What Works Better? A Study of Classifying Requirements

Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.Comment: 7 pages, the 25th IEEE International Conference on Requirements Engineering (RE'17

Designing requirements engineering research

Engineering sciences study different different topics than natural sciences, and utility is an essential factor in choosing engineering research problems. But despite these differences, research methods for the engineering sciences are no different than research methods for any other kind of science. At most there is a difference in emphasis. In the case of requirements engineering research - and more generally software engineering research - there is a confusion about the relative roles of research and about design and the methods appropriate for each of these activities. This paper analyzes these roles and provides a classification of research methods that can be used in any science—engineering or otherwise

Evidence-Based Structuring and Evaluation of Empirical Research in Requirements Engineering - Fundamentals, Framework, Research Map

The objective of the contribution is to develop and motivate an approach of structuring, evaluating, and representing empirical research results regarding requirements engineering. Therefore, the authors develop a framework in order to organize the area of interest. The use of this framework and an evidence-based classification system allows us to develop a research map which helps to structure identified empirical research while enabling the derivation of further research needs. Additionally, it supports the selection of methods, techniques, etc. in requirements engineering practice

An overview of very high level software design methods

Very High Level design methods emphasize automatic transfer of requirements to formal design specifications, and/or may concentrate on automatic transformation of formal design specifications that include some semantic information of the system into machine executable form. Very high level design methods range from general domain independent methods to approaches implementable for specific applications or domains. Applying AI techniques, abstract programming methods, domain heuristics, software engineering tools, library-based programming and other methods different approaches for higher level software design are being developed. Though one finds that a given approach does not always fall exactly in any specific class, this paper provides a classification for very high level design methods including examples for each class. These methods are analyzed and compared based on their basic approaches, strengths and feasibility for future expansion toward automatic development of software systems

Classification System for Impedance Spectra

This thesis documents research, methods, and results to satisfy the requirements for the M.S. degree in Electrical Engineering at the University of Tennessee. This thesis explores two primary steps for proper classification of impedance spectra: data dimension reduction and effectiveness of similarity/dissimilarity measure comparison in classification. To understand the data characteristics and classification thresholds, a circuit model analysis for simulation and unclassifiable determination is studied. The research is conducted using previously collected data of complex valued impedance measurements taken from 1844 similar devices. The results show a classification system capable of proper classification of 99% of data samples with well-separated data and approximately 85% using the full range of data available to this research

Modeling Users Feedback Using Bayesian Methods for Data-Driven Requirements Engineering

Data-driven requirements engineering represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. App developers now receive abundant user feedback from user comments in app stores and social media, i.e., explicit feedback, to feedback from usage data and system logs, i.e, implicit feedback. In this dissertation, we describe two novel Bayesian approaches that utilize the available user\u27s to support requirements decisions and activities in the context of applications delivered through software marketplaces (web and mobile). In the first part, we propose to exploit implicit user feedback in the form of usage data to support requirements prioritization and validation. We formulate the problem as a popularity prediction problem and present a novel Bayesian model that is highly interpretable and offers early-on insights that can be used to support requirements decisions. Experimental results demonstrate that the proposed approach achieves high prediction accuracy and outperforms competitive models. In the second part, we discuss the limitations of previous approaches that use explicit user feedback for requirements extraction, and alternatively, propose a novel Bayesian approach that can address those limitations and offer a more efficient and maintainable framework. The proposed approach (1) simplifies the pipeline by accomplishing the classification and summarization tasks using a single model, (2) replaces manual steps in the pipeline with unsupervised alternatives that can accomplish the same task, and (3) offers an alternative way to extract requirements using example-based summaries that retains context. Experimental results demonstrate that the proposed approach achieves equal or better classification accuracy and outperforms competitive models in terms of summarization accuracy. Specifically, we show that the proposed approach can capture 91.3% of the discussed requirement with only 19% of the dataset, i.e., reducing the human effort needed to extract the requirements by 80%

A Taxonomy of Current Issues in Requirements Engineering

The contents of a requirements specification is presented in light of the consensus reached both theoreticians and practitioners. The desirable properties of a requirements specification justified from a functionalist viewpoint and it is suggested that changes in the way one uses requirements may alter the relative significance of different properties. Finally, a classification requirements specification techniques is proposed and used as a backdrop against which current issues in the requirements engineering field are examined. The emphasis is on identifying general problem areas rather than offering the reader a literature survey. The paper shows that, despite significant growth, the requirements area still face a number of important unresolved issues including the need for: broader formal foundation for both functional and non-functional requirements, greater degree of formality and automation, new requirements development methods, and higher level of integration in the overall design process

A Taxonomy of Requirements Specification Techniques

A taxonomy is introduced and used as a backdrop against which current state-of-the-art in the requirements engineering field is reviewed. The emphasis is on identifying general trends and issues rather than offering the reader a literature survey. The contents of a requirements specification is presented in light of the consensus reached by both theoreticians and practitioners. The desirable proper ties of a requirements may alter the relative significance of difference properties. Finally, the classification of requirements specification techniques is approached from a total system design perspective. The paper shows that, despite significant growth, the requirements area still faces a number of important unresolved issues including the need for: broader formal foundation for both functional and non-functional requirements, greater degree of formality and automation, new requirements development methods, and higher level of integration in the overall design process