Software Engineering

THE RISK DRIVEN MODEL - Introduction As digital systems become part of everyday life for most of us that caused increase in the development of software systems and applications hugely. The increasing number also shows the worries of developers for risk of failure for different stages of the development and different platforms including web. The management of risk is very important factor to be considered for all software development processes. The risk driven model is very simple and easy to use with having low “risk of failure” level which attracts most of the developers. However, the development of software means developer must anticipate the possible risk of failure for different stages by using right tools and techniques. Though which development technique is more reliable? Or which architecture is enough for developer? And which is model should developer use is still a question. So here I present how risk driven is enough for the developers for all stages (including an

Weka - Data Mining Tool   Experiment  (Irish Dataset): In this experiment I am using Irish dataset and different algorithm to show classification using 10 fold cross-validation methods, there will be 10 repetitions on the processes to determine the results. (Note: Use of all other datasets and algorithms is similar process).  For beginners, start Weka and click on Experimenter Option 1          Using J48      Here I am using J48 algorithm to Irish datasets, the process is as follows: There are three panels starting with Setup Panel: ·          Click New to start new experiment ·          Click add new under datasets in order to add new dataset i.e. Irish.arff ·            Click add new under Algorithm in order to add new dataset i.e. J48 ·          Experiment type is cross validation by default  ·          And Its classification by default We can also choose other experiment types such as percentage split etc., regression types and we can set the number of

Weka - Data Mining Tool Data Sets You can download datasets from  Here   (eg. Adult, Irish, Zoo etc used here) and save the datasets in to “.arff” format. All the datasets are in numeric value format so I changed it into Nominal values in order to process by algorithms used in Weka by following process:          Click Open file in order to open dataset file eg. Adult.arff          Select Choose > Filter > Unsupervised >attribute> NumericToNominal          Click All to apply change to all  attributes          Click Apply          See the Result > Type: Nominal         Click Save to save the result eg. Adult.arff Furthermore, we can found number of attribute as 15, and instances as 32562 and Relation as conversion to Nominal. Same Procedure applied to all data sets for eg: In irish , we can found number of attribute as 5, and instances as 151 and Relation is not shown because here yet to apply the changes.And another  dataset (Zoo.arff) also follows sam

Weka - Data mining Tool W eka is a tool for big data and data mining. It is used to various classification, experiments, and analysis over large data sets. Installation Guide -weka  You can download Weka from   here   and follow the normal installation procedure. After completion you will get following window, here you can begin your classification or experiment on different data sets with Weka.