Abstract: A method, system and computer program product for analyzing multidimensional data are disclosed. In embodiments, the method comprises obtaining an original set of data having a sequential order and multiple original dimensions; selecting a topic-based summarization scheme to summarize the original set of data; and applying the selected topic-based summarization scheme to the original set of data to transform the original set of data into a new set of data having fewer dimensions than the original set of data, while preserving, within a defined measure, the sequential order of the original set of data. In embodiments, the selecting a topic-based summarization scheme includes selecting a plurality of topics, each of the topic representing a set of the original dimensions. In embodiments, the applying the topic-based summarization scheme includes performing dimensionality reduction on the original set of data to transform the original dimensions to the topics.

Abstract: Techniques for distributing the training of machine learning models across a plurality of computing devices are presented. An example method includes receiving, from a computing device in a distributed computing environment, a request for a set of outstanding jobs for training part of a machine learning model. A system transmits, to the computing device, information identifying the set of outstanding jobs. The system receives, from the computing device, a selected job for execution on the computing device from the set of outstanding jobs. A chunk of training data associated with the selected job and one or more parameters associated with the selected job may be transmitted to the computing device, and the system may take one or more actions with respect to the chunk of data associated with the selected job based on a response from the computing device.

Abstract: A method and system of matching an application program interface (API) code usage with an API specification are provided. A program having an API code usage is received and its features are extracted therefrom. Features from meta data of a plurality of API specifications are extracted. For each API specification of the plurality of API specifications, a match probability with the API code usage is determined. An API specification having a highest probability is determined. The API code usage is matched with the API specification having the highest probability.

Abstract: A computer implemented method for testing an application according to usage data includes receiving an application to be tested and a set of usage data corresponding to the application to be tested, wherein the set of usage data corresponds to previously executed code sequences, identifying one or more code sequences of interest corresponding to the received application, wherein the code sequences of interest correspond to codes sequences that are configured to exercise the received application, extracting concrete usages of the code sequence of interest from the received set of usage data, generating one or more test cases for the application according to the extracted usages, and providing the one or more generated test cases. The method may additionally include testing the application according to the one or more generated test cases.

Abstract: Techniques for autonomously generating a code usage summary associated with a web application programming interface request are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, data from a data repository, wherein the evaluating is based on a defined machine learning process. Also, the computer-implemented method can comprise generating, by the system, a usage summary of the data, wherein the usage summary is based on a statistic derived from a web application programming interface request, and the web application programming interface request is associated with the data.

Abstract: Various embodiments migrate a legacy application to a multi-tenant computing environment. In one embodiment, at least one virtualized computing container is instantiated on a host system in a multi-tenant computing environment. An instance of the legacy application is executed within the virtualized computing container. The legacy application having been initially configured to run on premise and serve one tenant at a time. The virtualized computing container securely isolates the executing instance of the legacy application from other executing instances of the legacy application. At least one request received from a first client is sent to the instance of the legacy application executing within the virtualized computing container. The virtualized computing container is quiesced based on at least one quiescing criterion having been satisfied by the instance of the legacy application.

Abstract: Techniques for distributing the training of machine learning models across a plurality of computing devices are presented. An example method includes receiving, from a computing device in a distributed computing environment, a request for a set of outstanding jobs for training part of a machine learning model. A system transmits, to the computing device, information identifying the set of outstanding jobs. The system receives, from the computing device, a selected job for execution on the computing device from the set of outstanding jobs. A chunk of training data associated with the selected job and one or more parameters associated with the selected job may be transmitted to the computing device, and the system may take one or more actions with respect to the chunk of data associated with the selected job based on a response from the computing device.

Abstract: A method and system of matching an application program interface (API) code usage with an API specification are provided. A program having an API code usage is received and its features are extracted therefrom. Features from meta data of a plurality of API specifications are extracted. For each API specification of the plurality of API specifications, a match probability with the API code usage is determined. An API specification having a highest probability is determined. The API code usage is matched with the API specification having the highest probability.

Abstract: A method, system and computer program product for analyzing multidimensional data are disclosed. In embodiments, the method comprises obtaining an original set of data having a sequential order and multiple original dimensions; selecting a topic-based summarization scheme to summarize the original set of data; and applying the selected topic-based summarization scheme to the original set of data to transform the original set of data into a new set of data having fewer dimensions than the original set of data, while preserving, within a defined measure, the sequential order of the original set of data. In embodiments, the selecting a topic-based summarization scheme includes selecting a plurality of topics, each of the topic representing a set of the original dimensions. In embodiments, the applying the topic-based summarization scheme includes performing dimensionality reduction on the original set of data to transform the original dimensions to the topics.

Abstract: A method of generating process trace clusters includes receiving a set of process traces, where a process trace is an application level event log from a computer system; receiving an identification of node types and edge types of the application event log to generate a heterogeneous information network (HIN) graph, where a temporal node type value is a difference between a current system time and a reference timestamp divided by a user chosen time granularity parameter, performing dimension reduction of the HIN to generate clusters of new event types, generating a set of meta-paths that connect nodes of a same reduced event type in the application event log, calculating similarities between process traces using the new event type clusters, and clustering the set of process traces to generate a plurality of disjoint graph models of processes in the application event log based on the clusters of new event types.

Abstract: Techniques for autonomously generating a code usage summary associated with a web application programming interface request are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, data from a data repository, wherein the evaluating is based on a defined machine learning process. Also, the computer-implemented method can comprise generating, by the system, a usage summary of the data, wherein the usage summary is based on a statistic derived from a web application programming interface request, and the web application programming interface request is associated with the data.

Abstract: Techniques for autonomously generating a code usage summary associated with a web application programming interface request are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, data from a data repository, wherein the evaluating is based on a defined machine learning process. Also, the computer-implemented method can comprise generating, by the system, a usage summary of the data, wherein the usage summary is based on a statistic derived from a web application programming interface request, and the web application programming interface request is associated with the data.

Abstract: Techniques for autonomously generating a code usage summary associated with a web application programming interface request are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, data from a data repository, wherein the evaluating is based on a defined machine learning process. Also, the computer-implemented method can comprise generating, by the system, a usage summary of the data, wherein the usage summary is based on a statistic derived from a web application programming interface request, and the web application programming interface request is associated with the data.

Abstract: A method and system of determining whether a specification is an accurate representation of an application program interface (API) is provided. The specification is received electronically over a network. Service calls to be tested are identified based on the specification. A test case is created for each of the identified service calls. A sequence is created for the test cases. A test plan is generated based on the created sequence. The generated test plan is executed. Upon identifying an error in response to the executed test plan, a notification is generated, indicating that the specification is not an accurate representation of the API.

Abstract: A computer implemented method for testing an application according to usage data includes receiving an application to be tested and a set of usage data corresponding to the application to be tested, wherein the set of usage data corresponds to previously executed code sequences, identifying one or more code sequences of interest corresponding to the received application, wherein the code sequences of interest correspond to codes sequences that are configured to exercise the received application, extracting concrete usages of the code sequence of interest from the received set of usage data, generating one or more test cases for the application according to the extracted usages, and providing the one or more generated test cases. The method may additionally include testing the application according to the one or more generated test cases.

Abstract: An action sequence including a plurality of actions and a corresponding input dictionary are obtained. A first container, running an image for a first one of the actions implemented in a first programming language, is instantiated, and the image is executed on the input dictionary to obtain a first action result; the input dictionary is then updated with the first action result to obtain an updated input dictionary. A second container, running an image for a second one of the actions implemented in a second programming language, different than the first programming language, is instantiated, and the image is executed on the updated input dictionary to obtain a second action result; the updated input dictionary is further updated with the second action result to obtain a further updated input dictionary. The input dictionaries are independent of the programming languages.

Abstract: An action sequence including a plurality of actions and a corresponding input dictionary are obtained. A first container, running an image for a first one of the actions implemented in a first programming language, is instantiated, and the image is executed on the input dictionary to obtain a first action result; the input dictionary is then updated with the first action result to obtain an updated input dictionary. A second container, running an image for a second one of the actions implemented in a second programming language, different than the first programming language, is instantiated, and the image is executed on the updated input dictionary to obtain a second action result; the updated input dictionary is further updated with the second action result to obtain a further updated input dictionary. The input dictionaries are independent of the programming languages.

Abstract: A method and system of determining whether a specification is an accurate representation of an application program interface (API) is provided. The specification is received electronically over a network. Service calls to be tested are identified based on the specification. A test case is created for each of the identified service calls. A sequence is created for the test cases. A test plan is generated based on the created sequence. The generated test plan is executed. Upon identifying an error in response to the executed test plan, a notification is generated, indicating that the specification is not an accurate representation of the API.

Abstract: Techniques for autonomously generating a code usage summary associated with a web application programming interface request are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, data from a data repository, wherein the evaluating is based on a defined machine learning process. Also, the computer-implemented method can comprise generating, by the system, a usage summary of the data, wherein the usage summary is based on a statistic derived from a web application programming interface request, and the web application programming interface request is associated with the data.

Abstract: Techniques for autonomously generating a code usage summary associated with a web application programming interface request are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, data from a data repository, wherein the evaluating is based on a defined machine learning process. Also, the computer-implemented method can comprise generating, by the system, a usage summary of the data, wherein the usage summary is based on a statistic derived from a web application programming interface request, and the web application programming interface request is associated with the data.