Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work

TL;DR

This paper explores the use of deep learning techniques to detect requirements smells in software engineering, aiming to improve detection accuracy over classical NLP methods by addressing generalization issues.

Contribution

It introduces a manually labeled dataset and applies ensemble learning, deep learning, and transfer learning to enhance requirements smell detection beyond traditional NLP approaches.

Findings

Dataset is unbalanced, affecting model training

Models tend to overfit due to limited data

Future work includes dataset balancing and noise reduction

Abstract

Requirements Engineering (RE) is the initial step towards building a software system. The success or failure of a software project is firmly tied to this phase, based on communication among stakeholders using natural language. The problem with natural language is that it can easily lead to different understandings if it is not expressed precisely by the stakeholders involved, which results in building a product different from the expected one. Previous work proposed to enhance the quality of the software requirements detecting language errors based on ISO 29148 requirements language criteria. The existing solutions apply classical Natural Language Processing (NLP) to detect them. NLP has some limitations, such as domain dependability which results in poor generalization capability. Therefore, this work aims to improve the previous work by creating a manually labeled dataset and using ensemble learning, Deep Learning (DL), and techniques such as word embeddings and transfer learning to overcome the generalization problem that is tied with classical NLP and improve precision and recall metrics using a manually labeled dataset. The current findings show that the dataset is unbalanced and which class examples should be added more. It is tempting to train algorithms even if the dataset is not considerably representative. Whence, the results show that models are overfitting; in Machine Learning this issue is solved by adding more instances to the dataset, improving label quality, removing noise, and reducing the learning algorithms complexity, which is planned for this research.