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Studying the quality requirements (aka Non-Functional Requirements (NFR)) ofa system is crucial in Requirements Engineering. Many software projects failbecause of neglecting or failing to incorporate the NFR during the softwarelife development cycle. This paper focuses on analyzing the importance of thequality requirements attributes in software effort estimation models based onthe Desharnais dataset. The Desharnais dataset is a collection of eighty onesoftware projects of twelve attributes developed by a Canadian software house.The analysis includes studying the influence of each of the qualityrequirements attributes, as well as the influence of all quality requirementsattributes combined when calculating software effort using regression andArtificial Neural Network (ANN) models. The evaluation criteria used in thisinvestigation include the Mean of the Magnitude of Relative Error (MMRE), thePrediction Level (PRED), Root Mean Squared Error (RMSE), Mean Error and theCoefficient of determination (R2). Results show that the quality attributeLanguage is the most statistically significant when calculating softwareeffort. Moreover, if all quality requirements attributes are eliminated in thetraining stage and software effort is predicted based on software size only,the value of the error (MMRE) is doubled.

Analyzing the Non-Functional Requirements in the Desharnais Dataset for Software Effort Estimation