Abstract: Methods, systems, and articles of manufacture to autonomously select data structures are disclosed. An example apparatus includes an ordinal assigner to assign training code operations to respective first ordered values, and assign candidate data structure types to respective second ordered values, a filter generator to, for a first instruction of the training code operations, generate a Bloom filter bit vector pattern based on (a) one of the first ordered values, (b) one of the second ordered values corresponding to a first one of the candidate data structure types, and (c) a size of the first instruction, a label generator to generate a first model training input feature vector based on the Bloom filter bit vector pattern, data corresponding to the first instruction, and a performance metric of the first one of the candidate data structure types, and a neural network manager to train the data structure selection model with the first model training input feature vector.

Abstract: Methods, apparatus, systems, and articles of manufacture to perform heterogeneous data structure selection via programmer annotations. An example apparatus includes a phase tracker to identify a first phase and a second phase, a cost predictor to estimate interaction costs of interacting with respective types of data structures within the first phase and the second phase, a tree constructor to construct a tree corresponding to a first data structure type, the tree including a first node in the first phase, a second node in the second phase, and an edge connecting the first node and the second node, the second node representing a second data structure type different from the first data structure type, a transformation cost calculator to calculate a transformation cost for the edge, and a branch selector to select a sequence of data structures based on the combined interaction costs and transformation costs.

Abstract: Systems and methods for determining a configuration for a microarchitecture are described herein. An example system includes a proposal generator to generate a first candidate configuration of parameters for the microarchitecture, a machine learning model to process the first candidate configuration of parameters to output estimated performance indicators for the microarchitecture, an uncertainty checker to determine whether the estimated performance indicators are reliable, and a performance checker. In response to a determination that the estimated performance indicators are reliable, the performance checker is to determine whether the estimated performance indicators have improved toward a target. Further, if the estimated performance indicators have improved, the performance checker is to store the first candidate configuration of parameters in a memory as a potential solution for a microarchitecture without performing a full simulation on the first candidate configuration of parameters.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Abstract: An apparatus includes a software parser to generate a plurality of abstract syntax trees based on a plurality of software files, the ASTs including subtrees corresponding to a plurality of functions of the software files, a subtree encoder to generate a plurality of code vectors representative of one or more semantic properties of the subtrees, a function identifier to determine a plurality of clusters for the subtrees and assign a cluster identifier and a function label to the subtrees, a tree database to store the subtrees and map the plurality of subtrees to respective ones of cluster identifiers and function names, and a processor to: train a model based on a feature vector and the plurality of clusters stored in the tree database and predict the cluster identifier for the subtrees, based on the trained model, to identify a name of the function.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for object manipulation via action sequence optimization. An example method disclosed herein includes determining an initial state of a scene, generating a first action phase sequence to transform the initial state of the scene to a solution state of the scene by selecting a plurality of action phases based on action phase probabilities, determining whether a first simulated outcome of executing the first action phase sequence satisfies an acceptability criterion and, when the first simulated outcome does not satisfy the acceptability criterion, calculating a first cost function output based on a difference between the first simulated outcome and the solution state of the scene, the first cost function output utilized to generate updated action phase probabilities.

Abstract: Apparatus, systems, articles of manufacture, and methods for a context and complexity-aware recommendation system for efficient software development.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for code review assistance for dynamically typed languages. An example apparatus to analyze a segment of code includes a function identifier to identify a first input of a first function call included in the segment of the code, a parameter type vector (PTV) estimatior model to estimate a first data structure based on the first input, the PTV estimatior model generated via a set of reviewed code, a PTV determiner to generate a second data structure based on a data parameter type of the first input, an error comparator to determine a first reconstruction error based on the first data structure, and the second data structure and a recommendation generator to, if the first reconstruction error does not satisfy a recommendation threshold, generate a first recommendation to review the first function call.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for intentional programming for heterogeneous systems. An example apparatus includes a code lifter to identify annotated code corresponding to an algorithm to be executed on the heterogeneous system based on an identifier being associated with the annotated code, and convert the annotated code in the first representation to intermediate code in a second representation by identifying the intermediate code as having a first algorithmic intent that corresponds to a second algorithmic intent of the annotated code, a domain specific language (DSL) generator to translate the intermediate code in the second representation to DSL code in a third representation when the first algorithmic intent matches the second algorithmic intent, the third representation corresponding to a DSL representation, and a code replacer to invoke a compiler to generate an executable including variant binaries based on the DSL code.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that adjust autonomous vehicle driving software using machine programming. An example apparatus for adjusting autonomous driving software of a vehicle includes an input analyzer to determine a software adjustment based on an obtained driving input and a priority determiner to determine a priority level of the software adjustment. The apparatus further includes a program adjuster to, when the priority level is above a threshold, identify a parameter of the autonomous driving software of the vehicle associated with the software adjustment and adjust the parameter based on the software adjustment, the adjustment to the parameter to change driving characteristics of the vehicle.

Abstract: Methods, apparatus, systems and articles of manufacture for providing query selection systems are disclosed. An example query selection system includes a processor to: analyze a graph database; identify respective ones of objects associated with the graph database; obtain properties associated with the objects; identify common properties present in the respective ones of the objects; in response to determining the common properties present in the identified objects, output a list of the common properties corresponding to the respective ones of the objects; generate a table for the common properties and the respective ones of the objects; and populate the table with the common properties and the respective ones of the objects from the graph database to establish a relational database. The system further includes a machine learning model classifier to enable relational database query searching via the relational database.

Abstract: Example apparatus and methods for program synthesis using genetic algorithms are disclosed herein. An example apparatus includes a program length predictor to predict a length of a first program by executing a first neural network model, a program generator to generate a candidate program having a length corresponding to the predicted length, a candidate program analyzer to generate a fitness score for the candidate program by executing a second neural network model and to identify the first candidate program for use in a breeding operation relative a second candidate program based on the fitness score, and a genetic program generator to perform the breeding operation with at least one of the first candidate program or the second candidate program to generate an evolved candidate program.

Abstract: Methods, apparatus, systems and articles of manufacture to optimize execution of a machine learning model are disclosed. An example apparatus includes a quantizer to quantize a layer of a model based on an execution constraint, the layer of the model represented by a matrix. A packer is to pack the quantized layer of the matrix to create a packed layer represented by a packed matrix, the packed matrix having non-zero values of the matrix grouped together along at least one of a row or a column of the matrix. A blocker is to block the packed layer into a blocked layer by dividing the non-zero values in the packed matrix into blocks. A fuser is to fuse the blocked layer into a pipeline. A packager is to package the pipeline into a binary.

Abstract: Apparatus, devices, systems, methods, and articles of manufacture for intent-based machine programming are disclosed. An example system categorize source code blocks includes a code repository accessor to access a code repository and select a source code block. The example system also includes a signature generator to generate a signature for the source code block, a collateral miner to extract collateral associated with the source code block, and a tokenizer to transform the source code block into tokens. In addition, the example system includes a function assessor to determine a function of the source code block based on the collateral and the tokens and an input/output determiner to determine an input and an output of the source code block based on the collateral and the signature. The example system further includes a tagger to categorize the source code block with the function, input, and output.

Abstract: Methods, apparatus, systems and articles of manufacture to recommend instruction adaptations to improve compute performance are disclosed. An example apparatus includes a pattern detector to detect an execution pattern from an execution profile provided by a server, the execution profile associated with an instruction stored in an instruction repository. An adaptation identifier is to identify a possible instruction adaptation that may be applied to the instruction associated with the execution pattern. A model processor is to predict, using a machine learning model, an expected performance improvement of the adaptation. A result comparator is to determine whether the expected performance improvement meets an threshold. An instruction editor is to, in response to the result comparator determining that the expected performance improvement meets the threshold, apply the possible instruction adaptation to the instruction in the instruction repository.

Abstract: Apparatus, systems, methods, and articles of manufacture for automated quality assurance and software improvement are disclosed. An example apparatus includes a data processor to process data corresponding to events occurring with respect to a software application in i) a development and/or a testing environment and ii) a production environment. The example apparatus includes a model tool to: generate a first model of expected software usage based on the data corresponding to events occurring in the development and/or the testing environment; and generate a second model of actual software usage based on the data corresponding to events occurring in the production environment. The example apparatus includes a model comparator to compare the first model to the second model. The example apparatus includes a correction generator to generate an actionable recommendation to adjust the development and/or the testing environment to reduce a difference between the first model and the second model.

Abstract: Apparatus, systems, methods, and articles of manufacture for automatic robot perception programming by imitation learning are disclosed. An example apparatus includes a percept mapper to identify a first percept and a second percept from data gathered from a demonstration of a task and an entropy encoder to calculate a first saliency of the first percept and a second saliency of the second percept. The example apparatus also includes a trajectory mapper to map a trajectory based on the first percept and the second percept, the first percept skewed based on the first saliency, the second percept skewed based on the second saliency. In addition, the example apparatus includes a probabilistic encoder to determine a plurality of variations of the trajectory and create a collection of trajectories including the trajectory and the variations of the trajectory.

Abstract: Methods and apparatus to facilitate generation of database queries are disclosed. An example apparatus includes a generator to generate a global importance tensor. The global importance tensor based on a knowledge graph representative of information stored in a database. The knowledge graph includes objects and connections between the objects. The global importance tensor includes importance values for different types of the connections between the objects. The example apparatus further includes an importance adaptation analyzer to generate a session importance tensor based on the global importance tensor and a user query, and a user interface to provide a suggested query to a user based on the session importance tensor.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve code characteristics. An example apparatus includes a weight manager to apply a first weight value to a first objective function, a state identifier to identify a first state corresponding to candidate code, and an action identifier to identify candidate actions corresponding to the identified first state.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that analyze computer system attack mechanisms. An example apparatus includes a graph generator utilizing a natural language processing model to generate a graph based on a publication, an analyzer to: analyze two or more nodes in the graph by identifying respective attributes of the two or more nodes in the graph, and provide an indication of the two or more nodes that include similar respective attributes, a variation generator to generate an attack mechanism based on the indication, and a weight postulator to obtain the generated attack mechanism and, based on (A) the two or more nodes in the graph and (B) the generated attack mechanism, indicate a weight associated with a severity of the generated attack mechanism.