Agile project management

1. Agile project management focuses on continuous improvement, scope flexibility, team input, and delivering essential quality products. Agile project management approaches include scrum as a framework, extreme programming (XP) for building in quality upfront, and lean thinking to eliminate waste.

2. Agile project management is an iterative approach to managing software development projects that focuses on continuous releases and incorporating customer feedback with every iteration.
Traditional agile project management can be categorized into two frameworks: scrum and kanban. While scrum is focused on fixed-length project iterations, kanban is focused on continuous releases. Upon completion, the team immediately moves on to the next. 

How scrum works

Scrum is a framework for agile project management that uses fixed-length iterations of work, called sprints. There are four ceremonies that bring structure to each sprint.

SPRINT PLANNING

A team planning meeting that determines what to complete in the coming sprint.

SPRINT DEMO

 A sharing meeting where the team shows what they've shipped in that sprint.

DAILY SCRUM

Also known as a stand-up, a 15-minute mini-meeting for the software team to sync.

RETROSPECTIVE

A review of what did and didn't go well with actions to make the next sprint better.

How kanban works

Kanban is a framework for agile project management that matches the work to the team's capacity. It's focused on getting things done as fast as possible, giving teams the ability to react to change even faster than scrum.

The kanban framework includes the following four components:

LIST OF WORK 
(OR STORIES)

List of work, or stories, are defined as issues or tasks that need to get done.

WORK IN PROGRESS
(WIP) LIMITS

A rule to limit the amount of work to be done based on the team's capacity. 

WORK IN PROGRESS
(WIP) LIMITS

A rule to limit the amount of work to be done based on the team's capacity. 

CONTINUOUS RELEASES

The team works on the amount of stories within the WIP limit and can release at anytime.

Agile project estimating

Project estimating is an extremely important aspect of both kanban and scrum project management. For kanban, many teams set their WIP limit for each state based on their previous experiences and team size. Scrum teams use project estimating to identify how much work can be done in a particular sprint. Many agile teams adopt unique estimating techniques like planning poker, ideal hours, or story points to determine a numeric value for the task at hand.

Agile reporting

Project estimations come into play at the beginning and end of each sprint. They help teams determine what they can get done at the beginning of the sprint, but also show how accurate those initial estimates were at the end. 

1. Software Firm/team Advantages of using Agile: 

• Our highest priority is to satisfy the customer through early and continuous delivery of valuable software.
• Welcome changing requirements, even late in development. Agile processes harness change for the customer’s competitive advantage.
• Deliver working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale.
• Business people and developers must work together daily throughout the project.
• Build projects around motivated individuals. Give them the environment and support they need, and trust them to get the job done.
• The most efficient and effective method of conveying information to and within a development team is face-to-face conversation and a big community of agile practitioners with whom you can share knowledge.
• You can detect and fix issues and defects faster
• Working software is the primary measure of progress.
• Agile processes promote sustainable development. The sponsors, developers, and users should be able to maintain a constant pace indefinitely.
• Continuous attention to technical excellence and good design enhances agility.
• Simplicity — the art of maximizing the amount of work not done — is essential.
• The best architectures, requirements, and designs emerge from self-organizing teams.
• Developers can improve their coding skills based on QA feedback.
• You can experiment and test ideas because it costs are low

1. Disadvantages of agile project management

• Documentation tends to get sidetracked, which makes it harder for new members to get up to speed
• It’s more difficult to measure progress than in waterfall because progress happens across several cycles
• Agile demands more time and energy from everyone because developers and customers must constantly interact with each other
• When developers run out of work, they can’t work on a different project because they’ll be needed soon
• Projects can become ever-lasting because there’s no clear end of that system.
• Clients who work on a specified budget or schedule can’t know how much the project will actually cost, which makes for a very complicated sales cycle (until iteration ends is not something clients like to hear)
• Teams can get sidetracked into delivering new functionality at the expense of technical debt, which increases the amount of unplanned work
• Features that are too big to fit into one or even several cycles are avoided because they don’t fit in nicely into the philosophy
• You need a long term vision for the product and actively work on communicating it
• Products lack cohesion and the user journey is fragmented because the design is fragmented. The more time passes, the more disjointed software ends up.
• Short cycles don’t leave enough time for the design thinking process so designers have to redevelop the experience over and over due to negative feedback.

C Program to Find LCM and HCF of Two Numbers

#include<stdio.h>   int main() {     int a,b,hcf,lcm,max,min,r;     printf("Enter two numbers:");     scanf("%d%d",&a,&b);          if(a>b)     {         max=a;         min=b;     }     else         if(b>a)         {             max=b;             min=a;         }          if(a==b)         hcf=a;     else     {         do         {             r=max%min;             max=min;             min=r;         }while(r!=0);                  hcf=max;     }          lcm=(a*b)/hcf;          printf("\nLCM=%d\nHCF=%d",lcm,hcf);       return 0; }

Classification of algorithms

There are broadly 4 ways in which classification of algorithms can be done.

1.  Classification by purpose

Each algorithm has a goal, for example, the purpose of the Quick Sort algorithm is to sort data in ascending or descending order. But the number of goals is infinite, and we have to group them by kind of purposes.

2.  Classification by implementation

• Recursive or iterative
  A recursive algorithm is one that calls itself repeatedly until a certain condition matches. It is a method common to functional programming. 
  For example, the towers of hanoi problem is well understood in recursive implementation. Every recursive version has an iterative equivalent iterative, and vice versa.

• Logical or procedural
  An algorithm may be viewed as controlled logical deduction. 
  A logic component expresses the axioms which may be used in the computation and a control component determines the way in which deduction is applied to the axioms.
• Serial or parallel                                                                                Algorithms are usually discussed with the assumption that computers execute one instruction of an algorithm at a time. This is a serial algorithm, as opposed to parallel algorithms, which take advantage of computer architectures to process several instructions at once. Sorting algorithmscan be parallelized efficiently.

• Deterministic or non-deterministic
  Deterministic algorithms solve the problem with a predefined process whereas non-deterministic algorithm must perform guesses of best solution at each step through the use of heuristics.

3.   Classification by design paradigm

• Divide and conquer
  A divide and conquer algorithm repeatedly reduces an instance of a problem to one or more smaller instances of the same problem (usually recursively), until the instances are small enough to solve easily. One such example of divide and conquer is merge sorting. The binary search algorithm is an example of a variant of divide and conquer called decrease and conquer algorithm, that solves an identical subproblem and uses the solution of this subproblem to solve the bigger problem. 
• Dynamic programming
  The shortest path in a weighted graph can be found by using the shortest path to the goal from all adjacent vertices. 
  When the optimal solution to a problem can be constructed from optimal solutions to subproblems, using dynamic programming avoids recomputing solutions that have already been computed. 
  - The main difference with the "divide and conquer" approach is, subproblems are independent in divide and conquer, where as the overlap of subproblems occur in dynamic programming. 
  - Dynamic programming and memoization go together. The difference with straightforward recursion is in caching or memoization of recursive calls. Where subproblems are independent, this is useless. By using memoization or maintaining a table of subproblems already solved, dynamic programming reduces the exponential nature of many problems to polynomial complexity.
• The greedy method
  A greedy algorithm is similar to a dynamic programming algorithm, but the difference is that solutions to the subproblems do not have to be known at each stage. Instead a "greedy" choice can be made of what looks the best solution for the moment. 
  The most popular greedy algorithm is finding the minimal spanning tree as given by Kruskal.
• Linear programming
  The problem is expressed as a set of linear inequalities and then an attempt is made to maximize or minimize the inputs. This can solve many problems such as the maximum flow for directed graphs, notably by using the simplex algorithm. 
  A complex variant of linear programming is called integer programming, where the solution space is restricted to all integers.
• Reduction also called transform and conquer
  Solve a problem by transforming it into another problem. A simple example:finding the median in an unsorted list is first translating this problem into sorting problem and finding the middle element in sorted list. The main goal of reduction is finding the simplest transformation possible.
• Using graphs
  Many problems, such as playing chess, can be modeled as problems on graphs. A graph exploration algorithms are used. 
  This category also includes the search algorithms and backtracking.

4.  The probabilistic and heuristic paradigm 

1. Probabilistic 
   Those that make some choices randomly.
2. Genetic 
   Attempt to find solutions to problems by mimicking biological evolutionary processes, with a cycle of random mutations yielding successive generations of "solutions". Thus, they emulate reproduction and "survival of the fittest".
3. Heuristic 
   Whose general purpose is not to find an optimal solution, but an approximate solution where the time or resources to find a perfect solution are not practical.

1. Searching and sorting algorithms -
Sorting algorithms include Quicksort , Merge sort, Heapsort, Bubble sort,Insertion sort, Radix sort. Other imp soting algorithms are Topological sort, Counting sort, Shell sort A comprehensive list can be found here.

Important searching algorithms include breadth/ depth first  search, binary search etc.

2. Dynamic Programming -- To name a few DP problems, Longest Common Subsequence problem, Knapsack, travelling salesman problemetc.

3. Graph algorithms -- Important graph algorithms are Dijkstra, Prim,Kruskal, Bellman-Ford. A comprehensive list can be found