Abstract: Embodiments include a system for release cycle optimization; the system includes a processor configured to perform a method. The method includes accessing, by a processor, historical data relating to a plurality of software version each having a plurality of attributes; selecting a subset of attributes from the plurality of attributes; receiving a set of data values for each of the subset of attributes from the plurality of attributes; performing one or more simulations of a software development cycle utilizing the set of data values; and obtaining a set of results from the one or more simulations comprising a plurality of predicted field defects values corresponding to each of the set of data values.

Abstract: The disclosed herein relates to a method for failure rate prediction of a feature of a system under development. The method is executed by a processor coupled to a memory. The method includes defining a feature state of the feature during a predetermined time interval, the predetermined time interval being associated with a development stage of the system. The method also includes assigning a first defect class value to the feature for the predetermined time interval, the first defect class value configured to indicate a first condition and selecting, when a defect is reported for the feature, a second defect class value indicating a second condition, the second condition being associated with a higher failure rate than the first condition. The method can be embodied in system and a computer program product.

Abstract: A method for performing software error detection and prediction. The method includes identifying a plurality of software components in a computer software product. For each of the software components of the plurality of software components, the risk-relevant historical data pertaining to the respective software component is measured, then classified into at least a set of risk-increasing data and a set of risk-decreasing data. The set of risk-increasing data and the set of risk-decreasing data are then normalized, and a failure risk value for the respective software component is calculated by subtracting a weighted sum of the normalized values for the risk-decreasing data from a weighted sum of the normalized values for the risk-increasing data.

Abstract: A method and a related system for reconciling data sets from different data sources includes comparing the data sets using static data from the data sets, determining an uncertainty factor whether to reconcile the data sets based on an incomplete match of the static data of data sets, and performing a dynamic matching if the uncertainty factor exceeds a predefined uncertainty threshold value. The dynamic data matching comprises the following: comparing dynamic communication data relating to each of the data sets, determining a certainty factor based on the comparison of the dynamic communication data, and reconciling those data sets whose certainty factor exceeds a predefined certainty threshold value.

Abstract: Embodiments include a system for release cycle optimization; the system includes a processor configured to perform a method. The method includes accessing, by a processor, historical data relating to a plurality of software version each having a plurality of attributes; selecting a subset of attributes from the plurality of attributes; receiving a set of data values for each of the subset of attributes from the plurality of attributes; performing one or more simulations of a software development cycle utilizing the set of data values; and obtaining a set of results from the one or more simulations comprising a plurality of predicted field defects values corresponding to each of the set of data values.

Abstract: A method for performing software error detection and prediction. The method includes identifying a plurality of software components in a computer software product. For each of the software components of the plurality of software components, the risk-relevant historical data pertaining to the respective software component is measured, then classified into at least a set of risk-increasing data and a set of risk-decreasing data. The set of risk-increasing data and the set of risk-decreasing data are then normalized, and a failure risk value for the respective software component is calculated by subtracting a weighted sum of the normalized values for the risk-decreasing data from a weighted sum of the normalized values for the risk-increasing data.