Abstract: A machine learning (ML) based code transformation system that transforms a source programming code developed using a source library for execution on a source platform into remediated code for execution on a target platform is disclosed. Metadata extracted from the source programming code is used to detect the source programming language, source libraries, and the source platform. The metadata also enables modularizing the source programming code based on the functionality and identifying a node from a plurality of nodes in a communication network to execute the various source code modules. A similarity map is generated mapping the source libraries to the target libraries and the source code modules that are incompatible with the target platform are identified and remediated with similar target code modules using the similarity map.

Abstract: A device may receive developer profile data identifying developers associated with different technology communities and technology domains and may assign attributes and weights to the developer profile data to generate weighted developer profile data. The device may utilize a first machine learning model, with the weighted developer profile data, to calculate similarity indexes identifying developers associated with multiple technology communities, and may process the weighted developer profile data and the similarity indexes, with a second machine learning model, to calculate developer activity scores for the technology domains and for developer classifications. The device may identify, based on the developer activity scores, a particular developer profile with a greatest developer activity score, and may process the particular developer profile, with a third machine learning model, to determine an engagement strategy for addressing the particular developer.

Abstract: A dynamic application testing and scoring system that tests applications under various tenets and categorizes the applications to be published, to be further reviewed or rejected. The results from administering a plurality of tests to an application are analyzed by applying rulesets that pertain to criteria under each of the tenets. An application score is determined from the analysis using weights associated with the tenets, priority levels of the criteria and the severity levels of the rulesets. The application score identifies a position for the application on a scoring scale relative to two trust threshold values. The application is categorized based on the position. Feedback regarding the categorization is received and the trust threshold values on the scoring scale can be adjusted if the categorization in the feedback is different from the categorization produced by the scoring system.

Abstract: An Artificial Intelligence (AI) based payments processing system analyzes data from a user's purchase invoice to identify a combination of payment modes to be used to pay for the user's purchases thereby maximizing the savings to the user while accounting for the user preferences. A savings oriented model including a mix and match model and a dynamic decision tree scoring model generates a savings based best offer suggesting the payment modes to be used by analyzing the user's purchases based on the offers associated with the purchased products. A user behavior model generates a user behavior based best offer by further accounting for user preferences. Based on the user's autopay settings, the payments processing system may automatically initiate payment for the user's purchases or may display the best offers to the user before initiating the payments.

Abstract: A dynamic application testing and scoring system that tests applications under various tenets and categorizes the applications to be published, to be further reviewed or rejected. The results from administering a plurality of tests to an application are analyzed by applying rulesets that pertain to criteria under each of the tenets. An application score is determined from the analysis using weights associated with the tenets, priority levels of the criteria and the severity levels of the rulesets. The application score identifies a position for the application on a scoring scale relative to two trust threshold values. The application is categorized based on the position. Feedback regarding the categorization is received and the trust threshold values on the scoring scale can be adjusted if the categorization in the feedback is different from the categorization produced by the scoring system.