Abstract: One embodiment provides a method comprising mapping project attributes for past projects to a first parameter set associated with a first model that models distribution of event types of project events, and a second parameter set associated with a second model that models distribution of the time intervals of project events. Specifically, machine learning is applied to a set of historical data for the past projects to obtain a first and a second set of learned weights. The method further comprises predicting information relating to a next project event for an ongoing project by generating a first probability distribution for a set of possible event types for the next project event utilizing the first model, and, for each possible event type, generating a corresponding probability distribution for time intervals of occurrence of the possible event type utilizing the first model and the second model in a pipelined fashion.

Abstract: According to an aspect, document-based requirement identification and extraction includes parsing a set of documents and identifying relationships among parsed components of the documents and applying the parsed components and identified relationships to a meta-model that defines requirements. The requirements include a statement expressing a need and/or responsibility. A further aspect includes identifying candidate requirements and their candidate topics from results of the applying. For each of the identified candidate topics, a feature vector is built from the corresponding candidate requirements. A further aspect includes training the meta-model with the feature vectors, validating the meta-model, and classifying output of the validating to identify a subset of the candidate requirements, and corresponding topics expressed in the set of documents.

Abstract: In one embodiment, a computer-implemented method includes receiving a set of comments related to a project. One or more sentiment tags are extracted from the set of comments, where each sentiment tag includes an associated sentiment phrase. One or more sentiment scores are assigned to the one or more sentiment tags, with a sentiment score being assigned to each associated sentiment tag. The sentiment score for an associated sentiment tag is selected to represent the strength and polarity of the associated sentiment phrase. The one or more sentiment tags and the one or more sentiment scores are mapped, by a computer processor, to a first prediction for the project.