Abstract: Examples for automatically evaluating a software project requirement are disclosed. In an example, a neural word vector corresponding to a requirement file is generated and the neural word vector based on a score based vector is updated. An output vector comprising a conditional probability distribution of a plurality of answers associated with a plurality of questions identified from the updated neural word vector is generated. Further, a set of input parameters associated with at least one of the software project and the requirement is obtained. Based on the output vector and the set of input parameters, an effort required for completing the requirement may be estimated. A validation score associated with the requirement based on the output vector and a plurality of validation and classification parameters may be determined.

Abstract: Examples for automatically evaluating a software project requirement are disclosed. In an example, a neural word vector corresponding to a requirement file is generated and the neural word vector based on a score based vector is updated. An output vector comprising a conditional probability distribution of a plurality of answers associated with a plurality of questions identified from the updated neural word vector is generated. Further, a set of input parameters associated with at least one of the software project and the requirement is obtained. Based on the output vector and the set of input parameters, an effort required for completing the requirement may be estimated. A validation score associated with the requirement based on the output vector and a plurality of validation and classification parameters may be determined.