Abstract: An artificial document generation method generating a larger set of artificial documents which mimics a smaller set of source documents. The method may include deriving metadata parameters from source documents, each parameter characterizing the source documents and having more than one possible value. The deriving may include: determining which ratio of the source documents has each of the values, defining metadata ratios characteristic of the source documents; training a model, on the source documents, to output artificial documents; and running the model as trained thereby to output artificial documents and overriding at least some words generated by the model in the draft artificial documents. Overriding is configured to ensure that at least some of the ratios characteristic of the smaller set of source documents are retained in the larger set.

Abstract: A proof-of-concept (PoC) method comprising: on a networked platform, serving a population of enterprise end-users and a population of ISV end-users, on which PoCs are run, providing a PoC-defining user interface via which at least one enterprise end-user generates a definition of at least one PoC; and using a processor to automatically assess whether an individual machine learning model embodied in a body of code of an individual software product registered for an individual PoC, is suitable for the individual PoC as defined by the definition.

Abstract: A proof-of-concept (PoC) method comprising: on a networked platform, serving a population of enterprise end-users and a population of ISV end-users, on which PoCs are run, providing a PoC-defining user interface via which at least one enterprise end-user generates a definition of at least one PoC; and using a processor to automatically assess whether an individual machine learning model embodied in a body of code of an individual software product registered for an individual PoC, is suitable for the individual PoC as defined by the definition.

Abstract: A method for running proof-of-concepts for software solutions, including receiving, from an enterprise, an indication of network locations of servers in a production environment for a software solution selected from among plural candidates software solutions participating in a proof-of-concept running in a proof-of-concept (aka PoC) environment on a PoC platform; providing at least one recording, uploaded onto the platform, of traffic between the servers in the production environment; providing a mapping of the network locations to, respectively, PoC platform local network addresses of servers within the PoC environment; adapting the recording by replacing each occurrence of an individual one of the network locations, within the recording, with a PoC environment server local network PoC platform address to which the individual one was mapped, thereby to generate at least one adapted file; and replaying the at least one adapted file on the servers within the PoC environment.

Abstract: A proof-of-concept (PoC) method comprising: on a networked platform, serving a population of enterprise end-users and a population of ISV end-users, on which PoCs are run, providing a PoC-defining user interface via which at least one enterprise end-user generates a definition of at least one PoC; and using a processor to automatically assess whether an individual machine learning model embodied in a body of code of an individual software product registered for an individual PoC, is suitable for the individual PoC as defined by the definition.

Abstract: An artificial document generation method generating a larger set of artificial documents which mimics a smaller set of source documents. The method may include deriving metadata parameters from source documents, each parameter characterizing the source documents and having more than one possible value. The deriving may include: determining which ratio of the source documents has each of the values, defining metadata ratios characteristic of the source documents; training a model, on the source documents, to output artificial documents; and running the model as trained thereby to output artificial documents and overriding at least some words generated by the model in the draft artificial documents. Overriding is configured to ensure that at least some of the ratios characteristic of the smaller set of source documents are retained in the larger set.

Abstract: A proof-of-concept (PoC) method comprising: on a networked platform, serving a population of enterprise end-users and a population of ISV end-users, on which PoCs are run, providing a PoC-defining user interface via which at least one enterprise end-user generates a definition of at least one PoC; and using a processor to automatically assess whether an individual machine learning model embodied in a body of code of an individual software product registered for an individual PoC, is suitable for the individual PoC as defined by the definition.

Abstract: A method for running proof-of-concepts for software solutions, including receiving, from an enterprise, an indication of network locations of servers in a production environment for a software solution selected from among plural candidates software solutions participating in a proof-of-concept running in a proof-of-concept (aka PoC) environment on a PoC platform; providing at least one recording, uploaded onto the platform, of traffic between the servers in the production environment; providing a mapping of the network locations to, respectively, PoC platform local network addresses of servers within the PoC environment; adapting the recording by replacing each occurrence of an individual one of the network locations, within the recording, with a PoC environment server local network PoC platform address to which the individual one was mapped, thereby to generate at least one adapted file; and replaying the at least one adapted file on the servers within the PoC environment.