Natural Language Requirements Specifications Research Articles

ReqGen: Keywords-Driven Software Requirements Generation

Software requirements specification is undoubtedly critical for the whole software life-cycle. Currently, writing software requirements specifications primarily depends on human work. Although massive studies have been proposed to speed up the process via proposing advanced elicitation and analysis techniques, it is still a time-consuming and error-prone task, which needs to take domain knowledge and business information into consideration. In this paper, we propose an approach, named ReqGen, which can provide further assistance by automatically generating natural language requirements specifications based on certain given keywords. Specifically, ReqGen consists of three critical steps. First, keywords-oriented knowledge is selected from the domain ontology and is injected into the basic Unified pre-trained Language Model (UniLM) for domain fine-tuning. Second, a copy mechanism is integrated to ensure the occurrence of keywords in the generated statements. Finally, a requirements-syntax-constrained decoding is designed to close the semantic and syntax distance between the candidate and reference specifications. Experiments on two public datasets from different groups and domains show that ReqGen outperforms six popular natural language generation approaches with respect to the hard constraint of keywords’ (phrases’) inclusion, BLEU, ROUGE, and syntax compliance. We believe that ReqGen can promote the efficiency and intelligence of specifying software requirements.

Automated Conditional Statements Checking for Complete Natural Language Requirements Specification

Defects such as the duality and the incompleteness in natural language software requirements specification have a significant impact on the success of software projects. By now, many approaches have been proposed to assist requirements analysts to identify these defects. Different from these approaches, this paper focuses on the requirements incompleteness implied by the conditional statements, and proposes a sentence embedding- and antonym-based approach for detecting the requirements incompleteness. The basic idea is that when one condition is stated, its opposite condition should also be there. Otherwise, the requirements specification is incomplete. Based on the state-of-the-art machine learning and natural language processing techniques, the proposed approach first extracts the conditional sentences from the requirements specification, and elicits the conditional statements which contain one or more conditional expressions. Then, the conditional statements are clustered using the sentence embedding technique. The conditional statements in each cluster are further analyzed to detect the potential incompleteness by using negative particles and antonyms. A benchmark dataset from an aerospace requirements specification has been used to validate the proposed approach. The experimental results have shown that the recall of the proposed approach reaches 68.75%, and the F1-measure (F1) 52.38%.

DETECTING AND RESOLVING AMBIGUITY APPROACH IN REQUIREMENT SPECIFICATION: IMPLEMENTATION, RESULTS AND EVALUATION

Requirements documentsare always written in natural language. At the point when a sentence can be understood diversely among various readersambiguity is happened [1]. In this paper, we illustrate an automated tool for detectingand resolvingambiguities thatcause a high risk of misunderstanding byseveralreaders and lead to confusion, waste of both effort and time and rework. Sentences in a natural language requirements specification document thathaveambiguity are initialdetected automatically from the text andambiguity type is determined. Sentences thatincludeambiguity are thenresolved automatically also by resolving algorithm based on a set of rules that we collected from training data. We implemented a tool for Detecting and Resolving Ambiguity (DARA), in order to clarifyand estimate our approach. The tool focuses on Lexical, Referential, Coordination, Scope and Vague ambiguity.We determine on the results of a collection of requirement specification documents to evaluatethe performance and utility of the approach.

Test Scenario Generation from Natural Language Requirements Descriptions based on Petri-Nets

Test generation from functional requirements in natural language (NL) is often time-consuming and error prone, especially in complex projects. In this context, formal representations like Petri-Nets are increasingly used as input for automated test scenario generation. However, formal representations are not trivial, and it requires a strong knowledge on formal modeling. In this paper we propose an approach to generate test scenarios that takes as input a Restricted-form of Natural Language (RNL) requirements specification. This approach translates automatically RNL requirements specified as Scenarios into executable Petri-Net models; these Petri-Nets are used as input model for test scenario generation. Our approach checks the quality of the input models and aims to decrease the time and the effort with respect to test scenario generation process. Demonstration of the feasibility of the proposed approach is based on an example of use that describes the operation of the approach.

Analysis of Feature Models Using Alloy: A Survey

Feature Models (FMs) are a mechanism to model variability among a family of closely related software products, i.e. a software product line (SPL). Analysis of FMs using formal methods can reveal defects in the specification such as inconsistencies that cause the product line to have no valid products. A popular framework used in research for FM analysis is Alloy, a light-weight formal modeling notation equipped with an efficient model finder. Several works in the literature have proposed different strategies to encode and analyze FMs using Alloy. However, there is little discussion on the relative merits of each proposal, making it difficult to select the most suitable encoding for a specific analysis need. In this paper, we describe and compare those strategies according to various criteria such as the expressivity of the FM notation or the efficiency of the analysis. This survey is the first comparative study of research targeted towards using Alloy for FM analysis. This review aims to identify all the best practices on the use of Alloy, as a part of a framework for the automated extraction and analysis of rich FMs from natural language requirement specifications.

Parsed use case descriptions as a basis for object-oriented class model generation

Abstract Object-oriented analysis and design has become a major approach in the design of software systems. Recent developments in CASE tools provide help in documenting the analysis and design stages and in detecting incompleteness and inconsistency in analysis. However, these tools do not contribute to the initial and difficult stage of the analysis process of identifying the objects/classes, attributes and relationships used to model the problem domain. This paper presents a tool, Class-Gen, which can partially automate the identification of objects/classes from natural language requirement specifications for object identification. Use case descriptions (UCDs) provide the input to Class-Gen which parses and analyzes the text written in English. A parsed use case description (PUCD) is generated which is then used as the basis for the construction of an initial UML class model representing object classes and relationships identified in the requirements. PUCDs enable the extraction of nouns, verbs, adjectives and adverbs from traditional UCDs for the identification process. Finally Class-Gen allows the initial class model to be refined manually. Class-Gen has been evaluated against a collection of unseen requirements. The results of the evaluation are encouraging as they demonstrate the potential for such tools to assist with the software development process.

Requirements for tools for ambiguity identification and measurement in natural language requirements specifications

This paper proposes a two-step approach to identifying ambiguities in natural language (NL) requirements specifications (RSs). In the first step, a tool would apply a set of ambiguity measures to a RS in order to identify potentially ambiguous sentences in the RS. In the second step, another tool would show what specifically is potentially ambiguous about each potentially ambiguous sentence. The final decision of ambiguity remains with the human users of the tools. The paper describes several requirements-identification experiments with several small NL RSs using four prototypes of the first tool based on linguistic instruments and resources of different complexity and a manual mock-up of the second tool.

Deriving static and dynamic concepts from software requirements using sophisticated tagging

Natural language requirements specifications form the basis for the subsequent phase of the information system development process, namely the development of conceptual schemata. Both, the textual as well as the conceptual representations are not really appropriate for being thoroughly captured and validated by the ‘requirement holders’, i.e. the end users. Therefore, in our approach the textual specifications are firstly linguistically analyzed and translated into a so-called conceptual predesign schema. That schema is formulated using an interlingua which is based on a lean semantic model, thus allowing users to participate more efficiently in the design and validation process. After validation, the predesign schema is mapped to a conceptual representation (e.g. UML). The sequence of these translation and transformation steps is described by the “NIBA workflow”. This paper focuses on the information supporting a step by step mapping of natural language requirements specifications to a conceptual model, and on how that information is gained. On particular, we present a four-level interpretation of tagging-output.