Abstract: Copper-tin-nickel brazing material prepared by alloys recycled from E-waste, preparation method therefor, and system thereof are provided. A preparation method for the copper-tin-nickel brazing material includes the following steps: (a) spreading nano-SiO2 on the bottom of crucible and then adding a crude copper-tin-iron-nickel alloy recycled from E-waste; (b) heating the crucible to melt the crude alloy into a metal liquid so that Zn and Pb in the metal liquid react with the SiO2 to form a slag that floats out; (c) introducing a refining gas to the bottom of metal liquid in step (b), thereby removing the scums or gases formed by Pb, Fe, S, and O in the metal liquid; (d) performing heat-preserving directional solidification on the metal liquid, to bias-aggregate the Fe and Sb at one end and remove the same to obtain a copper-based intermediate alloy; and smelting and powdering the copper-based intermediate alloy.

Abstract: A machine learning model can be trained to infer the probability of the presence of categories of a software bug in a source code file. A bug tracker can provide information concerning the category to which a software bug belongs. The bug data supplied to a machine learning model for inferring the presence of particular categories of bugs can be filtered to exclude a specified category or categories of bugs. Information including but not limited to organizational boundaries can be inferred from the category of bugs present in a body of source code. The inferred organization boundaries can be used to generate team-specific machine learning models.

Abstract: A data copy method comprises: acquiring, by a destination server, a data block identifier, version information and hole information; transmitting, by the destination server, the data block identifier, the version information and the hole information to a source terminal server so as to enable the source server to find corresponding data according to the data block identifier, the version information and the hole information; receiving, by the destination server, a response message carrying the data transmitted by the source server and copying the data to a hole corresponding to the data block identifier, the version information and the hole according to the response message.

Abstract: Data storage method, apparatus, and system are disclosed. The data storage method includes: encoding a data block using an erasure coding to obtain corresponding multiple data fragments; separately writing the multiple data fragments to corresponding allocated storage nodes; and determining that writing of the data block is successful if a number of storage nodes to which data fragments is successfully written within a first defined time duration is greater than or equal to a defined value, wherein the defined value is greater than or equal to a number of data block fragments in the multiple data fragments and is less than or equal to a number of the multiple data fragments. As such, the latency of a data write can be reduced, and the performance stability can be improved when a storage node encounters a failure.

Abstract: Embodiments of the disclosure provide a data write-in method and apparatus. The data write-in method includes: selecting a target replica server from a plurality of replica servers managed by a metadata server according to remaining capacity of the plurality of replica servers; selecting a write-in disk in the target replica server according to remaining capacity and load of disks managed by the target replica server; and storing write-in data into the write-in disk through the target replica server.

Abstract: Data storage method, apparatus, and system are disclosed. The data storage method includes: encoding a data block using an erasure coding to obtain corresponding multiple data fragments; separately writing the multiple data fragments to corresponding allocated storage nodes; and determining that writing of the data block is successful if a number of storage nodes to which data fragments is successfully written within a first defined time duration is greater than or equal to a defined value, wherein the defined value is greater than or equal to a number of data block fragments in the multiple data fragments and is less than or equal to a number of the multiple data fragments. As such, the latency of a data write can be reduced, and the performance stability can be improved when a storage node encounters a failure.

Abstract: A method for updating data in a distributed storage system is disclosed. The method may comprise receiving, by a sender terminal device, an update request; obtaining, by the sender terminal device, update data and control information from the update request; sending, by the sender terminal device, the update data, via a data update request, to a receiver terminal device; and sending, by the sender terminal device, the control information, via a control information update request, to the receiver terminal device.

Abstract: Information concerning software bugs including bug detection, bug prediction data and/or historical bug data can be used to determine whether it is safe to commit, integrate, deploy and/or deliver a software change. If the change is deemed unsafe, the change can be rejected automatically. Alternatively, the change can proceed following approval by an administrator, supervisor, implementer, manager and/or other designated approval mechanism. Actions taken to override a block can be recorded along with information concerning failed deployments and/or deliveries, a higher than normal customer failure rate after deployment and/or delivery or through manual data entry.

Abstract: Information concerning software bugs including bug detection, bug prediction data and/or historical bug data can be used to determine whether it is safe to commit, integrate, deploy and/or deliver a software change. If the change is deemed unsafe, the change can be rejected automatically. Alternatively, the change can proceed following approval by an administrator, supervisor, implementer, manager and/or other designated approval mechanism. Actions taken to override a block can be recorded along with information concerning failed deployments and/or deliveries, a higher than normal customer failure rate after deployment and/or delivery or through manual data entry.

Abstract: For each detected bug, historical code with similar characteristics and bug corrections from a historical bug dataset can be displayed in a source code editor. Relevant training and/or testing data can be found by comparing an internal representation of the code under development with an internal representation of the original buggy code in the historical bug dataset. Training and/or testing data that is relevant to the current code can be distinguished from irrelevant training and/or testing data by determining that the code syntax tokens from the current and historical data overlap to at least a specified percentage. Code can be devolved into a set of metrics. The degree of overlap between the metric sets can be determined. If a computed risk factor for the bug correction meets or exceeds a specified threshold, the bug correction can be automatically applied. Additional testing can be automatically performed on and/or added to the corrected code.

Abstract: A machine learning model can be trained to infer the probability of the presence of categories of a software bug in a source code file. A bug tracker can provide information concerning the category to which a software bug belongs. The bug data supplied to a machine learning model for inferring the presence of particular categories of bugs can be filtered to exclude a specified category or categories of bugs. Information including but not limited to organizational boundaries can be inferred from the category of bugs present in a body of source code. The inferred organization boundaries can be used to generate team-specific machine learning models.

Abstract: For each detected bug, historical code with similar characteristics and bug corrections from a historical bug dataset can be displayed in a source code editor. Relevant training and/or testing data can be found by comparing an internal representation of the code under development with an internal representation of the original buggy code in the historical bug dataset. Training and/or testing data that is relevant to the current code can be distinguished from irrelevant training and/or testing data by determining that the code syntax tokens from the current and historical data overlap to at least a specified percentage. Code can be devolved into a set of metrics. The degree of overlap between the metric sets can be determined. If a computed risk factor for the bug correction meets or exceeds a specified threshold, the bug correction can be automatically applied. Additional testing can be automatically performed on and/or added to the corrected code.

Abstract: A risk factor that software written by a developer includes bugs can be calculated for the developer. The risk factor can be used to determine the quality of the developer's code. The risk factor associated with code produced by a particular developer can be provided to a manager or management system. The risk factor can be used to provide bug-based information to a corporate review and reward process.

Abstract: Information concerning software bugs including bug detection, bug prediction data and/or historical bug data can be used to determine whether it is safe to commit, integrate, deploy and/or deliver a software change. If the change is deemed unsafe, the change can be rejected automatically. Alternatively, the change can proceed following approval by an administrator, supervisor, implementer, manager and/or other designated approval mechanism. Actions taken to override a block can be recorded along with information concerning failed deployments and/or deliveries, a higher than normal customer failure rate after deployment and/or delivery or through manual data entry.

Abstract: Embodiments of the disclosure provide a data write-in method and apparatus. The data write-in method includes: selecting a target replica server from a plurality of replica servers managed by a metadata server according to remaining capacity of the plurality of replica servers; selecting a write-in disk in the target replica server according to remaining capacity and load of disks managed by the target replica server; and storing write-in data into the write-in disk through the target replica server.

Abstract: A probabilistic machine learning model is generated to identify potential bugs in a source code file. Source code files with and without bugs are analyzed to find features indicative of a pattern of the context of a software bug, wherein the context is based on a syntactic structure of the source code. The features may be extracted from a line of source code, a method, a class and/or any combination thereof. The features are then converted into a binary representation of feature vectors that train a machine learning model to predict the likelihood of a software bug in a source code file.

Abstract: A method including sending an update request to replica servers listed in a copy list respectively; receiving respective response information from a respective replica server of the replica servers after an update of a respective data copy in the respective replica server is completed; in response to determining that a number of response information received in a preset update time is less than a total number of the replica servers, modifying a status of copy information corresponding to a replica server that has not sent a response information to a status of continuing to update; in response to determining that response information from the replica servers with the status of continuing to update has not been received within a preset continuous update time, deleting corresponding copy information; updating a list attribute value of the copy list; and sending the updated list attribute value to a central server.

Abstract: A data copy method comprises: acquiring, by a destination server, a data block identifier, version information and hole information; transmitting, by the destination server, the data block identifier, the version information and the hole information to a source terminal server so as to enable the source server to find corresponding data according to the data block identifier, the version information and the hole information; receiving, by the destination server, a response message carrying the data transmitted by the source server and copying the data to a hole corresponding to the data block identifier, the version information and the hole according to the response message.

Abstract: Multidimensional key based construction of a filterable hierarchy is provided. A data service initiates operations to construct the hierarchy by joining current node-key elements with an element delimiter into a current node string. Child node elements are also joined with the element delimiter into child node string(s). The child node string(s) are further encapsulated with nesting delimiter(s) and the resulting string is concatenated to the current node string. Next, parent node elements are joined with the element delimiter. The current node-key is generated by concatenating the parent node string to the current node string (that includes the child node string(s)) with a parent delimiter. The current node-key is inserted into a representation for the current node within a data structure.

Abstract: A product or service is enhanced by optimizing success factors associated with the product or service. A enhancement application initiates operations to compute a predicted score (of a success of the product or service) and a suggestion to achieve the predicted score by retrieving performance and/or configuration data associated with existing products or services from a data source. The performance and/or configuration data is analyzed to generate a model of success factors associated with the existing products or services. Next, configuration conditions of a current product are received from a stakeholder. In response, predicted score(s) are computed for the success factors using the model by simulating the configuration conditions of the current product or service on the model. Furthermore, the predicted score(s) and/or suggestion(s) to achieve the predicted score(s) are provided in a visualization to the stakeholder.