Thursday, 27 June 2019

SOFTWARE ENGINEERING





Software Engineering


                     Software engineering is an engineering branch associated with development of software product using well-defined scientific principles, methods and procedures. The outcome of software engineering is an efficient and reliable software product.
Software project management has wider scope than software engineering process as it involves communication, pre and post delivery support etc.
Software is more than just a program code. A program is an executable code, which serves some computational purpose. Software is considered to be collection of executable programming code, associated libraries and documentations. Software, when made for a specific requirement is called software product.
                        Engineering on the other hand, is all about developing products, using well-defined, scientific principles and methods.


Software engineering is an engineering branch associated with development of software product using well-defined scientific principles, methods and procedures. The outcome of software engineering is an efficient and reliable software product.

Definitions

IEEE defines software engineering as:
(1) The application of a systematic,disciplined,quantifiable approach to the development,operation and maintenance of software; that is, the application of engineering to software.
(2) The study of approaches as in the above statement.
Fritz Bauer, a German computer scientist, defines software engineering as:
Software engineering is the establishment and use of sound engineering principles in order to obtain economically software that is reliable and work efficiently on real machines.

Software Evolution

The process of developing a software product using software engineering principles and methods is referred to as software evolution. This includes the initial development of software and its maintenance and updates, till desired software product is developed, which satisfies the expected requirements.


Evolution starts from the requirement gathering process. After which developers create a prototype of the intended software and show it to the users to get their feedback at the early stage of software product development. The users suggest changes, on which several consecutive updates and maintenance keep on changing too. This process changes to the original software, till the desired software is accomplished.
Even after the user has desired software in hand, the advancing technology and the changing requirements force the software product to change accordingly. Re-creating software from scratch and to go one-on-one with requirement is not feasible. The only feasible and economical solution is to update the existing software so that it matches the latest requirements.


Need of Software Engineering

The need of software engineering arises because of higher rate of change in user requirements and environment on which the software is working.
  • Large software - It is easier to build a wall than to a house or building, likewise, as the size of software become large engineering has to step to give it a scientific process.
  • Scalability- If the software process were not based on scientific and engineering concepts, it would be easier to re-create new software than to scale an existing one.
  • Cost- As hardware industry has shown its skills and huge manufacturing has lower down he price of computer and electronic hardware. But the cost of software remains high if proper process is not adapted.
  • Dynamic Nature- The always growing and adapting nature of software hugely depends upon the environment in which user works. If the nature of software is always changing, new enhancements need to be done in the existing one. This is where software engineering plays a good role.
  • Quality Management- Better process of software development provides better and quality software product.

Characteristics of good software

A software product can be judged by what it offers and how well it can be used. This software must satisfy on the following grounds:
  • Operational
  • Transitional
  • Maintenance
Well-engineered and crafted software is expected to have the following characteristics:

Operational

This tells us how well software works in operations. It can be measured on:
  • Budget
  • Usability
  • Efficiency
  • Correctness
  • Functionality
  • Dependability
  • Security
  • Safety

Transitional

This aspect is important when the software is moved from one platform to another:
  • Portability
  • Interoperability
  • Reusability
  • Adaptability

Maintenance

This aspect briefs about how well a software has the capabilities to maintain itself in the ever-changing environment:
  • Modularity
  • Maintainability
  • Flexibility
  • Scalability
In short, Software engineering is a branch of computer science, which uses well-defined engineering concepts required to produce efficient, durable, scalable, in-budget and on-time software products.




                                                                                                By
                                                                                                K.Deepa


                                                                                                Dept of Computer Applications

DATA MINING BASICS




                                                                               What is Data Mining?

                Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. The information or knowledge extracted so can be used for any of the following applications :
  • Market Analysis
  • Fraud Detection
  • Customer Retention
  • Production Control
  • Science Exploration
Data Mining Applications
Data mining is highly useful in the following domains −
  • Market Analysis and Management
  • Corporate Analysis & Risk Management
  • Fraud Detection
Apart from these, data mining can also be used in the areas of production control, customer retention, science exploration, sports, astrology, and Internet Web Surf-Aid
Market Analysis and Management
Listed below are the various fields of market where data mining is used −
·        Customer Profiling − Data mining helps determine what kind of people buy what kind of products.
·        Identifying Customer Requirements − Data mining helps in identifying the best products for different customers. It uses prediction to find the factors that may attract new customers.
·        Cross Market Analysis − Data mining performs Association/correlations between product sales.
·        Target Marketing − Data mining helps to find clusters of model customers who share the same characteristics such as interests, spending habits, income, etc.
·        Determining Customer purchasing pattern − Data mining helps in determining customer purchasing pattern.
·        Providing Summary Information − Data mining provides us various multidimensional summary reports.
Corporate Analysis and Risk Management
Data mining is used in the following fields of the Corporate Sector −
·        Finance Planning and Asset Evaluation − It involves cash flow analysis and prediction, contingent claim analysis to evaluate assets.
·        Resource Planning − It involves summarizing and comparing the resources and spending.
·        Competition − It involves monitoring competitors and market directions.
Fraud Detection
Data mining is also used in the fields of credit card services and telecommunication to detect frauds. In fraud telephone calls, it helps to find the destination of the call, duration of the call, time of the day or week, etc. It also analyzes the patterns that deviate from expected norms.
Data mining deals with the kind of patterns that can be mined. On the basis of the kind of data to be mined, there are two categories of functions involved in Data Mining −
  • Descriptive
  • Classification and Prediction

Descriptive Function

The descriptive function deals with the general properties of data in the database. Here is the list of descriptive functions −
  • Class/Concept Description
  • Mining of Frequent Patterns
  • Mining of Associations
  • Mining of Correlations
  • Mining of Clusters

Class/Concept Description

Class/Concept refers to the data to be associated with the classes or concepts. For example, in a company, the classes of items for sales include computer and printers, and concepts of customers include big spenders and budget spenders. Such descriptions of a class or a concept are called class/concept descriptions. These descriptions can be derived by the following two ways −
·        Data Characterization − This refers to summarizing data of class under study. This class under study is called as Target Class.
·        Data Discrimination − It refers to the mapping or classification of a class with some predefined group or class.

Mining of Frequent Patterns

Frequent patterns are those patterns that occur frequently in transactional data. Here is the list of kind of frequent patterns −
·        Frequent Item Set − It refers to a set of items that frequently appear together, for example, milk and bread.
·        Frequent Subsequence − A sequence of patterns that occur frequently such as purchasing a camera is followed by memory card.
·        Frequent Sub Structure − Substructure refers to different structural forms, such as graphs, trees, or lattices, which may be combined with item-sets or subsequences.

Mining of Association

Associations are used in retail sales to identify patterns that are frequently purchased together. This process refers to the process of uncovering the relationship among data and determining association rules.
For example, a retailer generates an association rule that shows that 70% of time milk is sold with bread and only 30% of times biscuits are sold with bread.

Mining of Correlations

It is a kind of additional analysis performed to uncover interesting statistical correlations between associated-attribute-value pairs or between two item sets to analyze that if they have positive, negative or no effect on each other.

Mining of Clusters

Cluster refers to a group of similar kind of objects. Cluster analysis refers to forming group of objects that are very similar to each other but are highly different from the objects in other clusters.

Classification and Prediction

Classification is the process of finding a model that describes the data classes or concepts. The purpose is to be able to use this model to predict the class of objects whose class label is unknown. This derived model is based on the analysis of sets of training data. The derived model can be presented in the following forms −
  • Classification (IF-THEN) Rules
  • Decision Trees
  • Mathematical Formulae
  • Neural Networks
The list of functions involved in these processes are as follows 
·        Classification − It predicts the class of objects whose class label is unknown. Its objective is to find a derived model that describes and distinguishes data classes or concepts. The Derived Model is based on the analysis set of training data i.e. the data object whose class label is well known.
·        Prediction − It is used to predict missing or unavailable numerical data values rather than class labels. Regression Analysis is generally used for prediction. Prediction can also be used for identification of distribution trends based on available data.
·        Outlier Analysis − Outliers may be defined as the data objects that do not comply with the general behavior or model of the data available.
·        Evolution Analysis − Evolution analysis refers to the description and model regularities or trends for objects whose behavior changes over time.


                                                                                                         BY
                                                                                                         S.SANGEETHA
                                                                                                         Asst Professor
                                                                                                         Dept of Computer Applications




Wednesday, 26 June 2019

MINIPROJECT GUIDE ALLOTED FOR PG STUDENTS

The III year MCA students are asked to meet their guides for discuss about miniprojects and follow the rules and regulations of project work that has been given in the Annexure-I,Annexure-II, Annexure-III.



Mini Project guide details:




S.No


Guide Name

Student Name

1
J. Sasirekha
G.Divya
A.Nandhini
2
P.Prabavathy
C.Asha
3
T.Thenmozhi
S.Kalaivani
4
M.Prithi
V.Priya
P.Ranjini
5
S.Sangeetha
T.Sridevi
6
R.Sandhiya
P.Thilagavathi
7
G.Sujatha
V.Sangavi
8
K.Deepa
G.Shalini
9
R.Janani
D.Indhu Mathi
10
S.Kanimozhi
M.Madhumitha
11
L.Sumi
T.Jayalakshmi