Identification Of Non-functional Requirements Research Articles

Classification of functional and non-functional requirement in software requirement using Word2vec and fast Text

One of the needs in adopting a crowdsourcing approach in software requirement system (SRS) is to be able to perform text analytics to gain insight or knowledge from the crowd’s feedback. One of the expected text analytic tasks is to be able of analyzing the feedback automatically; such as, whether the feedback concerns about the functional requirement (FR) and non-functional requirements (NFR). To automatically do the FR and NFR identification, one can treat the problem as a text classification task. Performing automatic identification requires features (word representation) that contain sufficient information which can be used to do the identification. Thus, the types of features can be considered as an important step in performing automatic identification of FR and NFR. In this study, the RE’17 dataset challenge is used as the dataset. Using the dataset, we will like to find out the effect of word embedding against traditional features (such as bag-of-words) in NFR and FR classification. In addition, we also want to find out whether is necessary to use a complex neural classifier to obtain the best performance of NFR and FR classification. Based on the obtained results, using fastText seems to be the promising classification model since the model obtained the highest Fl-score of 92.8%.

WITHDRAWN: A Review of Machine Learning Algorithms for Identification and Classification of Non-Functional Requirements

Abstract Context Recent developments in requirements engineering (RE) methods have seen a surge in using machine-learning (ML) algorithms to solve some difficult RE problems. One such problem is identification and classification of non-functional requirements (NFRs) in requirements documents. ML-based approaches to this problem have shown to produce promising results, better than those produced by traditional natural language processing (NLP) approaches. Yet, a systematic understanding of these ML approaches is still lacking. Method This article reports on a systematic review of 24 ML-based approaches for identifying and classifying NFRs. Directed by three research questions, this article aims to understand what ML algorithms are used in these approaches, how these algorithms work and how they are evaluated. Results (1) 16 different ML algorithms are found in these approaches; of which supervised learning algorithms are most popular. (2) All 24 approaches have followed a standard process in identifying and classifying NFRs. (3) Precision and recall are the most used matrices to measure the performance of these approaches. Finding The review finds that while ML-based approaches have the potential in the classification and identification of NFRs, they face some open challenges that will affect their performance and practical application. Impact The review calls for the close collaboration between RE and ML researchers, to address open challenges facing the development of real-world ML systems. Significance The use of ML in RE opens up exciting opportunities to develop novel expert and intelligent systems to support RE tasks and processes. This implies that RE is being transformed into an application of modern expert systems.

Identification of non-functional requirements in textual specifications: A semi-supervised learning approach

Context Early detection of non-functional requirements (NFRs) is crucial in the evaluation of architectural alternatives starting from initial design decisions. The application of supervised text categorization strategies for requirements expressed in natural language has been proposed in several works as a method to help analysts in the detection and classification of NFRs concerning different aspects of software. However, a significant number of pre-categorized requirements are needed to train supervised text classifiers, which implies that analysts have to manually assign categories to numerous requirements before being able of accurately classifying the remaining ones. Objective We propose a semi-supervised text categorization approach for the automatic identification and classification of non-functional requirements. Therefore, a small number of requirements, possibly identified by the requirement team during the elicitation process, enable learning an initial classifier for NFRs, which could successively identify the type of further requirements in an iterative process. The goal of the approach is the integration into a recommender system to assist requirement analysts and software designers in the architectural design process. Method Detection and classification of NFRs is performed using semi-supervised learning techniques. Classification is based on a reduced number of categorized requirements by taking advantage of the knowledge provided by uncategorized ones, as well as certain properties of text. The learning method also exploits feedback from users to enhance classification performance. Results The semi-supervised approach resulted in accuracy rates above 70%, considerably higher than the results obtained with supervised methods using standard collections of documents. Conclusion Empirical evidence showed that semi-supervision requires less human effort in labeling requirements than fully supervised methods, and can be further improved based on feedback provided by analysts. Our approach outperforms previous supervised classification proposals and can be further enhanced by exploiting feedback provided by analysts.