Abstract
A model of software complexity and reliability is developed. It uses an evolutionary process to transition from one software system to the next, while complexity metrics are used to predict the reliability for each system. Our approach is experimental, using data pertinent to the NASA satellite systems application environment. We do not use sophisticated mathematical models that may have little relevance for the application environment. Rather, we tailor our approach to the software characteristics of the software to yield important defect-related predictors of quality. Systems are tested until the software passes defect presence criteria and is released. Testing criteria are based on defect count, defect density, and testing efficiency predictions exceeding specified thresholds. In addition, another type of testing efficiency -- a directed graph representing the complexity of the software and defects embedded in the code -- is used to evaluate the efficiency of defect detection in NASA satellite system software. Complexity metrics were found to be good predictors of defects and testing efficiency in this evolutionary process.
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