacks, and queues. It also discusses algorithm strategies such as brute force, greedy, and divide-and-conquer algorithms. The course contents are described, including reviewing programming concepts, fundamental data structures and algorithms, recursion, and more. Assessment includes assignments, quizzes, class tests, and a final exam. Common operations on data structures like traversal, insertion, deletion, and searching are explained.
This document provides an overview of data structures and algorithms. It introduces common linear data structures like stacks, queues, and linked lists. It discusses the need for abstract data types and different data types. It also covers implementing stacks as a linked list and common stack operations. Key applications of stacks include function call stacks which use a LIFO structure to remember the order of function calls and returns.
- The document discusses one-dimensional and two-dimensional arrays in C. It defines arrays, provides examples of declaring and initializing arrays, and describes common array operations like insertion, deletion, modification, and traversal. It also discusses two-dimensional arrays and provides an example program to add two matrices using a 2D array. The key topics covered are the basic concepts of arrays, declaring and initializing single and multi-dimensional arrays, and common operations performed on arrays.
This document discusses various applications of common data structures like linked lists, stacks, queues, and trees. It provides examples of how linked lists are used to implement queues and stacks, and in web browsers to store browsing history. It also gives examples of how stacks can be used for reversing words, undo/redo functions, matching parentheses in compilers, and modeling real-world examples like plates in a cupboard. Applications of queues include asynchronous data transfer and resource sharing. Trees are used in operating systems to represent folder structures, in HTML for the document object model, for network routing, syntax trees in compilers, and modeling game moves in AI.
Introduction to datastructure and algorithmPratik Mota
Introduction to data structure and algorithm
-Basics of Data Structure and Algorithm
-Practical Examples of where Data Structure Algorithms is used
-Asymptotic Notations [ O(n), o(n), θ(n), Ω(n), ω(n) ]
-Calculation of Time and Space Complexity
-GNU gprof basic
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
An algorithm is a finite set of instructions to accomplish a predefined task. Performance of an algorithm is measured by its time and space complexity, with common metrics being big O, big Omega, and big Theta notation. Common data structures include arrays, linked lists, stacks, queues, trees and graphs. Key concepts are asymptotic analysis of algorithms, recursion, and analyzing complexity classes like constant, linear, quadratic and logarithmic time.
1. The Python if statement is used to execute code conditionally based on the evaluation of an expression. It can be used with simple conditions, if-else statements, and nested if statements.
2. Python if-else statements allow executing one block of code if a condition is true and another block if the condition is false.
3. Nested if statements in Python allow checking multiple conditions, with inner if statements executing only if the outer conditions are met.
This document provides an overview of data structures and algorithms analysis. It discusses big-O notation and how it is used to analyze computational complexity and asymptotic complexity of algorithms. Various growth functions like O(n), O(n^2), O(log n) are explained. Experimental and theoretical analysis methods are described and limitations of experimental analysis are highlighted. Key aspects like analyzing loop executions and nested loops are covered. The document also provides examples of analyzing algorithms and comparing their efficiency using big-O notation.
This document provides an overview of data structures and algorithms. It discusses key concepts like interfaces, implementations, time complexity, space complexity, asymptotic analysis, and common control structures. Some key points:
- A data structure organizes data to allow for efficient operations. It has an interface defining operations and an implementation defining internal representation.
- Algorithm analysis considers best, average, and worst case time complexities using asymptotic notations like Big O. Space complexity also measures memory usage.
- Common control structures include sequential, conditional (if/else), and repetitive (loops) structures that control program flow based on conditions.
The document discusses data structures and algorithms. It defines a data structure as a particular way of organizing data in a computer so that it can be used efficiently. Common data structures include arrays, stacks, queues, linked lists, trees, heaps, and hash tables. An algorithm is defined as a finite set of instructions to accomplish a particular task. Analyzing algorithms involves determining how resources like time and storage change with input size. Key considerations in algorithm design include requirements, analysis, data objects, operations, refinement, coding, verification, and testing.
The document discusses strings, arrays, pointers, and sorting algorithms in C programming. It provides definitions and examples of:
1) Strings as null-terminated character arrays. It demonstrates initializing and printing a string.
2) One-dimensional and two-dimensional arrays. It shows how to declare, initialize, access, and print multi-dimensional arrays.
3) Pointers as variables that store memory addresses. It explains pointer declaration and dereferencing pointers using asterisk (*) operator.
4) Bubble sort algorithm that iterates through an array and swaps adjacent elements if out of order, putting largest elements at the end of the array in each iteration.
- A linked list is a data structure where elements are linked using pointers. Each element points to the next element.
- Linked lists allow dynamic memory allocation as elements can be added or removed without relocating the entire structure. However, accessing a specific element takes linear time.
- Arrays provide constant time access to elements but require fixed block allocation. Adding or removing elements may require allocating new memory and copying the entire array.
Describe about the heap memory management such as memory allocation & deallocation. Explained the Memory manager functionality and fragmentation issues.
This document provides an introduction to data structures, including definitions, types, and operations. It defines a data structure as a particular way of organizing data in computer memory for effective use and retrieval. Data structures are classified as primitive (directly manipulated by machine instructions) and non-primitive (requiring machine instructions to manipulate). Non-primitive structures include linear (ordered sequences like arrays and lists) and non-linear (graphs and trees) types. Common operations on data structures include traversing, inserting, deleting, updating, searching, and sorting. The performance of algorithms that use these structures depends on their time complexity (computation time) and space complexity (memory usage). Lists and arrays are introduced as examples of linear data structures.
This document provides an overview of data structures and algorithms. It discusses primitive and non-primitive data structures. Non-primitive structures include arrays, files, lists (linear and non-linear), stacks, queues, graphs and trees. Common operations on data structures are creation, destruction, selection, and updation. Arrays have limitations such as static memory allocation and complex insertions/deletions. Lists allow dynamic memory allocation. The document also discusses merging arrays, two-dimensional arrays stored in row-major and column-major format, sparse matrices, and representing polynomials using arrays.
This document provides an introduction to data structures, including definitions, types, and operations. It defines a data structure as a particular way of organizing data in computer memory for effective use and retrieval. Data structures are classified as primitive (directly manipulated by machine instructions) and non-primitive (requiring machine instructions). Non-primitive structures include linear (ordered sequences like arrays and lists) and non-linear (graphs and trees). Common operations on data structures include traversing, inserting, deleting, updating, searching, and sorting. The document also discusses abstract data structures, algorithm analysis and complexity measures like time and space complexity, and defines lists and arrays.
This document provides an introduction to data structures. It defines data structures as a way of organizing data in computer memory so it can be used effectively. There are two main types: primitive structures like integers that can be directly manipulated, and non-primitive structures like lists that require additional operations. Non-primitive structures include linear structures like arrays and linked lists that arrange data in a sequence, as well as non-linear structures like trees and graphs. Common operations on data structures include traversing, inserting, deleting, updating, searching, and sorting data. The performance of algorithms that use data structures depends on factors like time complexity, which measures computation time, and space complexity, which measures memory usage. Lists and arrays are introduced as examples of data
This document provides an overview of common data structures and algorithms. It discusses static and dynamic data structures, including arrays, linked lists, stacks, and queues. Arrays allow storing multiple elements of the same type and can be one-dimensional, two-dimensional, or multidimensional. Linked lists connect nodes using pointers and can be singly linked, doubly linked, or circular linked. Stacks follow LIFO principles using push and pop operations, while queues use enqueue and dequeue following FIFO order. These data structures find applications in areas like memory management, expression evaluation, job scheduling, and graph searches.
Linear data structures include arrays, strings, stacks, queues, and lists. Arrays store elements contiguously in memory, allowing efficient random access. Linked lists store elements non-contiguously, with each element containing a data field and pointer to the next element. This allows dynamic sizes but less efficient random access. Linear data structures are ordered, with each element having a single predecessor and successor except for the first and last elements.
There are two types of memory allocation: static and dynamic. Static allocation is done by the compiler and allocates memory for global and local variables that exists for the lifetime of the program or function. Dynamic allocation is done explicitly by the programmer using operators like new and delete. It allows memory to be allocated and resized as needed, like for arrays of unknown size. The new operator allocates memory and returns a pointer, and delete must be called to free the memory when it is no longer needed.
The document discusses different levels of computer memory and cache memory. It describes four levels of memory:
1) Register - Stores data accepted by the CPU.
2) Cache memory - Faster memory that temporarily stores frequently accessed data from main memory.
3) Main memory - The memory the computer currently works on but data is lost when powered off.
4) Secondary memory - External memory that stores data permanently but is slower than main memory.
It then discusses cache memory in more detail, describing it as very high-speed memory that stores copies of frequently used data from main memory to reduce average access time. It explains the concepts of cache hits, misses, and hit ratio. Finally, it
This document provides an introduction to data structures and algorithms. It defines data structures as storage used to organize data and algorithms as sets of instructions to solve problems. Common linear data structures described include arrays, stacks, queues and linked lists. Non-linear structures include trees and graphs. The document explains that different problems require different data structures and algorithms to efficiently store and process data. Overall, understanding data structures and algorithms is essential for programming to select the best approach for a given task.
This document discusses the differences between the stack and the heap in computing memory. The stack is a temporary storage area where function variables are stored. Data is added or removed in a last-in, first-out manner. The stack has a fixed size and data is automatically deleted when a function exits. The heap is used for dynamic memory allocation and data remains until manually deleted. The stack is faster than the heap for memory allocation due to its structure. Examples are given showing how variables are allocated on the stack or heap.
The document discusses arrays and linked lists as abstract data types (ADTs). It describes arrays as the simplest data structure, storing elements in sequential memory locations. Linked lists store elements using pointers, with each node containing data and a pointer to the next node. The document outlines common operations on arrays and linked lists like traversal, insertion, deletion, and searching. It also discusses different types of linked lists like singly linked, doubly linked, and circular linked lists.
Memory management is important for mobile devices with limited memory. There are several strategies for allocating variables to memory areas like the stack and heap. Design patterns for limited memory include using linear data structures, reusing objects, and compressing data. In mobile Java, avoiding small classes, dependencies, and unnecessary objects can reduce memory usage. Strategies also include using StringBuffers and efficiently handling arrays and strings.
Data structures and algorithms Module-1.pdfDukeCalvin
This document provides an introduction to the course "Introduction to Data Structures and Algorithms". The course objectives are for students to learn basic static and dynamic data structures, analyze algorithms in terms of time and memory complexity, and understand the advantages and disadvantages of different algorithms and data structures. The document then discusses what algorithms are, their key characteristics, and why understanding algorithms and data structures is important for solving computational problems efficiently. It also defines what data structures are and why they are needed to organize large amounts of data.
This document discusses different data structures and their implementation. It describes linear data structures like arrays, linked lists, stacks and queues that store elements sequentially and non-linear structures like trees and graphs that store elements non-sequentially. It also discusses abstract data types, static and dynamic implementation of data structures, and built-in versus user defined data structures. Real life applications of different data structures are provided.
The document discusses the Standard Template Library (STL) in C++. It describes the three key components of STL - containers, algorithms, and iterators. Containers like vectors, lists, and maps are used to store and organize data. Algorithms perform operations on data stored in containers. Iterators are used by algorithms to access container elements. The containers, algorithms, and iterators work together to provide support for various programming solutions.
This document discusses run-time addressing and storage of variables in programming. It covers how variables are accessed using offsets from frames or stacks. It also discusses variable-length local data and how it can be allocated dynamically on the stack or heap. The document then covers scope, static and dynamic scoping rules, and how static links are used to access non-local variables at run-time.
This document provides an overview of advanced data structures and algorithms. It defines data structures as a way to store and organize data for efficient access and modification. The document then reviews basic concepts like variables, data types, and algorithms. It describes common data structures like arrays, linked lists, stacks, queues, trees, and hash tables. It also covers topics like asymptotic analysis, algorithms analysis, and the substitution method for solving algorithm recurrences. The document is an introduction to advanced data structure and algorithm concepts.
Feature selection is the process of selecting a subset of the terms occurring in the training set and using only this subset as features in text classification.
Wi-Fi stands for Wireless Fidelity. Fidelity: A faithful output.
Generally used to connect devices in wireless mode.
It is a term that refers to IEEE 802.11 communications
Li-Fi stands for Light Fidelity.
Uses light instead of radio waves.
Uses Visible part of electromagnetic spectrum.
Also known as Light based Wi-Fi.
Communication channels through which news entertainment, education, data, or promotional messages are delivered is known as media. Media includes every broadcasting and narrow-casting medium such as Newspaper, Magazines, Television, Radio, Billboards, Direct mail, Telephone, Fax and Internet.
AGILE TESTING is a testing practice that follows the rules and principles of agile software development. Unlike the Waterfall method, Agile Testing can begin at the start of the project with continuous integration between development and testing. Agile Testing is not sequential but continuous. Agile testing involves testing as early as possible in the software development life-cycle.
Ethical hacking also known as penetration testing or white-hat hacking, involves the same tools, tricks, and techniques that hackers use, but with one major difference that Ethical hacking is legal. It focuses on authorised attempts to gain unauthorised access to systems and find vulnerabilities. Ethical hacking is done with the legal permission of a company to test and increase the security of its systems and networks.
In software engineering and software architecture design, design decisions address architecturally significant requirements; they are perceived as hard to make and/or costly to change. It is called also architecture strategies and tactics.
An algorithm is a plan, a logical step-by-step process for solving a problem. Algorithms are normally written as a flowchart or in pseudo-code.
A flowchart is a diagram that represents a set of instructions. Flowcharts normally use standard symbols to represent the different types of instructions. These symbols are used to construct the flowchart and show the step-by-step solution to the problem.
Software Quality Assurance is a means and practice of monitoring the software engineering processes and methodologies used in a project to ensure proper quality of the software. Scrum is a framework utilising an agile mindset for developing, delivering, and sustaining products in a complex environment.
Quicksort is a divide and conquer algorithm that works by partitioning an array around a pivot value and recursively sorting the subarrays. It first selects a pivot element and partitions the array by moving all elements less than the pivot before it and greater elements after it. The subarrays are then recursively sorted through this process. When implemented efficiently with an in-place partition, quicksort is one of the fastest sorting algorithms in practice, with average case performance of O(n log n) time but worst case of O(n^2) time.
This presentation defines the asteroids and describes its role in our solar system and how to protect our earth from these space rocks in case of any mishap.
The document discusses double and circular linked lists. It covers inserting and deleting nodes from doubly linked lists and circular linked lists. Specifically, it describes how to insert nodes at different positions in a doubly linked list, such as at the front, after a given node, at the end, and before a given node. It also explains how to delete nodes from a doubly linked list. For circular linked lists, it outlines how to insert nodes in an empty list, at the beginning, at the end, and between nodes. It also provides the steps to delete nodes from a circular linked list.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Revolutionizing Visual Effects Mastering AI Face Swaps.pdfUndress Baby
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
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A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
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4. 4
Problem with Arrays
• Sometimes
▫ Amount of data cannot be predicted beforehand
▫ Number of data items keeps changing during program execution
• Example: Search for an element in an array of N elements
• One solution: find the maximum possible value of N and allocate an
array of N elements
▫ Wasteful of memory space, as N may be much smaller in some
executions
▫ Example: maximum value of N may be 10,000, but a particular
run may need to search only among 100 elements
Using array of size 10,000 always wastes memory in most cases
5. Better Solution
• Dynamic memory allocation
▫ Know how much memory is needed after the program is
run
Example: ask the user to enter from keyboard
▫ Dynamically allocate only the amount of memory needed
5
7. Memory Allocation - Static
Static – Done by the compiler automatically (implicitly).
o Global variables or objects -- memory is allocated at the start
of the program, and freed when program exits; alive
throughout program execution
1. Can be access anywhere in the program.
o Local variables (inside a routine) – memory is allocated when
the routine starts and freed when the routine returns.
1. A local variable cannot be accessed from another routine.
o Allocation and free are done implicitly.
o No need to explicitly manage memory is nice, but has
limitations!
1. Using static allocation, the array size must be fixed.
8. Memory Allocation - Dynamic
Wouldn’t it be nice to be able to have an array whose
size can be adjusted depending on needs.
Dynamic memory allocation deals with this situation.
Dynamic – Done explicitly by programmer.
Programmer explicitly requests the system to allocate
memory and return starting address of memory allocated
(what is this?). This address can be used by the
programmer to access the allocated memory.
When done using memory, it must be explicitly freed.
9. Explicitly allocating memory in C++:
The ‘new’ Operator
• Used to dynamically allocate memory
• Can be used to allocate a single variable/object or an
array of variables/objects
• The new operator returns pointer to the type
allocated
• Before the assignment, the pointer may or may not
point to a legitimate memory
• After the assignment, the pointer points to a
legitimate memory.
10.
11. Explicitly freeing memory in C++:
The ‘delete’ Operator
• Used to free memory allocated with new operator
• The delete operator should be called on a pointer to
dynamically allocated memory when it is no longer needed
• Can delete a single variable/object or an array
delete PointerName;
delete [] ArrayName;
• After delete is called on a memory region, that region should
no longer be accessed by the program
• Convention is to set pointer to deleted memory to NULL
Any new must have a corresponding delete --- if not, the
program has memory leak.
New and delete may not be in the same routine.
12.
13. Example
• int main()
• {
• // Below variables are allocated memory
• // dynamically.
• int *ptr1 = new int;
• int *ptr2 = new int[10];
•
• // Dynamically allocated memory is
• // deallocated
• delete ptr1;
• delete [] ptr2;
• }
14. The Heap
• Large area of memory controlled by the runtime
system that is used to grant dynamic memory
requests.
• It is possible to allocate memory and “lose” the
pointer to that region without freeing it. This is
called a memory leak.
• A memory leak can cause the heap to become full
• If an attempt is made to allocate memory from the
heap and there is not enough, an exception is
generated (error)
15. Why use dynamic memory allocation?
• Allows data (especially arrays) to take on variable
sizes (e.g. ask the user how many numbers to store,
then generate an array of integers exactly that size).
• Allows locally created variables to live past end of
routine.
• Allows us to create many structures used in Data
Structures and Algorithms
16. The . and -> operators
• The dot operator is used to access an object’s members
▫ M1.Simplify();
▫ M1.num = 5;
• But how do we access an objects members if we only
have a pointer to the object?
• If we have M1_ptr = &M1, Perhaps we would use
(*(M1_ptr)).Simplify()
• A shorthand for this is the arrow operator
• M1_ptr->Simplify() is equivalent to(*(M1_ptr)).Simplify()
18. Linked Lists
• Linked List is a very commonly used linear data
structure which consists of group of nodes in a
sequence.
• Each node holds its own data and the address of the
next node hence forming a chain like structure.
• Linked Lists are used to create trees and graphs.
20. Types of Linked Lists
There are 3 different implementations of Linked List
available, they are:
1. Singly Linked List
2. Doubly Linked List
3. Circular Linked List
21. Singly Linked List
• Singly linked lists contain nodes which have a data
part as well as an address part i.e. next, which points
to the next node in the sequence of nodes.
• The operations we can perform on singly linked lists
are insertion, deletion and traversal.
22.
23. Doubly Linked List
• In a doubly linked list, each node contains a data part
and two addresses, one for the previous node and
one for the next node.
24.
25. Circular Linked List
• In circular linked list the last node of the list holds
the address of the first node hence forming a circular
chain.
26.
27. Advantages of Linked Lists
• They are a dynamic in nature which allocates the
memory when required.
• Insertion and deletion operations can be easily
implemented.
• Linked List reduces the access time.
28. Disadvantages of Linked Lists
• The memory is wasted as pointers require extra
memory for storage.
• No element can be accessed randomly; it has to
access each node sequentially.
• Reverse Traversing is difficult in linked list.
• Not cache friendly. Since array elements are
contiguous locations, there is locality of reference
which is not there in case of linked lists.
29. Applications of Linked Lists
• Linked lists are used to implement stacks, queues,
graphs, etc. Linked lists let you insert elements at the
beginning and end of the list. In Linked Lists we don't
need to know the size in advance
30. Array vs Linked List
• Both Linked List and Array are used to store linear
data of similar type, but an array consumes
contiguous memory locations allocated at compile
time, i.e. at the time of declaration of array, while for
a linked list, memory is assigned as and when data is
added to it, which means at runtime.
31. On the left, we have Array and on the right, we have
Linked List.
32. Summary
• Dynamic Memory Allocation
▫ New Operator
▫ Delete Operator
▫ Heap
▫ dot . and -> operators
• Introduction to Linked List
▫ Types
▫ Advantages
▫ Disadvantages
▫ Applications
▫ Difference between Array and Linked List