Abstract: A method comprising: obtaining multiple glossary terms each comprising one or more words; generating multiple fuzzy tokens from each word of each of the glossary terms; calculating a similarity score for each of the fuzzy tokens, the similarity score denoting a similarity between the respective fuzzy token and its respective word; obtaining multiple input terms to be matched with the multiple glossary terms; separating each of the input terms into multiple input tokens; generating multiple n-grams from each of the input tokens; comparing the n-grams with the fuzzy tokens, to output a list of matching n-grams and fuzzy tokens; based on the list of matching n-grams and fuzzy tokens, identifying, from the glossary terms, candidate glossary term matches for each of the input terms; and calculating one or more scores that quantify the match between each of the candidate glossary term matches and its respective input term.

Abstract: A method for automatically migrating infrastructure as code (IaC) from a first cloud infrastructure platform to a second cloud infrastructure platform is provided. The method may include receiving an original IaC comprising a first type of coding language. The method may further include using natural language processing to map a connection between the first type of coding language and a second type of coding language. The method may further include based on the mapped connection, using the NLP to automatically generate a partial translation of the first type of coding language to the second type of coding language. The method may further include using a machine learning algorithm to correct at least one inaccuracy in the partial translation. The method may further include generating a complete translation and implementing a second IaC on the second cloud infrastructure platform based on the complete translation.

Abstract: An example system includes a processor to receive a data set for training a machine learning model. The processor can train the machine learning model on the data set. The processor can also translate the machine learning model into constraint satisfaction problem (CSP) variables and constraints. The processor can generate fabricated data based on the CSP variables and constraints.

Abstract: A method for automatically migrating infrastructure as code (IaC) from a first cloud infrastructure platform to a second cloud infrastructure platform is provided. The method may include receiving an original IaC comprising a first type of coding language. The method may further include using natural language processing to map a connection between the first type of coding language and a second type of coding language. The method may further include based on the mapped connection, using the NLP to automatically generate a partial translation of the first type of coding language to the second type of coding language. The method may further include using a machine learning algorithm to correct at least one inaccuracy in the partial translation. The method may further include generating a complete translation and implementing a second IaC on the second cloud infrastructure platform based on the complete translation.

Abstract: A computer-implemented method comprising, automatically: analyzing a machine learning dataset which comprises multiple datapoints, to deduce constraints on features of the datapoints; generating a first set of CSP (Constraint Satisfaction Problem) rules expressing the constraints; based on a machine learning model which was trained on the dataset, generating a second set of CSP rules that define one or more perturbation candidates among the features of one of the datapoints; formulating a CSP based on the first and second sets of CSP rules; solving the formulated CSP using a solver; and using the solution of the CSP as a counterfactual explanation of a prediction made by the machine learning model with respect to the one datapoint.

Abstract: A method, computer program product, and computer system are provided. A processor receives an executable file for execution by an operating system, where the executable file includes a plurality of sections in a first order. A processor determines a second order that indicates a loading order for the plurality of sections, where the second order is distinct from the first order. A processor loads the plurality of sections of the executable file into a plurality of locations in memory of a device based on the second order. A processor resolves one or more memory references for the plurality of sections based on the plurality of locations in memory. A processor executes the plurality of sections of the executable file in the plurality of locations in memory.

Abstract: A method, computer program product, and computer system are provided. A processor receives an executable file for execution by an operating system, where the executable file includes a plurality of sections in a first order. A processor determines a second order that indicates a loading order for the plurality of sections, where the second order is distinct from the first order. A processor loads the plurality of sections of the executable file into a plurality of locations in memory of a device based on the second order. A processor resolves one or more memory references for the plurality of sections based on the plurality of locations in memory. A processor executes the plurality of sections of the executable file in the plurality of locations in memory.

Abstract: Systems, methods and products for code optimization are provided. In one embodiment, the method comprises dividing a target code into basic blocks; analyzing traversed execution paths between the basic blocks during multiple executions of the target code to determine a frequency with which the execution path between two or more basic blocks are traversed; and determining whether code optimization may be achieved by identifying execution paths that have been traversed subject to one or more threshold levels.

Abstract: A method for optimizing program code is provided. The method comprises detecting a branch instruction comprising a condition expression, wherein the branch instruction, when executed by a processor, causes the processor to execute either a first set of instructions or a second set of instructions according to a value of the condition expression; and replacing the branch instruction with a third set of instructions that are non-branching, wherein the third set of instructions, when executed by a processor, has a collective effect same as if either the first or second set of instructions were executed according to the value of the condition expression. The third set of instructions comprises a negation instruction to normalize the value of the condition expression.