Abstract: Aspects of the invention include computer-implemented methods, computing systems and computer program products for calculating an active time on a software application with respect to a first work item. A computer-implemented method includes retrieving activity data from a software development application and a plurality of input signals generated by a peripheral device. The method further includes determining a causal relationship between the input data and any events described by the activity data. In response to a determination of a causal relationship, a first time interval is calculated between an initial signal and a final signal of the plurality of input signals. The first time interval is compared to an estimated time interval. A schedule of a second work item is determined based on the comparison, wherein the first time interval relates to a first work item.

Abstract: Aspects of the invention include computer-implemented methods, computing systems and computer program products for calculating an active time on a software application with respect to a first work item. A computer-implemented method includes retrieving activity data from a software development application and a plurality of input signals generated by a peripheral device. The method further includes determining a causal relationship between the input data and any events described by the activity data. In response to a determination of a causal relationship, a first time interval is calculated between an initial signal and a final signal of the plurality of input signals. The first time interval is compared to an estimated time interval. A schedule of a second work item is determined based on the comparison, wherein the first time interval relates to a first work item.

Abstract: A method, system, and computer program product for amorphous data preparation for intelligent data source selection in application development are provided in the illustrative embodiments. A set of keywords is selected from a code portion of a source code. Using a subset of the set of keywords, an expected circumstance is determined, the expected circumstance being a circumstance relevant to a purpose of the source code. From a set of data sources, a data source is identified, wherein the data source provides information usable in the expected circumstance. A manner of accessing the information from the data source is identified. Pseudo-code is constructed to implement the manner of accessing the information from the data source. A weight is assigned to the data source based upon a type of input used for selecting the pseudo-code, wherein the selecting uses the pseudo-code in the source code.

Abstract: A method, system, and computer program product for amorphous data preparation for intelligent data source selection in application development are provided in the illustrative embodiments. A set of keywords is selected from a code portion of a source code. Using a subset of the set of keywords, an expected circumstance is determined, the expected circumstance being a circumstance relevant to a purpose of the source code. From a set of data sources, a data source is identified, wherein the data source provides information usable in the expected circumstance. A manner of accessing the information from the data source is identified. Pseudo-code is constructed to implement the manner of accessing the information from the data source. A weight is assigned to the data source based upon a type of input used for selecting the pseudo-code, wherein the selecting uses the pseudo-code in the source code.

Abstract: A common metrics manager analyzes customer feedback responses, and assigns a weighted priority to each feedback response. The weighted priority corresponds to a particular feedback response's impact on business goals, such as customer satisfaction. The common metrics manager uses weighted priority feedback responses to generate and rank a set of common metrics. The common metrics manager uses the common metrics to generate phase goals for each phase of a product's lifecycle. The number of phase goals for each product phase corresponds to the ranking of each particular common metric. Once a product ships to a customer which is developed using the common metrics, the customer sends feedback responses to the common metrics manager which the common metrics manager uses to generate new common metrics for use with a new product lifecycle.