Causality Management and Analysis in Requirement Manuscript for Software Designs

Abstract

For software design tasks involving natural language, the results of a causal investigation provide valuable and robust semantic information, especially for identifying key variables during product (software) design and product optimization. As the interest in analytical data science shifts from correlations to a better understanding of causality, there is an equal task focused on the accuracy of extracting causality from textual artifacts to aid requirement engineering (RE) based decisions. This thesis focuses on identifying, extracting, and classifying causal phrases using word and sentence labeling based on the Bi-directional Encoder Representations from Transformers (BERT) deep learning language model and five machine learning models. The aim is to understand the form and degree of causality based on their impact and prevalence in RE practice. Methodologically, our analysis is centered around RE practice, and we considered 12,438 sentences extracted from 50 requirement engineering manuscripts (REM) for training our machine models. Our research reports that causal expressions constitute about 32% of sentences from REM. We applied four evaluation metrics, namely recall, accuracy, precision, and F1, to assess our machine models’ performance and accuracy to ensure the results’ conformity with our study goal. Further, we computed the highest model accuracy to be 85%, attributed to Naive Bayes. Finally, we noted that the applicability and relevance of our causal analytic framework is relevant to practitioners for different functionalities, such as generating test cases for requirement engineers and software developers and product performance auditing for management stakeholders.