Abstract: A graphical interface module may provide a set of graphical presentations comprising at least: a Likelihood of Delivery chart showing a probability distribution of predicted delivery dates; a Delivery Date Risk Trend chart showing how the completion time for the project predicted according to the Likelihood of Delivery chart has changed over time; and a Burndown chart that shows at least work-items of planned work for the project. Each of the Likelihood of Delivery chart, the Delivery Date Risk Trend chart, and the Burndown chart has a timeline axis.

Abstract: Techniques are provided for performing automated operations to determine maturity of a specified project. Information is received regarding each of a plurality of artifacts associated with the project, such as project documentation, source code repositories, and a tracked issue database for the project. A data sufficiency level associated with each provided artifact is determined, and each artifact is provided to one or more of multiple analysis engines. The analysis engines are executed to produce one or more weighted feature vectors for each of the artifacts associated with the specified project, and input to a prediction engine in order to provide a maturity rating for the project based on the weighted feature vectors.

Abstract: A computing method receives a labeled sample from an annotator. The method may determine a plurality of reference model risk scores for the first labeled sample, where each reference model risk score corresponds to an amount of risk associated with adding the first labeled sample to a respective reference model of a plurality of reference models. The method may determine an overall risk score for the first labeled sample based on the plurality of reference model risk scores. The method may further determine a probe for confirmation of the first labeled sample and a trust score for the annotator by sending the probe to one or more annotators. In response to determining a trust score for the annotator the method may add the labeled sample to a ground truth or reject the labeled sample.

Abstract: A Web application programming interfaces (API) recommendations technology for use in existing context (e.g., considering an already selected API) is disclosed. For example, recommendations for a “next” API, considering already “selected” APIs can be provided. Web API co-occurrence documents are derived for each Web API, based on modeling and previous usages with other web APIs. Web API co-occurrence topics and features are derived from the co-occurrence documents. Web APIs used together frequently can be considered as belonging to the same co-occurrence topic. Content about Web APIs can be associated with topics for later feature extraction. Features that can be extracted include: importance of topics, representative Web APIs in a topic (without being subject to bias due to frequent compositions in one topic), and descriptive words for a topic (if content about Web APIs was associated with topics).

Abstract: A Web application programming interfaces (API) recommendations technology for use in existing context (e.g., considering an already selected API) is disclosed. For example, recommendations for a “next” API, considering already “selected” APIs can be provided. Web API co-occurrence documents are derived for each Web API, based on modeling and previous usages with other web APIs. Web API co-occurrence topics and features are derived from the co-occurrence documents. Web APIs used together frequently can be considered as belonging to the same co-occurrence topic. Content about Web APIs can be associated with topics for later feature extraction. Features that can be extracted include: importance of topics, representative Web APIs in a topic (without being subject to bias due to frequent compositions in one topic), and descriptive words for a topic (if content about Web APIs was associated with topics).

Abstract: Techniques are provided for performing automated operations to determine maturity of a specified project. Information is received regarding each of a plurality of artifacts associated with the project, such as project documentation, source code repositories, and a tracked issue database for the project. A data sufficiency level associated with each provided artifact is determined, and each artifact is provided to one or more of multiple analysis engines. The analysis engines are executed to produce one or more weighted feature vectors for each of the artifacts associated with the specified project, and input to a prediction engine in order to provide a maturity rating for the project based on the weighted feature vectors.

Abstract: Embodiments include method, systems and computer program products for a path-sensitive contextual help system. In some embodiments, user actions are obtained from a user session of a user. A concrete user action trace is captured using the obtained user actions, wherein the concrete user action trace is a subset of user actions from the user session. An abstract user action trace is generated using the concrete user action trace. A help action corresponding to the abstract user action trace is identified and, in some embodiments, is presented to the user.

Abstract: Techniques that facilitate matrix factorization associated with graphics processing units are provided. In one example, a system includes a first graphics processing unit, a second graphics processing unit and a central processing unit. The first graphics processing unit processes a first data block of a data matrix associated with a matrix factorization system to generate first information for the matrix factorization system. The second graphics processing unit processes a first portion of a second data block of the data matrix separate from a second portion of the second data block to generate second information for the matrix factorization system. The central processing unit processes a machine learning model for the matrix factorization system based on at least the first information provided by the first graphics processing unit and the second information provided by the second graphics processing unit.

Abstract: Historic and current development data associated with the project may be gathered. A catalog of patterns, each pattern associated with a data measure and an analysis routine capable of detecting the pattern according to the data measure in a given data set may be obtained. A pattern describes a particular indication in the historical and development data, which arises one or more of, at a discrete point in time or over a period of time. The analysis routine may be applied to the historic and current development data. A notification may be issued responsive to identifying the pattern in the historic and current development data. The applying and the issuing may be performed for each pattern in the catalog of patterns.

Abstract: Incorporating user insights in predicting, diagnosing and remediating problems that threaten on-time delivery of a project may comprise gathering information. The information may be conveyed to one or more users. A user may be allowed to input a new value associated with the project, the new value assessed based on the information and representing an expert assessment of the information. The user may be allowed to also indicate a period of time the user considers the information to be applicable. The new value and the period of time may be incorporated into data used in a prediction algorithm that predicts probability distribution of completion time of the project as the project is progressing.

Abstract: A method, system, and/or computer program product manages the lifecycle of trained models used to deliver cognitive services. One or more processors obtain and deploy a cognitive engine that utilizes artificial intelligence (AI), machine learning, and/or similar algorithms. One or more processors obtain and deploy a version of a trained model that includes data that supports cognitive operations of the cognitive engine within a cognitive service. In response to changes to the input used to produce the trained model, one or more processors obtain and deploy a subsequent version of the trained model in support of the cognitive service.

Abstract: A user may be allowed to specify a change in one or more parameter data associated with the project, the one or more parameter data used previously to compute a probability distribution of completion time of the project. The probability distribution of completion time of the project may be recomputed based on the change. The recomputed probability distribution of the completion time of the project may be presented. An option to save the recomputed probability distribution may be provided. An option may be provided to specify another change in one or more parameter data associated with the project and repeat the recomputing and the presenting procedures based on another change in one or more parameter data associated with the project.

Abstract: A graphical interface module may provide a set of graphical presentations comprising at least: a Likelihood of Delivery chart showing a probability distribution of predicted delivery dates; a Delivery Date Risk Trend chart showing how the completion time for the project predicted according to the Likelihood of Delivery chart has changed over time; and a Burndown chart that shows at least work-items of planned work for the project. Each of the Likelihood of Delivery chart, the Delivery Date Risk Trend chart, and the Burndown chart has a timeline axis.

Abstract: A graphical interface module may provide a set of graphical presentations comprising at least: a Likelihood of Delivery chart showing a probability distribution of predicted delivery dates; a Delivery Date Risk Trend chart showing how the completion time for the project predicted according to the Likelihood of Delivery chart has changed over time; and a Burndown chart that shows at least work-items of planned work for the project. Each of the Likelihood of Delivery chart, the Delivery Date Risk Trend chart, and the Burndown chart has a timeline axis.

Abstract: A task effort estimator may determine a probability distribution of an estimated effort needed to complete unfinished tasks in a project based on one or more of a set of completed tasks belonging to a project and attributes associated with the completed tasks belonging to the project, a set of completed tasks not belonging to the project and attributes associated with the completed tasks not belonging to the project, or the combination of both. A project completion predictor may determine a probability distribution of completion time for the project based on the probability distribution of an estimated effort needed to complete the unfinished tasks in the project, and one or more resource and scheduling constraints associated with the project.

Abstract: A method for projecting the progress of a project, the project including work items, the method including: obtaining starting state transition probabilities for the work items in a first time interval; obtaining starting populations of the work items, wherein the starting populations of the work items include states of the work items at the beginning of the first time interval; determining expected distributions for the work item states at the end of the first time interval by using the starting state transition probabilities and the starting populations; identifying actual states for the work items at the end of the first time interval; determining actual state transition rates of the work items for the first time interval by using the starting populations and the actual states; and determining expected future state transition probabilities for the work items by using the starting state transition probabilities and the actual state transition rates.

Abstract: A system and method to automatically and continuously update the urgency rating of a backlog item associated with a technical debt item in a software development project are described. The technical debt represents potential additional work or rework associated with current code. The method includes a user defining a plurality of components that comprise the software development project. The method also includes a processor associating the technical debt with one or more components of the plurality of components, and the processor updating the urgency of the backlog item associated with the technical debt according to a function based on a development event associated with at least one of the one or more components.

Abstract: A system and method to automatically and continuously update the urgency rating of a backlog item associated with a technical debt item in a software development project are described. The technical debt represents potential additional work or rework associated with current code. The method includes a user defining a plurality of components that comprise the software development project. The method also includes a processor associating the technical debt with one or more components of the plurality of components, and the processor updating the urgency of the backlog item associated with the technical debt according to a function based on a development event associated with at least one of the one or more components.

Abstract: A system and method to automatically and continuously update the urgency rating of a backlog item associated with a technical debt item in a software development project are described. The technical debt represents potential additional work or rework associated with current code. The method includes a user defining a plurality of components that comprise the software development project. The method also includes a processor associating the technical debt with one or more components of the plurality of components, and the processor updating the urgency of the backlog item associated with the technical debt according to a function based on a development event associated with at least one of the one or more components.

Abstract: A system and method to automatically and continuously update the urgency rating of a backlog item associated with a technical debt item in a software development project are described. The technical debt represents potential additional work or rework associated with current code. The method includes a user defining a plurality of components that comprise the software development project. The method also includes a processor associating the technical debt with one or more components of the plurality of components, and the processor updating the urgency of the backlog item associated with the technical debt according to a function based on a development event associated with at least one of the one or more components.