Abstract: A method, computer program product, and a system where a processor(s) obtains, a requirement comprising a structure defined by textual content. The processor(s) identifies content relevant to predefined label(s) in the structure, based on applying a natural language classification algorithm to the structure; each predefined label indicates an atomic function. The processor(s) generates via the natural language classification algorithm, an array of values for each label; each value corresponds to a level of confidence the natural language classification algorithm correctly identified the atomic function indicated by each predefined label, in the structure. The processor(s) ranks the values in the array of values by confidence level, pairing values with labels. The processor(s) evaluates the pairs, utilizing a linear regression, to identify a portion of the pairs relevant to the requirement which are above a relevance threshold.

Abstract: A requirements-traceability system extracts and classifies project requirements stored in a set of source documents. If a source document is unstructured, such as a natural-language word-processing file, the system uses a self-learning or cognitive natural-language tool to inferentially infer requirements in that document. Each requirement may be composed of more detailed sub-requirements in parent-child relationships. Requirements are reclassified into a standardized classification scheme and stored in a standardized hierarchical data structure in which each level corresponds to a requirement's relative degree of granularity. The tree is updated whenever requirements are revised, allowing users and downstream applications to bidirectionally trace each requirement's ancestors and descendants and to review and audit revision histories of the project's entire requirements hierarchy.

Abstract: A method, computer program product, and a system where a processor(s) obtains, a requirement comprising a structure defined by textual content. The processor(s) identifies content relevant to predefined label(s) in the structure, based on applying a natural language classification algorithm to the structure; each predefined label indicates an atomic function. The processor(s) generates via the natural language classification algorithm, an array of values for each label; each value corresponds to a level of confidence the natural language classification algorithm correctly identified the atomic function indicated by each predefined label, in the structure. The processor(s) ranks the values in the array of values by confidence level, pairing values with labels. The processor(s) evaluates the pairs, utilizing a linear regression, to identify a portion of the pairs relevant to the requirement which are above a relevance threshold.

Abstract: A requirements-traceability system extracts and classifies project requirements stored in a set of source documents. If a source document is unstructured, such as a natural-language word-processing file, the system uses a self-learning or cognitive natural-language tool to inferentially infer requirements in that document. Each requirement may be composed of more detailed sub-requirements in parent-child relationships. Requirements are reclassified into a standardized classification scheme and stored in a standardized hierarchical data structure in which each level corresponds to a requirement's relative degree of granularity. The tree is updated whenever requirements are revised, allowing users and downstream applications to bidirectionally trace each requirement's ancestors and descendants and to review and audit revision histories of the project's entire requirements hierarchy.

Abstract: Managing cross-project dependencies is provided. A list of Information Technology (IT) projects and their corresponding dependent IT projects is generated. A cross-project dependency risk chart having an X-axis based on IT project release dates and a Y-axis based on IT project positions is generated. Each IT project, along with its corresponding set of dependent IT projects from the list, is plotted on the X-axis of the cross-project dependency risk chart based on determined project release dates and on the Y-axis of the cross-project dependency risk chart based on determined project positions. A line representing cross-project dependency is inserted between each dependent IT project and an IT project that a respective dependent IT project depends on with an arrow pointing to the IT project that the respective dependent IT project depends on. The cross-project dependency risk chart is outputted on a display device.