In an organization operating in the bancassurance sector we identified a low-risk IT subportfolio of 84 IT projects comprising together 16,500 function points, each project varying in size and duration, for which we were able to quantify its requirements volatility. This representative portfolio stems from a much larger portfolio of IT projects. We calculated the volatility from the function point countings that were available to us. These figures were aggregated into a requirements volatility benchmark. We found that maximum requirements volatility rates depend on size and duration, which refutes currently known industrial averages. For instance, a monthly growth rate of 5% is considered a critical failure factor, but in our low-risk portfolio we found more than 21% of successful projects with a volatility larger than 5%. We proposed a mathematical model taking size and duration into account that provides a maximum healthy volatility rate that is more in line with the reality of low-risk IT portfolios. Based on the model, we proposed a tolerance factor expressing the maximal volatility tolerance for a project or portfolio. For a low-risk portfolio its empirically found tolerance is apparently acceptable, and values exceeding this tolerance are used to trigger IT decision makers. We derived two volatility ratios from this model, the π-ratio and the ρ-ratio. These ratios express how close the volatility of a project has approached the danger zone when requirements volatility reaches a critical failure rate. The volatility data of a governmental IT portfolio were juxtaposed to our bancassurance benchmark, immediately exposing a problematic project, which was corroborated by its actual failure. When function points are less common, e.g. in the embedded industry, we used daily source code size measures and illustrated how to govern the volatility of a software product line of a hardware manufacturer. With the three real-world portfolios we illustrated that our results serve the purpose of an early warning system for projects that are bound to fail due to excessive volatility. Moreover, we developed essential requirements volatility metrics that belong on an IT governance dashboard and presented such a volatility dashboard.
Researchers proposed a mathematical model to identify the requirements volatility; they found that the maximum changing rates depend on source code size and duration of requirements volatility, where the volatility is calculated from the function points and lines of source code in the software product REF .
Quantifying Requirements Volatility Effects
Sci. Comput. Program.
Sci. Comput. Program.
Computer Science