Abstract-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 Naïve Bayes for the sub-classification of NFRs.
Requirements engineers create large numbers of artifacts when eliciting and documenting requirements. They need to navigate through these artifacts and display information details at points of interest for reviewing or editing information. [Question/problem] Traditional visualization mechanisms such as scrolling and opening multiple windows lose context when navigating and can be cumbersome to use, hence. On the other hand, focus+context approaches can display details in context, but they distort the data shown (e.g., fisheye views) or result in a large display canvas which again requires scrolling (e.g., zooming in ADORA). [Principal ideas/results] We are developing a novel method for displaying just the information needed to perform an intended task. Our method partitions the available screen space into regions. The boundaries of regions are simulated with a model consisting of virtual magnetic balls and springs that behaves like a physical system. This model supports the requirements engineer in selecting how the relevant information should be displayed. [Contribution]
When designing a new presentation front-end called FlexiView for requirements modeling tools, we encountered a general problem: designing such an interface requires a lot of experimentation which is costly when the code of the tool needs to be adapted for every experiment. On the other hand, when using simplified user interface (UI) tools, the results are difficult to generalize. To improve this situation, we are developing an UI experimentation tool which is based on so-called ImitGraphs. ImitGraphs can act as a simple, but accurate substitute for a modeling tool. In this paper, we define requirements for such an UI experimentation tool based on an analysis of the features of existing requirements modeling tools.
Requirements documentation is essential for developing software systems of non-trivial size. The cost of creating and maintaining documentation artifacts in terms of time and effort is significantly influenced by the tools with which engineers view, navigate and edit documentation artifacts. However, there is not much evidence about how well documentation tools actually support engineers, particularly when dealing with artifacts that are larger than the available display screen and with multiple artifacts at the same time. Therefore, we conducted an exploratory study based on 29 interviews with software practitioners to understand the current practice of presenting and manipulating artifacts in documentation tools, and how practitioners deal with the challenges encountered. Our study shows that a significant number of artifacts cannot be viewed entirely, even on large screens. Moreover, more than half of the participants use four or more artifacts concurrently. Nevertheless, current tools only provide primitive capabilities for handling concurrent and large artifacts, thus forcing engineers to create, for example, mental images of the currently used artifacts or use workarounds such as hanging printouts to the wall. Our results may trigger new research and help improve requirements engineering tools. Abstract-Requirements documentation is essential for developing software systems of non-trivial size. The cost of creating and maintaining documentation artifacts in terms of time and effort is significantly influenced by the tools with which engineers view, navigate and edit documentation artifacts. However, there is not much evidence about how well documentation tools actually support engineers, particularly when dealing with artifacts that are larger than the available display screen and with multiple artifacts at the same time. Therefore, we conducted an exploratory study based on 29 interviews with software practitioners to understand the current practice of presenting and manipulating artifacts in documentation tools, and how practitioners deal with the challenges encountered. Our study shows that a significant number of artifacts cannot be viewed entirely, even on large screens. Moreover, more than half of the participants use four or more artifacts concurrently. Nevertheless, current tools only provide primitive capabilities for handling concurrent and large artifacts, thus forcing engineers to create, for example, mental images of the currently used artifacts or use workarounds such as hanging printouts to the wall. Our results may trigger new research and help improve requirements engineering tools.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.