Identification of Least Risk Path using GA-SVM for the Software Project Management

Abstract

Risk management is an important part of the development cycles for high quality applications. Most specific threats are incidents that may adversely affect the plan or organizational climate growth. The major risk factors contains time, budget and resources can affect adversely by events. Important considerations such as plan, time and cost are generally impacted. Essentially, risk assessment includes recognizing, assessing, preparing and monitoring incidents that affect the atmosphere of the project. Risk is the danger of volatility, lack of knowledge regarding events, activities and lack of appropriate technologies for managing measures and activities. Therefore, both exogenous and endogenous influences contribute in the venture risks and uncertainties. The high task failure rates due to poor planning of project which can limit the teams and future wealth creation, while project managers should allow for the plan being to anticipate potential risks when preparing their project achievements based on their own past experiences. This paper addresses the Supervised Learning mechanism with multi-label Support Vector Classifier (SVC) to predict the project risks and apply Genetic algorithm for providing avoidance action as recommendation.