A method, comprising: receiving, by a device, data associated with a software development platform; correlating, by the device, the data associated with the software development platform to generate correlated data, wherein the data associated with the software development platform is transformed from an unstructured format to a structured format that corresponds to the correlated data; training, by the device, a deep neural network model, with the correlated data, to generate a software impact analyzer model, wherein the software impact analyzer model includes a long short-term memory (LSTM) model that utilizes natural language processing techniques, and is used by the device to process data identifying a new software requirement or a defect to identify a file and/or a module impacted by the new software requirement or the defect; training, by the device, an artificial intelligence model, with the correlated data, to generate a software development behavior model for processing data identifying the file and/or the module impacted by the new software requirement or the defect in order to identify a software developer to handle the new software requirement or the defect, wherein the software development behavior model includes: a baseline vector model which is created corresponding to a baseline vector that represents software development behavior for each of multiple files and/or modules associated with the correlated data, and a distance measures based rewards and penalty model, wherein for each of the multiple files and/or modules associated with the correlated data, a developer vector for each developer that has a behavioral history associated with that file and/or module, is created; receiving, by the device, data identifying a new software requirement or a defect associated with the software development platform; processing, by the device, 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; processing, by the device, 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; and performing, by the device, one or more actions based on the data identifying the file or the module impacted by the new software requirement or the defect and data identifying the developer to handle the new software requirement or the defect. receiving, by a device, data associated with a software development platform; correlating, by the device, the data associated with the software development platform to generate correlated data, wherein the data associated with the software development platform is transformed from an unstructured format to a structured format that corresponds to the correlated data; training, by the device, a deep neural network model, with the correlated data, to generate a software impact analyzer model, wherein the software impact analyzer model includes a long short-term memory (LSTM) model that utilizes natural language processing techniques, and is used by the device to process data identifying a new software requirement or a defect to identify a file and/or a module impacted by the new software requirement or the defect; training, by the device, an artificial intelligence model, with the correlated data, to generate a software development behavior model for processing data identifying the file and/or the module impacted by the new software requirement or the defect in order to identify a software developer to handle the new software requirement or the defect, wherein the software development behavior model includes: a baseline vector model which is created corresponding to a baseline vector that represents software development behavior for each of multiple files and/or modules associated with the correlated data, and a distance measures based rewards and penalty model, wherein for each of the multiple files and/or modules associated with the correlated data, a developer vector for each developer that has a behavioral history associated with that file and/or module, is created; a baseline vector model which is created corresponding to a baseline vector that represents software development behavior for each of multiple files and/or modules associated with the correlated data, and a distance measures based rewards and penalty model, wherein for each of the multiple files and/or modules associated with the correlated data, a developer vector for each developer that has a behavioral history associated with that file and/or module, is created; receiving, by the device, data identifying a new software requirement or a defect associated with the software development platform; processing, by the device, 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; processing, by the device, 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; and performing, by the device, one or more actions based on the data identifying the file or the module impacted by the new software requirement or the defect and data identifying the developer to handle the new software requirement or the defect.