View metadata, citation and similar papers at core.ac.uk brought to you by CORE 146 Int'l Conf. Dataprovided Minin byg Covenant | DMIN'16 University Repository| Common Sense Knowledge, Ontology and Text Mining for Implicit Requirements Onyeka Emebo1,2, Aparna S. Varde1, Olawande Daramola 2 1. Department of Computer Science, Montclair State University, Montclair, NJ, USA. 2. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria [email protected], [email protected], [email protected] Abstract— The ability of a system to meet its requirements is a intended user. However the very fact that CSK is common, strong determinant of success. Thus effective requirements not all knowledge and requirements that entail common sense, specification is crucial. Explicit Requirements are well-defined will be captured or expressed by the expected user. As Polanyi needs for a system to execute. IMplicit Requirements (IMRs) are describes “We know more than we can tell”. It is therefore assumed needs that a system is expected to fulfill though not the responsibility of the software developer to capture as well elicited during requirements gathering. Studies have shown that as manage the unexpressed requirements in the development a major factor in the failure of software systems is the presence of a suitable and satisfactory system. The application of of unhandled IMRs. Since relevance of IMRs is important for Common Sense Knowledge can improve the identification as efficient system functionality, there are methods developed to well as management of IMRs. Common Sense Knowledge aid the identification and management of IMRs. In this paper, CSK) is defined in [3] as a collection of simple facts about we emphasize that Common Sense Knowledge, in the field of Knowledge Representation in AI, would be useful to people and everyday life, such as "Things fall down, not up", automatically identify and manage IMRs. This paper is aimed and "People eat breakfast in the morning". In [7], the authors at identifying the sources of IMRs and also proposing an describe CSK as a tremendous amount and variety of automated support tool for managing IMRs within an knowledge of default assumptions about the world, which is organizational context. Since this is found to be a present gap in shared by (possibly a group of) people and seems so practice, our work makes a contribution here. We propose a fundamental and obvious that it usually does not explicitly novel approach for identifying and managing IMRs based on appear in people's communications. CSK is mainly combining three core technologies: common sense knowledge, characterized by its implicitness. text mining and ontology. We claim that discovery and handling From the literature, it is observed that a number of reasons of unknown and non-elicited requirements would reduce risks have caused the emergence of implicit requirements some of and costs in software development. which include; i) When a software organization develops a product in a new domain and ii) as a result of knowledge gap Keywords- Implicit Requirements, Common Sense between developers and stakeholders due to the existence of Knowledge, Ontology, Text Mining, Requirement Engineering implicit knowledge. Given this background, we claim that CSK will aid in the I. INTRODUCTION identification of IMRs useful for domain-specific knowledge The challenge of identifying and managing implicit bases. This will be useful for storing domain concepts, requirements has developed to be a crucial subject in the field relations and instances for onward use in domain related of requirements engineering. In [7] it was stated “When processing, knowledge reuse and discovery. Thus we build an critical knowledge, goals, expectations or assumptions of key automated IMR support tool based on our proposed stakeholders remain hidden or unshared then poor framework for managing IMRs using common sense requirements and poor systems are a likely, and costly, knowledge, ontology and text mining. consequence.” With the relevance of implicit requirements The rest of this paper is organized as follows: Section II (IMRs) being identified and related to the efficient presents core technologies. Section III reviews related work functionality of any developed system, there have been on IMRs. Section IV describes our automated IMR process different proposals as well as practical methodologies framework. Section V describes the use and evaluation of the developed to aid the identification and management of IMRs. IMR support tool. Section VI gives the conclusions. Common Sense Knowledge (CSK) is an area that involves making a computer or another machine understand basic facts as intuitively as a human would. It is an area in the realm of II. BACKGROUND Knowledge Representation (KR) which involves paradigms In this section, an overview of the concepts relevant in for adequate depiction of knowledge in Artificial Intelligence CSK, ontology, text mining and natural language processing (AI). The area of CSK is being researched for its use in is presented. This is useful in order to understand our proposed identification and capturing of implicit requirements. IMR framework later. Since AI is aimed at enabling machines solve problems like humans, there is a need for common sense knowledge in A. Common Sense Knowledge AI systems to enhance problem-solving. This not only Common Sense Knowledge (CSK) according to [3] is a involves storing what most people know but also the tremendous amount and variety of knowledge of default application of that knowledge [8]. In software engineering, assumptions about the world, which is shared by people and the development of systems must meet the needs of the seems so obvious that it usually does not explicitly appear in ISBN: 1-60132-431-6, CSREA Press © Int'l Conf. Data Mining | DMIN'16 | 147 ࡯ࢄ࡯ is a set whose elements are called ك communications. Some characteristics of CSK as identified in 2. R the literature are as follows: relations. For r = (c1, c2) ϵ R, it is written x Share: A group of people possess and share CSK. r(c1) = c2. x Fundamentality: People have a good understanding 3. Ao is a set of axioms on O. of CSK that they tend to take CSK for granted. In recent times, there is an increased use of ontologies in x Implicitness:People more often don’t mention or software engineering. The use of ontologies has been document CSK explicitly since others also know it. proposed by different researchers’ in. the field of x Large-Scale: CSK is highly diversified and in large requirements engineering and management According to [40] quantity. the increased use can be attributed to the following: (i) they x Open-Domain: CSK is broad in nature covering all facilitate the semantic interoperability and (ii) they facilitate aspects of our daily life rather than a specific domain. machine reasoning. Researchers have so far proposed many x Default: CSK are default assumptions about typical different synergies between software engineering and cases in everyday life, so most of them might not ontologies. In Requirements Engineering (RE), ontology can always be correct. be used for: 1) describing requirements specification Previous work on common sense knowledge includes the documents and 2) to formally represent requirements seminal projects Cyc [9] and WordNet [5], ConceptNet [20], knowledge [10]. Ontology is an important resources of Webchild [31] and the work by [14] and [24]. Cyc has domain knowledge, especially in a specific application compiled complex assertions such as every human has exactly domain. In the management of IMRs, ontology can provides one father and exactly one mother. WordNet has manually shared knowledge which can be useful in the management of organized nouns and adjectives into lexical classes, with IMRs in similar or cross domain knowledge management. By careful distinction between words and word senses. conceptualizing domain knowledge including the identified ConceptNet is probably the largest repository of common implicit requirement, it enables the easy adoption and sense assertions about the world, covering relations such as identification and also management of IMRs. This reduces hasProperty, usedFor, madeOf, motivatedByGoal, etc. enormous costs in requirement development process.in Tandon et al. [14] automatically compiled millions of triples making “explicit specification” it aids the reduction of of the form <noun relation adjective> by mining n-gram ambiguous requirements and incomplete definitions during corpora. Lebani & Pianta [24] proposed encoding additional the elicitation process [40]. By using such ontology, several lexical relations for commonsense knowledge into WordNet. kinds of semantic processing can be achieved in requirements WebChild contains triples that connect nouns with adjectives analysis [31]. In this work, ontology is considered a valid via fine-grained relations like hasShape, hasTaste, solution approach, because it has the potential to facilitate evokesEmotion, etc. formalized semantic description of relevant domain B. Ontology knowledge for identification and management of IMR. The term ontology has different meanings. Ontology made C. Text Mining and Natural Language Processing and entrance in the field of computer science in the 1990s in association with Knowledge Acquisition. Different Text mining is the process of analyzing text to extract definitions have been given to the term “ontology”. A basic information that is useful for particular