Abstract: Examples of techniques for the real-time capture and translation of human thoughts and ideas into structured patterns are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method may include capturing, by a processing device, unstructured data. The method may also include extracting key terms from the unstructured data. Additionally, the method may include assigning an attribute to at least one of the key terms. The method may further include generating, by the processing device, a structured pattern based on the key terms and the attributes.

Abstract: Examples of techniques for the real-time capture and translation of human thoughts and ideas into structured patterns are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method may include capturing, by a processing device, unstructured data. The method may also include extracting key terms from the unstructured data. Additionally, the method may include assigning an attribute to at least one of the key terms. The method may further include generating, by the processing device, a structured pattern based on the key terms and the attributes.

Abstract: Systems and computer program products for providing usability predictions are set forth herein. A system includes a test case collector for obtaining test cases for analyzing usability of a graphical user interface of a given software application, the test case collector having at least a memory storing the test cases. The system further includes a human performance model generator for automatically generating a human performance model based on the test cases. The system also includes a usability predictor for generating the usability predictions based on the human performance model. The test case collector automatically generates at least some of the test cases from an analysis of the given software application.

Abstract: Methods of providing usability predictions are set forth herein. A method includes obtaining test cases for analyzing usability of a graphical user interface of a given software application. The method further includes automatically generating, using a processor, a human performance model based on the test cases. The method also includes generating the usability predictions based on the human performance model. At least some of the test cases are generated automatically from an analysis of the given software application.

Abstract: Systems and computer program products for providing usability predictions are set forth herein. A system includes a test case collector for obtaining test cases for analyzing usability of a graphical user interface of a given software application, the test case collector having at least a memory storing the test cases. The system further includes a human performance model generator for automatically generating a human performance model based on the test cases. The system also includes a usability predictor for generating the usability predictions based on the human performance model. The test case collector automatically generates at least some of the test cases from an analysis of the given software application.

Abstract: Methods of providing usability predictions are set forth herein. A method includes obtaining test cases for analyzing usability of a graphical user interface of a given software application. The method further includes automatically generating, using a processor, a human performance model based on the test cases. The method also includes generating the usability predictions based on the human performance model. At least some of the test cases are generated automatically from an analysis of the given software application.

Abstract: A system for optimizing at least one of a design, production, or testing process of a product in a mass manufacturing process includes: a central processing unit; a network interface operatively connected to the central processing unit; a storage device; a memory including logic for execution by the central processing unit, wherein the logic includes: a server handler made up of a client applet and a client interface servlet which are configured for enabling authorized end-user communication; an error data collection handler configured for gathering error data; an error data classification handler; an analysis handler; a suggested actions report handler; and the system further includes a server database configured for storing, modifying, and deleting data.

Abstract: Systems and methods are provided to produce natural language interpretations of analytic operations in an automated manner, by associating with each analytic procedure a set of parameters that determine the interpretation of the resulting analysis.

Abstract: A computer-implemented method of optimizing at least one of a design, production and testing process in a mass manufacturing process includes steps of: collecting error data relating to a product; classifying the error data into categories of symptoms; mapping the symptom to a revealing condition of the product; mapping the revealing condition to a test type; mapping a scope of a fix to phases of error injection mapping; and recommending modifications to an end user for at least one of the design, production, delivery, and testing process based on the scope of the fix.

Abstract: A method and system estimates future software support requirements based on statistical models of previous observations of support requests, either for the same product or for a different product having features previously identified as correlated with features of a new product. The estimates include an estimated volume of support requests and an estimated type of support requests. The estimated types include the activity occurring at the time of the failure, an identifier as to whether a defect in the software was previously known, and the like. The estimates are used to estimate and allocate support resources prior to support requests being received, and prior to a software product being released.

Abstract: A computer-implemented method of optimizing at least one of a design, production and testing process in a mass manufacturing process includes steps of: collecting error data relating to a product; classifying the error data into categories of symptoms; mapping the symptom to a revealing condition of the product; mapping the revealing condition to a test type; mapping a scope of a fix to phases of error injection mapping; and recommending modifications to an end user for at least one of the design, production, delivery, and testing process based on the scope of the fix.

Abstract: A method and system estimates future software support requirements based on statistical models of previous observations of support requests, either for the same product or for a different product having features previously identified as correlated with features of a new product. The estimates include an estimated volume of support requests and an estimated type of support requests. The estimated types include the activity occurring at the time of the failure, an identifier as to whether a defect in the software was previously known, and the like. The estimates are used to estimate and allocate support resources prior to support requests being received, and prior to a software product being released.

Abstract: A computer-implemented method of optimizing a design of a product in a mass manufacturing process includes steps of: collecting error data relating to a product; classifying the error data into categories of errors to provide classifier error data; analyzing relationships among the classified error data; producing an analysis report; and recommending modifications to an end user for the design of the product.

Abstract: A method, information processing system and computer readable medium for assessing risk in a software development project. The method includes receiving software project data for assessing risks in a software development project and identifying a plurality of focus areas for analysis. The method further includes defining a set of analysis tasks for each focus area and creating one or more rule sets for each analysis task, each rule set comprising one or more rules including software project data. The method further includes evaluating each rule set against the software project data summarizing results of evaluating each rule set corresponding to an analysis task. The method further includes providing a risk assessment value for each analysis task using the results of the summarizing element.

Abstract: Systems and methods are provided to produce natural language interpretations of analytic operations in an automated manner, by associating with each analytic procedure a set of parameters that determine the interpretation of the resulting analysis.

Abstract: A method to create an instance of a defect-based production and testing process analysis machine (DPTPAM) provides continual process improvement based on foundational questions and classified defect data. The method includes the following steps: obtaining domain specific questions; developing a domain specific classification scheme that supports the answering of the foundational and domain specific questions; determining a method of using the domain specific classification scheme to answer both the foundational and domain specific questions; and creating a domain specific DPTPAM instance embodying the domain specific classification scheme and the method of answering the foundational and domain specific questions. The method can be implemented with a machine and a computer readable medium comprising logic for performing the method.

Abstract: A system, method and computer program product for requirements management. Particularly, a requirements management scheme performs at least the following functions: 1) it guides a team through the requirements process and provides a structure for collecting “requests”. By using the scheme, team members are prompted to think about and record relevant information that will help clarify and complete requests; and, 2) the scheme allows users to use the attributes throughout the development process to identify risk and make improvements to their process. Many of these attributes can be refined during the development phase or even later. Besides helping team members achieve continuous improvement in their requirements process by providing a set of data and metrics for collection and assessment, the scheme enables an organization to effectively manage their requirements as well as manage changes to those requirements.

Abstract: A method and system estimates future software support requirements based on statistical models of previous observations of support requests, either for the same product or for a different product having features previously identified as correlated with features of a new product. The estimates include an estimated volume of support requests and an estimated type of support requests. The estimated types include the activity occurring at the time of the failure, an identifier as to whether a defect in the software was previously known, and the like. The estimates are used to estimate and allocate support resources prior to support requests being received, and prior to a software product being released.

Abstract: A system for optimizing at least one of a design, production, or testing process of a product in a mass manufacturing process includes: a central processing unit; a network interface operatively connected to the central processing unit; a storage device; a memory including logic for execution by the central processing unit, wherein the logic includes: a server handler made up of a client applet and a client interface servlet which are configured for enabling authorized end-user communication; an error data collection handler configured for gathering error data; an error data classification handler; an analysis handler; a suggested actions report handler; and the system further includes a server database configured for storing, modifying, and deleting data.

Abstract: A computer-implemented method of optimizing a design of a product in a mass manufacturing process includes steps of: collecting error data relating to a product; classifying the error data into categories of errors to provide classifier error data; analyzing relationships among the classified error data; producing an analysis report; and recommending modifications to an end user for the design of the product.