Abstract
A significant challenge of managing successful engineering projects is to know their status at any time. This paper describes the background of engineering projects’ progress and presents an automated approach based on machine learning (ML) algorithms (e.g., KNN, random forest, and decision tree). The information required to measure engineering activities’ progress is extracted from engineering artifacts and subsequently analyzed and interpreted concerning the project’s progress. The approach integrates information from previous projects by considering historical data using ML algorithms and actual unfinished artifacts to determine the degree of completion.
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