Measuring and predicting software requirements volatility for large-scale safety-critical avionics projects

Abstract

During the software development life cycle, software requirements are subjected to many changes despite the recent developments in software engineering. These modifications, additions, or removals are referred to as requirements volatility. Constantly changing requirements affect cost of the project, the project schedule and the quality of the product. In the worst case projects fail or partially completed due to requirements volatility. Various requirement volatility measures have been used in previous requirement volatility prediction studies and industrial volatility measurement practices. A very big safety-critical avionics software project with thousands of software requirements from ASELSAN company is employed to forecast the number of changes for each software requirement as requirements volatility in this thesis. To explain requirements volatility, we use a complete collection of the following metrics: requirement textual metrics, project-specific characteristics, and interdependencies between software requirements. Requirement textual metrics in this thesis are chosen from two requirements quality analyzer tools that are used in the literature. Project-specific metrics are created by focusing on safety-critical avionics project features one by one and including the ones that would give information on requirements volatility. Traceability links between system and software requirements are used to create a network graph, and network centrality metrics are created for software requirements with regard to this graph. Requirement volatility prediction is done by employing several machine learning techniques which are utilized by base studies: k-nearest neighbor regression algorithm, linear regression, random forest regression and support vector regression. Combining input metric groups with machine learning algorithms, 28 predictive models are created in this study. This research evaluates the performance of proposed models in predicting software requirement change proneness, outperforming input metric combinations, outperforming machine learning techniques, and the success of proposed models in labeling highly volatile software requirements. The model that combines requirement textual measurements, avionics project features, and network centrality metrics with a k-nearest neighbor machine learner produces the best prediction results (MMRE=0.366). Furthermore, the best predictive model properly labels 63.2 percent of highly volatile software requirements that are subject to 80 percent of total software requirement changes. The findings of our research are positive in terms of developing automated requirement change analyzer tools to minimize requirement volatility concerns in early development phases.