Abstract: The present disclosure relates to a method using a database engine for migrating data records from a source database to a target database, where said data records are arranged in a sorted source table of the source database in accordance with a clustered-base-table order following the Hilbert-Filling-Curve algorithm. The method comprises configuring the database engine for storing the CBT order of each data record of the source table in a reference table to said source table or a in a column of said source table. A reading step may be performed to read said data records from said source table, wherein in said reading step said stored CBT order is used. The read data and the stored CBT order may be transferred to the target database in accordance with the stored CBT order. The transferred data may be written at the target database in accordance with the CBT order.

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 present disclosure relates to a method using a database engine for migrating data records from a source database to a target database, where said data records are arranged in a sorted source table of the source database in accordance with a clustered-base-table order following the Hilbert-Filling-Curve algorithm. The method comprises configuring the database engine for storing the CBT order of each data record of the source table in a reference table to said source table or a in a column of said source table. A reading step may be performed to read said data records from said source table, wherein in said reading step said stored CBT order is used. The read data and the stored CBT order may be transferred to the target database in accordance with the stored CBT order. The transferred data may be written at the target database in accordance with the CBT order.

Abstract: A current data set with multiple records is fed into a data analysis model. The current data set is limited to data occurring in a current time window with a predetermined window size. The model is run on the current data set and a current data prediction result is generated. Limited historical data sets having multiple records are selected. Each record has values for several features. Each historical data set is limited to data occurring in a historical time window having the window size. A historical class label distribution is determined for the historical data sets and an upper and lower control limit are determined for the historical data sets using the historical class label distribution. A current class label distribution having a mean value is determined for the current prediction result. An alert is provided when the mean value is not between the upper and lower control limit.

Abstract: Embodiments include a method for generation of test scenarios based on risk analysis. The method includes receiving a first set of code test scenarios, the first set of code test scenarios configured to test one or more first code components, the first code components each including first code attributes. The method also includes determining a probability of finding defects in the first code components based on the first code attributes. The method also includes generating a second set of code test scenarios based on the determining the probability of finding defects in the first code components based on the first code attributes, where the second set of one or more test scenarios is configured to test one or more second code components based on the second code attributes.

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 and system for communicating supplemental data for nodes of a graph. A visual representation of the graph is divided into portions of a computer screen. Each portion is a rectangle corresponding to a respective node of the graph. Dividing the visual representation of the graph into portions includes determining a number of pixels in rectangles displayed on the computer screen along with the graph. Supplemental data for each respective node, together with an offset distance between a reference point of each respective node and a reference point of the supplemental data for each respective node, is encoded steganographically. Fixed length attributes related to the respective node are prioritized. The attributes are encoded steganographically into the rectangle corresponding to the respective node, starting with a highest priority attribute of the fixed length attributes and continuing sequentially according to a priority of the remaining attributes of the fixed length attributes.

Abstract: A method and system for communicating supplemental data for nodes of a graph. A visual representation of the graph is divided into portions of a computer screen. Each portion is a rectangle corresponding to a respective node of the graph. Dividing the visual representation of the graph into portions includes determining a number of pixels in rectangles displayed on the computer screen along with the graph. Supplemental data for each respective node, together with an offset distance between a reference point of each respective node and a reference point of the supplemental data for each respective node, is encoded steganographically. Fixed length attributes related to the respective node are prioritized. The attributes are encoded steganographically into the rectangle corresponding to the respective node, starting with a highest priority attribute of the fixed length attributes and continuing sequentially according to a priority of the remaining attributes of the fixed length attributes.

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 system for communicating supplemental data for nodes of a graph representing a system. A visual representation of the graph is divided into a portions, each portion of the visual representation of the graph being a rectangle corresponding to a respective node of the graph, wherein the visual representation is an area of a computer screen displaying the graph. Supplemental data for each respective node, together with an offset distance between a reference point of each respective node and a reference point of the supplemental data for each respective node, is encoded steganographically into the portion of the graphical representation corresponding to each respective node.

Abstract: A current data set with multiple records is fed into a data analysis model. The current data set is limited to data occurring in a current time window with a predetermined window size. The model is run on the current data set and a current data prediction result is generated. Limited historical data sets having multiple records are selected. Each record has values for several features. Each historical data set is limited to data occurring in a historical time window having the window size. A historical class label distribution is determined for the historical data sets and an upper and lower control limit are determined for the historical data sets using the historical class label distribution. A current class label distribution having a mean value is determined for the current prediction result. An alert is provided when the mean value is not between the upper and lower control limit.

Abstract: Embodiments include a method for generation of test scenarios based on risk analysis. The method includes receiving a first set of code test scenarios, the first set of code test scenarios configured to test one or more first code components, the first code components each including first code attributes. The method also includes determining a probability of finding defects in the first code components based on the first code attributes. The method also includes generating a second set of code test scenarios based on the determining the probability of finding defects in the first code components based on the first code attributes, where the second set of one or more test scenarios is configured to test one or more second code components based on the second code attributes.

Abstract: Embodiments include a method for generation of test scenarios based on risk analysis. The method includes receiving a first set of code test scenarios, the first set of code test scenarios configured to test one or more first code components, the first code components each including first code attributes. The method also includes determining a probability of finding defects in the first code components based on the first code attributes. The method also includes generating a second set of code test scenarios based on the determining the probability of finding defects in the first code components based on the first code attributes, where the second set of one or more test scenarios is configured to test one or more second code components based on the second code attributes.

Abstract: A method and system for communicating supplemental data for nodes of a graph representing a system. A visual representation of the graph is divided into a portions, each portion of the visual representation of the graph being a rectangle corresponding to a respective node of the graph, wherein the visual representation is an area of a computer screen displaying the graph. Supplemental data for each respective node, together with an offset distance between a reference point of each respective node and a reference point of the supplemental data for each respective node, is encoded steganographically into the portion of the graphical representation corresponding to each respective node.

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: The described herein related to a method executable by a processor of a computing environment, where the processor is communicatively coupled to a database of the computing environment. The method includes identifying a feature set from a plurality of incident tickets available on the database and selecting a first subset of the feature set as a learning set. The method further includes generating information from the learning set, where the information indicates unique changes to the computing environment corresponding to a failure. The method further includes selecting a second subset of the feature set as a classification set, validating the information via the classification set to determine whether the learning set is rendering correct values; and sourcing the failure using the validated information based on determining that the learning set is rendering correct values.

Abstract: Embodiments include a method for generation of test scenarios based on risk analysis. The method includes receiving a first set of code test scenarios, the first set of code test scenarios configured to test one or more first code components, the first code components each including first code attributes. The method also includes determining a probability of finding defects in the first code components based on the first code attributes. The method also includes generating a second set of code test scenarios based on the determining the probability of finding defects in the first code components based on the first code attributes, where the second set of one or more test scenarios is configured to test one or more second code components based on the second code attributes.