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公开(公告)号：US20240168757A1
公开(公告)日：2024-05-23
申请号：US18551466
申请日：2022-03-10
Inventor： Alistair MCCORMICK , Adam ZIOLKOWSKI , Emmanuel FERREYRA OLIVARES , Johannes NOPPEN
Abstract: A computer implemented method to generate a software service from software code for a software component, the method including converting the code to a model representation, the model including elements corresponding to functional components in the code and relationships between elements corresponding to one or more of functional links and data relationships between the functional components; applying a clustering method to the model to define a plurality of clusters of elements of the model, each cluster of elements representing a set of functional components in the code corresponding to the elements in the cluster; monitoring the software code in execution to identify a set of functional components in the code corresponding to a cluster of elements in which the set of functional components is collectively stateless between executions of any of the functional components in the set; and generating a software service as an executable software component comprising the identified set of functional components.
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公开(公告)号：US20240168756A1
公开(公告)日：2024-05-23
申请号：US18551475
申请日：2022-03-10
Inventor： Johannes NOPPEN , Aftab ALI , Mamun ABU-TAIR , Sally MCCLEAN , Adam ZIOLKOWSKI , Alistair MCCORMICK , Naveed KHAN
Abstract: A computer implemented method of updating software code in a code management system, the method including receiving candidate code for merging with the code in the code management system; extracting each of a plurality of features of the candidate code, each feature being based on one or more predetermined metrics of the candidate code; processing at least a subset of the extracted features by each of a plurality of disparate classifiers, each classifier being trained by a supervised training method to identify one or more software code defects, such that each classifier identifies a set of features as indicative of a software code defect, wherein intersections between a predetermined number of the sets of features identified by the classifiers are indicated as prospective code defects; selectively merging the candidate code with the code in the code management system based on the prospective code defects.
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公开(公告)号：US20240168755A1
公开(公告)日：2024-05-23
申请号：US18551471
申请日：2022-03-10
Inventor： Johannes NOPPEN , Alistair MCCORMICK , Adam ZIOLKOWSKI , Naveed KHAN , Mamun ABU-TAIR , Sally MCCLEAN , Aftab ALI
Abstract: A computer implemented method of updating software code in a code management system, the method including receiving candidate code for merging with the code in the code management system; extracting each of a plurality of features of the candidate code, each feature being based on one or more predetermined metrics of the candidate code; processing at least a subset of the extracted features by each of a plurality of disparate classifiers, each classifier being trained by a supervised training method to identify one or more software code defects, such that each classifier identifies a set of features as indicative of a software code defect, wherein intersections between a predetermined number of the sets of features identified by the classifiers are indicated as prospective code defects; and selectively merging the candidate code with the code in the code management system based on the prospective code defects.
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Patent Agency Ranking
公开(公告)号：US20240177066A1
公开(公告)日：2024-05-30
申请号：US18551481
申请日：2022-03-10
Inventor： Adam ZIOLKOWSKI , Alistair MCCORMICK , Emmanuel FERREYRA OLIVARES , Johannes NOPPEN
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: A computer implemented method of deploying an artificial intelligence (AI) algorithm to model a function can include defining a verification test for verifying that the AI algorithm models the function, the fitness test being defined based on a set of input/output pairs each indicating the required output of the function for an input; defining a machine learning component having a machine learning algorithm and a configuration, the machine learning algorithm being trained based on training data to model the function; iteratively adapting the machine learning component over a plurality of generations, wherein each generation of the component is adapted by modifying the configuration of the component, and wherein the adaptation for a generation is selected from a set of candidate adaptations based on a determination of a fitness of the component so adapted, the fitness being determined by the verification test, wherein the iteration ceases in response to a stopping condition such that, on cessation, a latest generation of the machine learning component is selected to constitute the AI algorithm modelling the function.
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公开(公告)号：US20240176615A1
公开(公告)日：2024-05-30
申请号：US18551486
申请日：2022-03-10
Inventor： Adam ZIOLKOWSKI , Alistair MCCORMICK , Emmanuel FERREYRA OLIVARES , Johannes NOPPEN
IPC: G06F8/72
CPC classification number: G06F8/72
Abstract: A computer implemented method of generating a software service for providing required software functionality can include accessing a software component having functionality including and exceeding the required functionality; defining verification test for verifying that the software component includes the required functionality; applying a genetic algorithm to the software component to iteratively adapt the software component over a plurality of generations, wherein each generation of the software component is adapted by removal of one or more portions of the software component of a preceding generation, wherein the adaptation for a generation is selected from a set of candidate adaptations based on a determination of a fitness of the component so adapted, the fitness being determined by the verification test, wherein the iteration of the genetic algorithm ceases in response to a stopping condition such that, on cessation, the latest generation of the software component constitutes the software service.
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公开(公告)号：US20240169271A1
公开(公告)日：2024-05-23
申请号：US18551461
申请日：2022-03-10
Inventor： Johannes NOPPEN , Panagiotis KOUROUKLIDIS , Alistair MCCORMICK
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: A computer implemented method for operating a software application including a trained machine learning model, the method comprising: receiving one or more rules for measuring a fitness of the machine learning model according to a predetermined specification of fitness; identifying one or more model data parameters derivable from the machine learning model required for execution of the rules; retrieving the identified 0 parameters; executing the rules to determine a measure of fitness of the machine learning model; and responsive to a determination that the measure of fitness meets a predetermined threshold measure to indicate insufficient fitness, performing one or more adjustments to the application such that a measure of fitness of the machine learning model meets a predetermined threshold measure to indicate sufficient fitness.
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公开(公告)号：US20220147887A1
公开(公告)日：2022-05-12
申请号：US17593624
申请日：2020-03-18
Inventor： Timothy GLOVER , Alistair MCCORMICK , Andrew STARKEY , Anthony CONWAY
Abstract: A computer implemented method of routing multiple resource carriers to exchange resources at multiple exchange points. The resource carriers have different quantity capacities for a resource and each exchange point has a geo-location. The method includes: iterating a genetic algorithm, having a stopping condition based on a characteristic indicative of a cost of the subset, modelling usage of proper subsets of the carriers. Each iteration of the genetic algorithm includes: defining, for each carrier in the subset, a set of exchange points based on geo-locations, an objective exchange point that the carrier must visit, and the carrier's capacity; evaluating the characteristic for the subset of carriers; and responsive to the characteristic, selecting the subset as a prospective optimal subset and determining, for each carrier in the prospective optimal subset, an optimum route through the exchange points including the objective exchange point. The prospective optimal subset is selected over multiple iterations of the genetic algorithm such that a current prospective optimal subset is selected as an optimal subset having associated an optimum route for each carrier in the optimal subset.
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公开(公告)号：US20180234324A1
公开(公告)日：2018-08-16
申请号：US15751296
申请日：2016-08-24
Inventor： Okung NTOFON , Siddhartha SHAKYA , Gilbert OWUSU , Jonathan MALPASS , Alistair MCCORMICK
CPC classification number: H04L43/12 , G06F11/34 , H04L41/046 , H04L41/0695 , H04L41/142 , H04L43/04 , H04L43/10 , H04L43/16
Abstract: Information received from disparate individual monitors that are concurrently measuring a predetermined property of a predetermined resource in a network are compared in a reliability computation engine to compute a metric of the degree of similarity between their measurements, and thus to determine a measure of the reliability of one or more of the individual monitors. This information can be used by a provisioning engine to select or reject individual resources for use in meeting service requirements on the basis of the reliability of the reports of their performance, as well as the reported performance itself. Monitors identified as unreliable can also be reported to a fault diagnosis function.
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