International Business Machines Corporation

Alexandre Quemy

Umit Bektas

Adam Nowotarski

Systems and method for automatically assigning cooperative platform tasks to appropriate participants are disclosed. In embodiments, a method includes receiving new task data for a new task posted to a remote server; transforming the new task data by natural language processing to produce transformed new task data; representing the new task as a vector in a vector space based on the transformed new task data, wherein the vector space includes representations of completed tasks, and the completed tasks are associated with respective participants; calculating distances between the new task and the respective completed tasks represented in the vector space; ranking the respective participants based on the distances between the new task and the completed tasks associated with respective participants; determining a select participant of the respective participants to be assigned to the new task based on the ranking; and initiating automatic assignment of the new task to the select participant.

Automatic assignment of cooperative platform tasks

A method, system, and computer program product provide automatic resolution of coding issues by applying code modifications to an application to generate a modified application, and then applying static analytic tools to the modified application to identify coding problems in the modified application related to at least one code modification, where the coding problems are evaluated using a first machine learning model to identify a subset of coding problems meeting a first project relevancy requirement.

Cloud application to automatically detect and solve issues in a set of code base changes using reinforcement learning and rule-based learning

Described are techniques for predictive regression testing. The techniques include a method comprising constructing a call graph of a modified codebase including at least one modified node corresponding to a modified function. The method further comprises generating a subset of codebase tests by removing respective codebase tests that do not call for the at least one modified node. The method further comprises generating respective partial Abstract Syntax Trees (AST) sequences for relevant test paths in the call graph that connect the at least one modified node to one of the subset of codebase tests. The method further comprises inputting, to a machine learning model, the respective partial AST sequences, and generating, based on output from the machine learning model, predicted regression testing results for the relevant test paths.

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Cracow, Poland

Alexandre Quemy