More than half a million Project Managers execute about a million software projects every year all around the world, turning out software worth more than $600 billion. A considerable part of the software projects, as one of the studies, Standish Group Report shows nearly three-quarter of over all as challenged or cancelled, goes in vain due to unhandled damaging project risks associated with them.
Most of them are unidentified until too late to manage them and the results of these projects have been nothing but desperate cancellation. Henceforth, it goes without saying the need of risk identification which is a core process of risk management, the most crucial management phase of all project management cycles.
Software risk is a measure of probability for gain or loss by unacceptable result that may happen in software project, software process, or software product.
Each software system is distinctive with its own particular set of risks. There are many software risks but fewer consequences that we care to avoid. Mostly, it is hard to avoid all kinds of risks in a project. This is why we often care about a few highly impacting risks like potential cost, schedule, technical consequences in any project. These software risks could avert a software project from meeting its cost, schedule, and technical objectives. Software risk is hazardous because it can prevent software project success.
Software risk identification is the initial process in the risk assessment which is a part of risk management. Without proper risk identification, it is tough to control and manage the risk that is associated with the software project.
One of our current works is an innovative endeavor to solve the software project risk identification problem with one of Graph Mining approaches. This procedure, in turn, simplifies the software risk identification and consolidates attempts to identify software risks more vivid and robust fashion.
Further, the procedure enhances human ability and approach to identify software risks by analytic capabilities.
The software risk identification process begins with the creation of software project risk scenarios. These scenarios are at first, made by collecting essential set of data or nodes as input to the formation of the Graphs.
Further analysis of existing graphs with data visualization and interpretation leads towards concrete results. Methodology including a number of visualization techniques, for instance, Link Mining, Node Mining, ubiquotous data mining, etc., may further be choosen as per suitability of scenarios.
Flexibility and handy applicabilities are key attractive features, in addition to wider analytic investigation of software risks. Further work is due to come in coming future.