Predicting systems performance through requirements quality attributes model

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Poor requirements definition can adversely impact system cost and performance for government acquisition programs. This can be mitigated by ensuring requirements statements are written in a clear and unambiguous manner that reflects high linguistic quality. This paper introduces a statistical model that uses requirements quality factors to predict system operational performance. This model is created using empirical data from current major acquisition programs within the federal government. Operational Requirements Documents and Operational Test Reports are the data sources, respectively, for the system requirements statements and the accompanying operational test results used for model development. A commercial-off-the-shelf requirements quality analysis tool is used to determine the linguistic quality metrics for the requirements statements. Following model construction, cross validation of the data is employed to confirm the predictive value of the model. In all, the results establish that requirements quality is indeed a predictive factor for end system operational performance; and the resulting statistical model can inform requirements decisions based on likelihood of successful operational performance. © 2014 the Authors. Published by Elsevier B.V.




Dargan, J. L., Campos-Nanez, E., Fomin, P., & Wasek, J. (2014). Predicting systems performance through requirements quality attributes model. In Procedia Computer Science (Vol. 28, pp. 347–353). Elsevier B.V.

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