Observational Discoveries in Agile Methodologies and Extreme Programming

 The   In this study we have focused on various methodologies of nimble programming advancement, for example, Extreme Programming, Crystal Clear, Scrum, Lean programming improvement and some others methods related to category. As there are several methods related to agile development, but we have mainly focused on some of the important methodologies, discovered so far []. This study also reveals the criticism over some of the agile methodologies, based on some of its parameters, while in some situations favor is given to the traditional methodologies. We have adopted quantitative and qualitative approaches to carry out this work, the major audience involved were professionals, software developers who were working in the industry, and were the real practitioners of these methodologies, by taking advantage of their experiences we have considered their suggestions, ideas and experiences. Any software development project involves certain parameters: productivity, quality, cost and schedule. These project parameter are at the main theme of our study, based on it we have discovered that how agile methods may influence the software development industry

Crowdsourced Machine Learning Based Recommender for Software Design Patterns

Software technology has become an essential part of human lives today. The role of software Engineers in making this technology as success is very fundamental. In software Engineering, the toughest stage is to design software as there is no particular rule or formula to covert requirements into design representation. A designer designs software using skills, critical thinking ability and previous experience only. To make this process easy, the design patterns came into existence which are the solutions that can be used repetitively to solve design problems. There have been several pieces of research presented regarding design Patterns but it is hard to find research regarding how the patterns are perceived and used in industries today and what nature of application uses which specific patterns. This paper uses a crowdsourced approach to acquire the finest practices that are being used in industries today including which quality attributes are affected most by the implementation of these patterns and which patterns are suitable for what type of applications. It also uses a machine learning supervised algorithm (Matchbox Recommender) to predict suitable design pattern for different nature of applications