Utilizing artificial intelligence to improve productivity of software development and information technology operations (DevOps)

A device, comprising:

• one or more memories;
• and
• one or more processors, communicatively coupled to the one or more memories, configured to:
• receive data associated with a software development platform;
• correlate the data associated with the software development platform to generate correlated data;
• train a deep neural network model, with the correlated data, to generate a software impact analyzer model;
• train an artificial intelligence model, with the correlated data, to generate a software development behavior model,
• wherein the software development behavior model includes:
• a baseline vector model,
• the baseline vector model having a quantity of dimensions corresponding to a quantity of software development attributes including one or more of:
• software build attributes for one or more developers of a plurality of developers, or
• defect quality attributes, and
• a distance measures based rewards and penalty model,
• the distance measure based rewards and penalty model including a developer vector for each developer of the plurality of developers;
• train a machine learning model, with the correlated data, to generate a defect lifecycle optimizer model;
• receive data identifying a new software requirement or a defect associated with the software development platform;
• process the data identifying the new software requirement or the defect, with the software impact analyzer model, to identify a file or a module impacted by the new software requirement or the defect;
• process data identifying the file or the module impacted by the new software requirement or the defect, with the software development behavior model, to identify a developer to handle the new software requirement or the defect;
• process the data identifying the defect, with the defect lifecycle optimizer model, to identify duplicates of the defect and a correction to the defect;
• and
• perform one or more actions based on data identifying one or more of:
• the file or the module impacted by the new software requirement or the defect,
• the developer to handle the new software requirement or the defect,
• the duplicates of the defect, or
• the correction to the defect.

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors, cause the one or more processors to:
• receive data associated with a software development platform;
• correlate the data associated with the software development platform to generate correlated data;
• obtain a first model that was trained based on the correlated data;
• obtain a second model that was trained based on the correlated data,
• wherein the second model includes:
• a baseline vector model,
• the baseline vector model having a quantity of dimensions corresponding to a quantity of software development attributes including one or more of:
• software build attributes for one or more developers of a plurality of developers, or
• defect quality attributes, and
• a distance measures based rewards and penalty model,
• the distance measure based rewards and penalty model including a developer vector for each developer of the plurality of developers;
• receive data identifying a new software requirement associated with the software development platform;
• process the data identifying the new software requirement, with the first model, to identify a file or a module impacted by the new software requirement;
• process data identifying the file or the module impacted by the new software requirement, with the second model, to identify a developer to handle the new software requirement;
• and
• perform one or more actions based on the data identifying the file or the module impacted by the new software requirement and data identifying the developer to handle the new software requirement.