Abstract: A computer-implemented method executed by at least one processor for software bug localization is presented. The method includes constructing a bug localization graph to capture relationships between bug tickets and relevant source code files from historical change-sets and an underlying source code repository, leveraging natural processing language tools to evaluate semantic similarity between a new bug ticket and a historical ticket, in response to the evaluated semantic similarity, for the new bug ticket, adding links between the new bug ticket a set of similar historical tickets, incorporating the new bug ticket in the bug localization graph, and developing a mathematical graph expression to determine a closeness relationship between the relevant source code files and the new bug ticket.

Abstract: Disclosed embodiments relate to a method and device for optimizing compilation of source code. The proposed method receives a first intermediate representation code of a source code and analyses each basic block instruction of the plurality of basic block instructions contained in the first intermediate representation code for blockification. In order to blockify the identical instructions, the one or more groups of basic block instructions are assessed for eligibility of blockification. Upon determining as eligible, the group of basic block instructions are blockified using one of one dimensional SIMD vectorization and two-dimensional SIMD vectorization. The method further generates a second intermediate representation of the source code which is translated to executable target code with more efficient processing capacity.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate automated generation of a machine learning pipeline based on a pipeline grammar are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a pipeline structure generator component that generates a machine learning pipeline structure based on a pipeline grammar. The computer executable components can further comprise a pipeline optimizer component that selects one or more machine learning modules that achieve a defined objective to instantiate a machine learning pipeline based on the machine learning pipeline structure.

Abstract: A technique for scheduling instructions includes obtaining a set of instructions that operate on memory objects, and determining the dependencies of the memory objects. The memory objects are then sorted into a sequence of memory objects based on the dependencies of the memory objects, and the set of instructions are scheduled into a sequence of instructions according to the sequence of memory objects. Sorting memory objects allows instructions that operate on the same memory object to be kept together. This helps minimize spilling conditions because intervening instructions that do not operate on the same memory object can be avoided.

Abstract: A static safety analysis for control-flow linearization receives a control flow graph (CFG) and an intermediate representation of a computer program, and identifies, for a given loop, all memory load instructions belonging to one side of a diamond-shape structure in the CFG. For each representation of an address of each memory load instruction identified, determining whether it is used on all other sides of the diamond-shape structure. Responsive to determining each representation of an address of each memory load instruction on the one side of the diamond-shape structure is used on all other sides of the diamond-shape structure, determining whether an immediate predecessor of a top of the diamond-shape structure for the given loop post-dominates a header of the given loop. Responsive to determining the immediate predecessor of the top of the diamond-shape structure for the given loop post-dominates the header of the given loop, affirming safety of linearization.

Abstract: A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

Abstract: A computer implemented method includes receiving an exception generated based on programming code, generating exception features from the received exception, the generated exception features being generated based on a set exception features derived from search logs, and executing a machine learning model on the received exception and generated exception features to provide information from the search logs identified as most helpful to resolve the received exception, wherein the machine learning model was trained on training data comprising extracted exceptions and the set of exception features derived from the search logs.

Abstract: Building a first layer model of a three-layer model based on attributes that are sensitive features is provided. A first dimension reduction of sensitive features removes each sensitive feature having an indicator that it is present in a patch and does not contribute to one or more of three probabilities. A second dimension reduction of insensitive features is performed using vectorizing and using one-hot encoding. The remaining insensitive features are main features. One or more second layer models of the three-layer model is built based on the main features. The third layer model is built based on a verification dataset and the first layer model. Regression test coverage is recommended based on prediction result of the third layer model, and wherein regression tests are selected. The training dataset is updated based on probability calculations of the first layer model.

Abstract: Techniques are described for generating HLSD for a textual format source code, which, when rendered, causes a display of visual content. The rendering of the source code generates a tree hierarchy of visual source elements, which logically is possible to map to any graph tree. In an embodiment, visual source elements of the source code are classified to higher-level semantic data (HLSD) labels based on their property(s) and/or the property(s) of neighbor visual source element(s) in the tree hierarchy (context). The HLSD labels indicate the type of HLSD widget mapped to the visual source elements. Techniques further include determining features and a layout arrangement for HLSD widgets and generating a template thereof for the visual content.

Abstract: A code development engine can be programmed to evaluate build code that can be representative of program code at an instance of time during or after a software development of the program code to identify and correct coding errors in the build code. A code run-time simulation engine can be programmed to simulate the build code in a modeled program code environment for the program code to identify and correct coding failures in the build code. A build code output module can be programmed to evaluate the build code to determine whether the build code is acceptable for use in a program code environment based on a level of acceptable risk for the build code in response to the coding error and/or coding failure being corrected in the build code.

Abstract: A system and method for determining a relative importance of a selected test in a plurality of tests, comprising a computational device for receiving one or more characteristics relating to an importance of the code, an importance of each of the plurality of tests, or both; and for determining the relative importance of the selected test according to said characteristics.

Abstract: An image of a graphical user interface is captured. For example, a screen shot of a browser display is captured. Text syntax is executed that contains one or more parameters for identifying a graphical object. For example, the text syntax may identify a rectangle that contains the text “OK” where the text is red. Based on the text syntax, a graphical object is identified in the image of the graphical user interface. Information is returned that identifies how to access the graphical object in the graphical user interface. For example, coordinates of the graphical object are identified. This information can then be used in a test script using existing programming languages to test the graphical user interface. For example, the coordinates may be used to click on the OK button.

Abstract: A computing environment of the invention comprises a graph structure, or multiple interconnected graph structures, the behavior of which is driven by data flow through interconnected nodes, independent of an external code base. The computer system may include a plurality of interconnected nodes in which data flow between the nodes drives execution of one or more functions and wherein code within the system is restricted to said nodes, each of which performs a dedicated function and an engine to drive communication between said system and operating hardware. Data are injected into a node and the node responds by outputting a value (data) to one or more subsequent nodes. Those nodes, in turn, output a behavior that is dependent upon the value (data) they received from one or more other nodes. Thus, it is the data that drives operation of the graph and not a traditional code stack.

Abstract: A system, method and apparatus for choosing a digital processing platform that is optimal for a specified type of application and satisfies a set of user-specified constraints is provided. In operation, all known parameters on all available processing platforms in a database are stored, providing this information to a computer software application run by the user by querying the database, and then allowing a remote user to specify the constraints, in terms of hardware and system software, to eliminate those entries that would not satisfy the constraints in a step-by-step filtering process. The user then chooses a set of application programs to run on the platforms that were not eliminated. The runtime performance parameters/characteristics—e.g. computational throughput, I/O bandwidth, environmental parameters, etc. are measured to select the optimal solution. The system and method also allows for a regression test to ensure consistency between test software processes running on discrete platforms.

Abstract: Methods, systems, and apparatus for executing a smart contract by a blockchain node of a blockchain. An example method includes receiving bytecode of a smart contract; deploying the smart contract, comprising storing the bytecode of the smart contract on the blockchain; compiling, through Just-In-Time (JIT) compilation, the bytecode of the smart contract into machine code; locally storing the machine code in a memory of the blockchain node; and executing the smart contract deployed on the blockchain, comprising determining whether the machine code corresponding to the bytecode of the smart contract is locally stored in the memory of the blockchain node, and interpreting and executing the bytecode of the smart contract if the machine code corresponding to the bytecode of the smart contract is not locally stored.

Abstract: Discussed are DevOps systems, methods, and apparatuses that provide a solution for organizing, reporting, and facilitating the automation of deploying infrastructure, platform, and application code for an IT enterprise. A logical data model for organizing components of the DevOps approach may include inventories for systems, environments, applications, resources, and deployments. A set of components may be built to provide an organizational structure to collect, report, and facilitate the automation of applications and the deployments of those applications across the enterprise. An artifact inventory may be used to request a deployment of specific versions of artifacts to an environment, providing a complete picture of what the environment should contain after a deployment. This inventory may be linked to business-level system inventory tracking as well as low-level computing resource tracking to provide a complete picture of total cost of ownership.

Abstract: An automated program repair tool utilizes a neural transformer model with attention to predict the contents of a bug repair in the context of source code having a bug of an identified bug type. The neural transformer model is trained on a large unsupervised corpus of source code using a span-masking denoising optimization objective, and fine-tuned on a large supervised dataset of triplets containing a bug-type annotation, software bug, and repair. The bug-type annotation is derived from an interprocedural static code analyzer. A bug type edit centroid is computed for each bug type and used in the inference decoding phase to generate the bug repair.

Abstract: Cloud services release orchestration with a reusable deployment pipeline. According to some implementations, responsive to receiving from a COS controller parameters from configuration information provided to the COS controller while an app aware proxy routes production traffic to a first application (app) version that communicates with a database management system (DBMS) and that runs in container orchestration system (COS) pods having first app version containers, causing a validation of a second app version using COS pods having second app version containers that are now live after having been brought up by the COS controller responsive to the provision of the configuration information. After the validation, causing the transition to sending production traffic to the second app version containers that are determined to be live instead of to the first app version containers.

Abstract: Described herein are improvements for generating courses of action for an information technology (IT) environment. In one example, a method includes determining that a decision step occurs between a one step and two or more other steps of a first course of action associated with an incident type in the information technology environment. The method further includes determining possible outputs of the one step that, when used as input to the decision step, cause the first course of action to proceed from the decision step to respective steps of the two or more other steps. The method also includes incorporating logic into the decision step to direct the course of action to respective steps of the two or more other steps based on one or more of the possible outputs.

Abstract: Disclosed subject matter is related to project reusability including method and system for identifying reusable project components for building a new project. The method comprises extracting current requirement of the new project from one or more data sources, using NLP and detect pre-existing requirements associated with pre-existing projects, similar to the current requirement by performing semantic analysis of the current requirement and the pre-existing requirements using a predefined machine learning technique such as Latent Semantic Analysis (LSA) technique. Further, a similarity score is determined for each of the one or more pre-existing requirements based on degree of similarity between the current requirement and the pre-existing requirements.