Predicting Requirements Volatility: An Industry Case Study

Software requirements are exposed to many changes during their software development life-cycle. These changes namely additions, modifications or deletions are defined as requirements volatility. Prior requirement volatility prediction studies utilize different requirement volatility measures. In this study we predict number of changes per software requirement as requirement volatility for a large scale safety-critical avionics project in ASELSAN. We employ a comprehensive metric set to explain requirements volatility: requirement quality measures, project specific factors and requirement interdependencies. Predictive models are created through combining input metric sets with machine learners. Success of models in predicting requirement changes, the best performing input metric combinations, the best performing machine learners and success of models in predicting highly-volatile requirements are evaluated in this study. The best prediction results are obtained with the model employing quality metrics, project specific metrics, network metrics altogether with k-nearest neighbour machine learner (MMRE=0.366). Also the best model correctly identifies 63.2% of highly volatile requirements which are exposed to 80% of the total requirement changes. Our study results are encouraging in terms of creating requirement change prediction tools to prevent requirement volatility risks prior to the requirement review process.