Abstract: A system and method for estimating software project delivery details is disclosed. The disclosed method and system accurately predicts project delivery details (e.g., person-days, effort, full-time equivalent (FTE), etc.) from input text (e.g., text describing a software project requirements) and project input parameters (e.g., variables, such as logical entities, transactions, individual/team proficiency, team level, duration of project, technology, industry domain, etc.) corresponding to a project scenario. In addition to predicting project delivery details, the disclosed system and method can provide a what-if analysis engine that allows a user to understand the possibilities of project scenarios having certain output.

Abstract: A device may receive requirement data identifying a requirement for modification of software code, and may process the requirement data, with a machine learning model, to identify entities and intents in the software code and to generate a query. The device may process the query, with a code locator model, to encode text of the query into high-dimensional vectors and to identify a semantic similarity between the high-dimensional vectors and code text. The device may process the query, the semantic similarity, and the code text, with a code developer model, to generate metadata, and may utilize the metadata to identify an identifier associated with the software code. The device may determine, utilizing the identifier, a portion of the software code, and may modify the portion of the software code based on the query to generate modified software code. The device may perform actions based on the modified software code.

Abstract: A device may receive requirement data identifying a requirement for modification of software code, and may process the requirement data, with a machine learning model, to identify entities and intents in the software code and to generate a query. The device may process the query, with a code locator model, to encode text of the query into high-dimensional vectors and to identify a semantic similarity between the high-dimensional vectors and code text. The device may process the query, the semantic similarity, and the code text, with a code developer model, to generate metadata, and may utilize the metadata to identify an identifier associated with the software code. The device may determine, utilizing the identifier, a portion of the software code, and may modify the portion of the software code based on the query to generate modified software code. The device may perform actions based on the modified software code.

Abstract: A device may obtain process data relating to a set of processes. The device may process the process data to generate a process analysis model. The device may classify, using the process analysis model, a particular process into a particular class of a set of classes of the process analysis model. The device may automatically assess the particular process based on the particular class, wherein a set of assessment parameters for assessing the particular process is selected based on the particular class, and wherein an assessment score is assigned to the particular process based on values for the set of assessment parameters. The device may determine, based on the assessment score and the particular class, an automation recommendation for the particular process. The device may automatically complete the particular process using a particular tool based on determining the automation recommendation.

Abstract: A device may obtain process data relating to a set of processes. The device may process the process data to generate a process analysis model. The device may classify, using the process analysis model, a particular process into a particular class of a set of classes of the process analysis model. The device may automatically assess the particular process based on the particular class, wherein a set of assessment parameters for assessing the particular process is selected based on the particular class, and wherein an assessment score is assigned to the particular process based on values for the set of assessment parameters. The device may determine, based on the assessment score and the particular class, an automation recommendation for the particular process. The device may automatically complete the particular process using a particular tool based on determining the automation recommendation.