Natural Language Processing for Detecting Forward Reference in a Document

Meyer’s seven sins have been recognized as types of mistakes that a requirements specialist are often fallen to when specifying requirements. Such mistakes play a significant role in plunging a project into failure. Many researchers were focusing in ambiguity and contradiction type of mistakes. Other types of mistakes have been given less attentions. Those mistakes often happened in reality and may equally costly as the first two mistakes. This paper introduces an approach to detect forward reference. It traverses through a requirements document, extracts, and processes each statement. During the statement extraction, any terms that may reside in the statement is also extracted. Based on certain rules which utilize POS patterns, the statement is classified as a term definition or not. For each term definition, a term is added to a list of defined terms. At the same time, every time a new term is found in a statement, it is check against the list of defined terms. If it is not found, then the requirements statement is classified as statement with forward reference. The experimentation on 30 requirements documents from various domains of software project shows that the approach has considerably almost perfect agreement with domain expert in detecting forward reference, given 0.83 kappa index value

Textual Analysis by using Knowledge-based Word Sense Disambiguation Approach

Textual analysis had been widely used in the software engineering area. Even though some approaches had been suggested over the time, these approaches encounter number of challenges, especially dealing with information extracted from the text requirement. Most studies had chosen to analyse the text manually in order to overcome this challenge. However, the long and complex text would consume more time. This paper will discuss a framework based on the knowledgebased word sense disambiguation approach, an attempt to improve the knowledge representation. In this approach, WordNet 2.1 would be used as the knowledge source used to identify concepts represented by each word in a text

Optimal-constraint lexicons for requirements specifications

Constrained Natural Languages (CNLs) are becoming an increasingly popular way of writing technical documents such as requirements specifications. This is because CNLs aim to reduce the ambiguity inherent within natural languages, whilst maintaining their readability and expressiveness. The design of existing CNLs appears to be unfocused towards achieving specific quality outcomes, in that the majority of lexical selections have been based upon lexicographer preferences rather than an optimum trade-off between quality factors such as ambiguity, readability, expressiveness, and lexical magnitude. In this paper we introduce the concept of 'replaceability' as a way of identifying the lexical redundancy inherent within a sample of requirements. Our novel and practical approach uses Natural Language Processing (NLP) techniques to enable us to make dynamic trade-offs between quality factors to optimise the resultant CNL. We also challenge the concept of a CNL being a one-dimensional static language, and demonstrate that our optimal-constraint process results in a CNL that can adapt to a changing domain while maintaining its expressiveness. © Springer-Verlag Berlin Heidelberg 2007

A semi-automatic verification tool for software requirements specification documents

Most software problems arise from deficiencies in the manner in which software requirements are elicited and expressed. Ensuring that the Software Requirements Specification document (SRS) has the necessary quality is crucial to the success of any software development project, since its information is used across all project stages. In this paper, we present a semiautomatic verification tool for SRS documents based on a comprehensive quality model.Sociedad Argentina de Informática e Investigación Operativ

Discourse structure analysis for requirement mining

International audienceIn this work, we first introduce two main approaches to writing requirements and then propose a method based on Natural Language Processing to improve requirement authoring and the overall coherence, cohesion and organization of requirement documents. We investigate the structure of requirement kernels, and then the discourse structure associated with those kernels. This will then enable the system to accurately extract requirements and their related contexts from texts (called requirement mining). Finally, we relate a first experimentation on requirement mining based on texts from seven companies. An evaluation that compares those results with manually annotated corpora of documents is given to conclude

PROMIRAR: Tool for Identifying and Managing Implicit Requirements in SRS Documents

Implicit requirements (IMRs) in software requirements specifications (SRS) are subtle and need to be identified as users may not provide all information upfront. It is found that successful functioning of a software crucially depends on addressing its IMRs. This work presents a novel system called PROMIRAR with an integrated framework of Natural Language Processing, Ontology and Analogy based Reasoning for managing Implicit Requirements. It automates early identification and management of IMRs and is found helpful in targeted application domain. We present the PROMIRAR system with its architecture, demo and evaluation