407,213 research outputs found

    What Works Better? A Study of Classifying Requirements

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    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

    Domain-specific reasoning for method engineering based on Toulmin's argumentation theory

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    Methods describe and embody a broad range of relevant knowledge of enterprises. Usually they have to account for requirements stated by a multitude of various stakeholders. These are typically those that are in charge of business related actions and those that are in charge to support such actions with an IT-Infrastructure. The statement of requirements as well as the validation of methods and in particular process models with respect to those requirements relies drastically on natural language. Natural language seems to be a substantial component to explain and to give an understanding about process models or certain aspects of it. This fact requires closing the gap between the natural language and the respective modelling language. This paper proposes argumentative method engineering for purposefully depicting design decisions and convictions for method engineering through arguments. The approach is derived from Toulmin’s Argumentation Model and explicates the process of negotiating with various stakeholders. So, a model, depicting a method, specified by means of argumentative method engineering, not just includes the claims about a certain domain, it further justifies these claims by referring to already established knowledge. While it can’t be ensured that certain requirements are considered in future project, if the reasons for design decisions of method engineering are transcribed in natural language text, but the semi- formalising of arguments regarding these methods allows such an assurance. So the argumentative approach enables the sophisticated management and reuse of knowledge during the development and extension of methods. The approach is evaluated using a case study, in which a software development method was outsourced to contractors

    Consistency Checking of Natural Language Temporal Requirements using Answer-Set Programming

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    Successful software engineering practice requires high quality requirements. Inconsistency is one of the main requirement issues that may prevent software projects from being success. This is particularly onerous when the requirements concern temporal constraints. Manual checking whether temporal requirements are consistent is tedious and error prone when the number of requirements is large. This dissertation addresses the problem of identifying inconsistencies in temporal requirements expressed as natural language text. The goal of this research is to create an efficient, partially automated, approach for checking temporal consistency of natural language requirements and to minimize analysts\u27 workload. The key contributions of this dissertation are as follows: (1) Development of a partially automated approach for checking temporal consistency of natural language requirements. (2) Creation of a formal language Temporal Action Language (TeAL), which provide a means to represent natural language requirements precisely and unambiguously. (3) Development of a front end to semi-automatically translate natural language requirements into TeAL. (4) Development of a translator from TeAL to the ASP language. Validation results to date show that the front end tool makes the task of translating natural language requirements into TeAL more accurate and efficient, and the translator generates ASP programs that correctly detect the inconsistencies in the requirements

    Domain-specific reasoning for method engineering based on Toulmin's argumentation theory

    Get PDF
    Methods describe and embody a broad range of relevant knowledge of enterprises. Usually they have to account for requirements stated by a multitude of various stakeholders. These are typically those that are in charge of business related actions and those that are in charge to support such actions with an IT-Infrastructure. The statement of requirements as well as the validation of methods and in particular process models with respect to those requirements relies drastically on natural language. Natural language seems to be a substantial component to explain and to give an understanding about process models or certain aspects of it. This fact requires closing the gap between the natural language and the respective modelling language. This paper proposes argumentative method engineering for purposefully depicting design decisions and convictions for method engineering through arguments. The approach is derived from Toulmin’s Argumentation Model and explicates the process of negotiating with various stakeholders. So, a model, depicting a method, specified by means of argumentative method engineering, not just includes the claims about a certain domain, it further justifies these claims by referring to already established knowledge. While it can’t be ensured that certain requirements are considered in future project, if the reasons for design decisions of method engineering are transcribed in natural language text, but the semi- formalising of arguments regarding these methods allows such an assurance. So the argumentative approach enables the sophisticated management and reuse of knowledge during the development and extension of methods. The approach is evaluated using a case study, in which a software development method was outsourced to contractors

    A Process Framework for Semantics-aware Tourism Information Systems

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    The growing sophistication of user requirements in tourism due to the advent of new technologies such as the Semantic Web and mobile computing has imposed new possibilities for improved intelligence in Tourism Information Systems (TIS). Traditional software engineering and web engineering approaches cannot suffice, hence the need to find new product development approaches that would sufficiently enable the next generation of TIS. The next generation of TIS are expected among other things to: enable semantics-based information processing, exhibit natural language capabilities, facilitate inter-organization exchange of information in a seamless way, and evolve proactively in tandem with dynamic user requirements. In this paper, a product development approach called Product Line for Ontology-based Semantics-Aware Tourism Information Systems (PLOSATIS) which is a novel hybridization of software product line engineering, and Semantic Web engineering concepts is proposed. PLOSATIS is presented as potentially effective, predictable and amenable to software process improvement initiatives

    Automatic classification of requirements based on convolutional neural networks

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    The results of the requirements engineering process are predominantly documented in natural language requirements specifications. Besides the actual requirements, these documents contain additional content such as explanations, summaries, and figures. For the later use of requirements specifications, it is important to be able to differentiate between legally relevant requirements and other auxiliary content. Therefore, one of our industry partners demands the requirements engineers to manually label each content element of a requirements specification as "requirement" or "information". However, this manual labeling task is time-consuming and error-prone. In this paper, we present an approach to automatically classify content elements of a natural language requirements specification as "requirement" or "information". Our approach uses convolutional neural networks. In an initial evaluation on a real-world automotive requirements specification, our approach was able to detect requirements with a precision of 0.73 and a recall of 0.89. The approach increases the quality of requirements specifications in the sense that it discriminates important content for following activities (e.g., which parts of the specification do I need to test?)

    Requirements analysis of the VoD application using the tools in TRADE

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    This report contains a specification of requirements for a video-on-demand (VoD) application developed at Belgacom, used as a trial application in the 2RARE project. The specification contains three parts: an informal specification in natural language; a semiformal specification consisting of a number of diagrams intended to illustrate the informal specification; and a formal specification that makes the requiremants on the desired software system precise. The informal specification is structured in such a way that it resembles official specification documents conforming to standards such as that of IEEE or ESA. The semiformal specification uses some of the tools in from a requirements engineering toolkit called TRADE (Toolkit for Requirements And Design Engineering). The purpose of TRADE is to combine the best ideas in current structured and object-oriented analysis and design methods within a traditional systems engineering framework. In the case of the VoD system, the systems engineering framework is useful because it provides techniques for allocation and flowdown of system functions to components. TRADE consists of semiformal techniques taken from structured and object-oriented analysis as well as a formal specification langyage, which provides constructs that correspond to the semiformal constructs. The formal specification used in TRADE is LCM (Language for Conceptual Modeling), which is a syntactically sugared version of order-sorted dynamic logic with equality. The purpose of this report is to illustrate and validate the TRADE/LCM approach in the specification of distributed, communication-intensive systems

    Conceptualization of the use of Artificial Intelligence for Interdependencies Analysis in Requirements Engineering

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    The efficiency in product development is largely determined by the quality of the requirements and the ability of the product design and production planner to analyze them. Interdependencies between multiple requirements identified at an early stage enable a sustainable design of the product as well as the corresponding production system by increasing process efficiency as well as the effectiveness of development processes. However, the necessary analysis of complex interdependencies between requirements of a product and the corresponding production system is time-consuming, error-prone, and highly inefficient when performed manually. Current development processes are based on such manual processes for analyzing requirements in natural language and must therefore be adapted. This paper describes a methodical approach based on a semi-systematic literature review making the complexity of the interdependencies manageable by using existing approaches and methods in the field of model-based systems engineering (MBSE) as well as natural language processing (NLP). Thereby, a transition from informal requirements represented in natural language to analyzable and structured information, which enable interdependencies modeling for requirement chains, is described. A corresponding framework for analyzing interdependencies in the requirements engineering process is derived

    ReForm: A Tool for Rapid Requirements Formalization

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    Formal methods practices can sometimes be challenging to adopt in industrial environments. On the other hand, the need for formalization and verification in the design of complex systems is now more evident than ever. To the end of easing integration of formal methods in industrial model based system engineering workflows, UTRC Ireland has developed a tool aiming to render requirements formalization as effortless as possible to the industrial engineer. The developed approach is an end-to-end solution, starting with natural language requirements as input and going all the way down to auto-generated monitors in MATLAB / Simulink. We employ natural language processing and machine learning techniques for (semi-)automatic pattern extraction from requirements, which drastically reduces the required formalization workload for both legacy and new requirements. For monitor generation, we provide our own approach which outperforms existing state-of-the-art tools by orders of magnitude in some cases
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