Classification of News Articles using Supervised Machine Learning Approach

Today the big challenge for NEWS organization to well organize the news and well categorize the news in automatically no need the data entry people to enter and select the category and then based on the category and its sub-category they will be manually selected and enter the details and then after this the analysis will later on used for different aspects. The news is almost every second used in different sources of media in soft and hard. We use the both sources of the Pakistan News in dual languages English and Urdu both and process them and prepare them for machine learning and based on the Machine learning trained data we build a very effective and efficient model that can predict the title category of the news and category of description of the news. We use different machine learning algorithms and different features extraction finally we build the model using the machine learning algorithm with 89% accuracy with logistic regression.

Quality Assurance of Medium Scale Projects Using Dxprum Methodology

Agile methods are being used as a new way of developing software. They are basically used to fasten software development cycle by keeping an eye on its quality. Using Agile methods in any project along with quality assurance perspective is a crucial task. Many software developing organizations are using Agile methods now a days in their projects to reduce cost involved in a project. Agile methods are best suited for small projects as they require less resources and able to generate output in less time. However, days many organizations are also using Agile methods for medium and large scale projects. These projects also use large teams. Moreover, there may be teams having members taking part in these projects at geographically distributed locations. Assuring quality of these projects using Agile method is a challenging task as in Agile, testing is also carried out in each iteration along with software development. So the system has to be up and running every time for testing purpose. It means testing is only easy when organizations are working on small projects. As the size of the projects increases, the quality assurance of these projects also becomes a difficult task. The projects become complex as they require more resources and time, so there is always a burden on quality assurance team to deliver. The project may go for days and weeks without a functional build and thus testing cannot be done on regular basis. Secondly as the size of the project gets larger by adding more and more features in it, the number of tests is also increased to assure that the system is still performing what it is supposed to perform. The assurance of the quality of such projects is always a difficult task for any quality assurance team. Many Agile methods are being used by software developing organizations to build high quality software which involve less cost and time. Each Agile method has its own strengths and weaknesses. This research work is based on purposing a new hybrid Agile methodology DXPRUM. The DXPRUM is a combination of three of the agile models named as Dynamic System Development Method, Extreme Programming and Scrum (D comes from DSDM, XP from Extreme Programing, RUM from Scrum). The objective of the research is to purpose a new Agile methodology DXPRUM which combines the strengths of all the three Agile methods by removing their weaknesses. The main strength of DXPRUM will be the in time delivery of the project to customer with reduced cost.and the weaknesses to overcome will be to reduce resource utilization without affecting the output and to remove the overlapped resources. This research will consider a medium scale project with the implementation of a new Agile methodology DXPRUM under the perspective of quality assurance. The results of this research will also be compared with results of all three methods individually to validate the research.The metrics used for the evaluation will be total time in weeks, total number of modules build, total predicted hours, total line of code (LOC) and pre and post release defects etc.