A model for identifying project sprint tasks based on their description

Abstract

The subject of research in this article is the identification of project sprint tasks. The purpose of the article is to find approaches to reducing the risks of not fulfilling sprint tasks. The article solves the following tasks: analyzing research on the classification and visualization of project tasks, developing an algorithm that can automatically classify text descriptions of sprint tasks, collecting and preparing a training sample of text descriptions of sprint tasks for training and testing the classification model, applying natural language processing methods to improve classification and ensure the accuracy of the results, validating the model on real data to assess the efficiency and accuracy of classification, and analyzing the results. The following methods have been used: machine learning methods for classification, text vectorization methods, methods for classifying text descriptions, natural language processing methods, methods for semantic analysis of task description text, methods for processing expert opinions. The following results were obtained: a comprehensive approach to using machine learning algorithms, including the collection and processing of textual descriptions of tasks, for classification and involvement of expert opinions to improve the quality of task perception by the project team. Text expressions were classified based on the Bayesian classifier and neural classifiers. A visual representation of the data was implemented. Semantic analysis of the text of the description and title of the tasks was performed. Data markup was obtained to classify the quality of the wording, which was performed by a team of experts. To measure the reliability of the obtained expert assessments, we calculated Cohen's kappa coefficient for each pair of markers. According to the experimental results, the accuracy of the Bayesian classifier is 70%. For the classifier based on deep learning, a neural network for binary classification based on the transformer architecture was selected. The neural network was trained using the Python programming language and deep learning frameworks. The result is a classifier that gives an accuracy score of 83% on a test dataset, which is a good result for a small dataset and data with conflicting labels. Conclusions: The analysis of textual data confirms that the existing data in the tracking system is incomplete and contains abbreviations, conventions, and slang. The results show that the assessment of the quality of the wording is determined by the level of expert knowledge of the specifics and context of the project, while increasing the number of experts has almost no effect on the result. In further research, it is recommended to test the hypothesis that the effectiveness of the classifier depends on the specific project and the use of unsupervised learning methods for the task of identifying the quality of formulations.