Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Requirements volatility is an important risk factor for software projects. Software measures can help in quantifying and predicting this risk. ,  present the results of a correlational study with the goal of predicting requirements volatility for a medium size software project. Based on the data collected from two industrial software projects for four measures of size of requirements (number of actors, use cases, words, and lines),  have evaluated prediction models for requirements volatility. These models can help project managers to estimate the volatility of requirements and minimize the risks caused by volatile requirements, like schedule and cost overruns. In cross systems validation our best model shod a mean magnitude of relative error (MMRE) of 0.25, which can be considered reliable. In an earlier study,  shod that decisions solely based on developers perception of requirements volatility are, instead unreliable. Predictions models, like the ones presented here, can therefore help taking more reliable decisions.
['Annabella Loconsole']
I am writing a research paper on a idea A correlational study on four measures of requirements volatility
[{"from": "human", "value": "I am writing a research paper on a idea A correlational study on four measures of requirements volatility"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Requirements volatility is an important risk factor for software projects. Software measures can help in quantifying and predicting this risk. ,  present the results of a correlational study with the goal of predicting requirements volatility for a medium size software project. Based on the data collected from two industrial software projects for four measures of size of requirements (number of actors, use cases, words, and lines),  have evaluated prediction models for requirements volatility. These models can help project managers to estimate the volatility of requirements and minimize the risks caused by volatile requirements, like schedule and cost overruns. In cross systems validation our best model shod a mean magnitude of relative error (MMRE) of 0.25, which can be considered reliable. In an earlier study,  shod that decisions solely based on developers perception of requirements volatility are, instead unreliable. Predictions models, like the ones presented here, can therefore help taking more reliable decisions."}]