In this paper1, we investigate and discuss the underlying nature of the requirements traceability problem. Our work is based on empirical studies, involving over 100 practitioners, and an evaluation of current support. We introduce the distinction between pre-requirements specification (pre-RS) traceability and post-requirements specification (post-RS) traceability, to demonstrate why an all-encompassing solution to the problem is unlikely, and to provide a framework through which to understand its multifaceted nature. We report how the majority of the problems attributed to poor requirements traceability are due to inadequate pre-RS traceability and show the fundamental need for improvements here. In the remainder of the paper, we present an analysis of the main barriers confronting such improvements in practice, identify relevant areas in which

The software and knowledge engineering literature defines maintenance strategies for seven main types of knowledge: words; sentences; behavioural knowledge; and meta-knowledge. Meta-knowledge divides into problem solving methods; quality knowledge; fix knowledge; social knowl- 5 edge; and processing activities. There are five main ways in which these seven knowledge types are processed: acquire; operationalise; fault; fix; and preserve. We review systems that contribute to these 7 5 = 35 types of knowledge maintenance. 1 Introduction 10 A general trend in the twentieth century is an increasing level of doubt about the things we speak or write or try to enter into programs. Popper argues that all knowledge is an hypothesis since nothing can ever be ultimately proved; Submitted to the Knowledge Engineering Review page 2 of 73 our currently believed ideas are merely those that have survive active attempts to refute them [89]. Knowledge representation theorists stress that KBs are...

Multiple viewpoints are often used in Requirements Engineering to facilitate traceability to stakeholders, to structure the requirements process, and to provide richer modelling by incorporating multiple conflicting descriptions. In the latter case, the need to reason with inconsistent models introduces considerable extra complexity. This paper describes an empirical study of the utility of multiple world reasoning (using abduction) for domain modelling. In the study we used a range of different models (ranging from correct to very incorrect), different fanouts, different amounts of data available from the domain, and different modelling primitives for representing time. In the experiments there was no significant change in the expressive power of models that incorporate multiple conflicting viewpoints. Whilst this does not negate the advantages of viewpoints during requirements elicitation, it does suggest some limits to the utility of viewpoints during requirements modelling. 1. Int...

The work reported in this thesis is motivated by the belief that knowledge-based systems (KBS) research needs to focus more on users ’ needs and cater for the various decision situations in which users will find themselves. To build individual systems that cater for all the activities that may be needed is not feasible or desirable. The problems associated with capturing knowledge are well known and the ability to capture knowledge once and access and manipulate the knowledge in multiple ways is highly desirable. It adds value to the original knowledge and offers all the benefits associated with the reuse of resources. Thus, the problem becomes one of knowledge reuse. The research question pursued in this thesis is “can knowledge captured for one purpose, such as consultation, be reused to support a wide range of alternative purposes, such as critquing or tutoring, allowing the user to answer different types of questions according to their current circumstances”? Further, this question was to be answered in a situated cognition, dynamic knowledge framework. The system developed in this thesis is based on the Multiple Classification Ripple Down Rule (MCRDR) knowledge acquisition and representation technique. MCRDR is a form of casedbased

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Knowledge-level (KL) modeling can be characterised as theory subset extraction where the extracted subset is consistent and relevant to some problem. Theory subset extraction is a synonym for Newell's principle of rationality, Clancey's model construction operators, and Breuker's components of expert solutions. In an abductive framework, a PSM is the extraction controller and is represented by a suite of BEST inference assessment operators. Each BEST operator is a single-classification expert system which accepts or culls a possible inference. PSMs can therefore be maintained by rippledown -rules, a technique for maintaining singleclassification expert systems. 1 Introduction Newell's knowledge-level (KL) approach modeled intelligence [37] as a search for appropriate operators that convert some current state to a goal state. Domain-specific knowledge are used to select the operators according to the principle of rationality; i.e. an intelligent agent will select an operator which i...

: This paper offers a framework and a process model for comparing requirements from different stakeholders. The framework combines a knowledge based system approach to requirements elicitation, a mathematically based technique for conceptual modelling, a four-state model of comparison to identify different types of conflict, a number of strategies to resolve inconsistencies and a distance metric for determining if viewpoints are converging. The framework is an extension of a knowledge engineering framework for reconciling differences between multiple sources of expertise. To justify this extension a comparison is made between the nature of knowledge and requirements and the issues that face both disciplines. The paper focuses on the common issues of maintenance and combining requirements, which are regarded as a specialised type of knowledge. Keywords: requirements engineering, knowledge engineering, ripple down rules, formal concept analysis 1 Using Knowledge Engineering Techniques ...

Previous attempts to evaluate different KEng techniques have labored under at least four difficulties: (1) lack of objective measures of success; (2) lack of success measures that are meaningful to the KBS user community; (3) lack of baseline values to make measurements meaningful; and (4) certain restrictions in the range of technologies assessed. In this paper, an accessible theory of knowledge engineering is presented: a good KEng-product offers high quality support for a design&learn loop. The high quality knowledge base initiative (hQkb) operationalizes that theory, plus some objective measures of system quality. The hQkb evaluation will be focused on external quality attributes (e.g. predictability, survivability, adaptability) of a wide range of techniques. To ensure objectivity, these quality attributes would be evaluated using specially hired verification and validation consultants. 1 Introduction Figure 1: The hQkb logo: any software system should clearly record its...

. A new framework is proposed in which software engineering (SE) is the construction of a search space and knowledge engineering (KE) is the constructing the intelligence to control the traversal of that space. Conventional information systems and object-oriented notations can specify the search space. An abductive inference engine can 5 implement the intelligent control. This unified framework supports conventional SE/KE approaches, plus automatic screen generation, conflict resolution in requirements capture, code library management, optimised code generation, and automatic testing tools. 5000 words. Submitted to the 10th European Workshop on Knowledge Ac- 10 quisition, Modeling, and Management Sant Feliu de Guixols, Catalonia, Spain October 15 - 18, 1997 1 Introduction Conventional software descriptions can be divided into several perspectives that include the events and activities in which data is processed (x2.2). Knowledge- 15 level modeling [20, 21] adds an extra perspective...

Despite improvements in various activities making up the software development process, the elicitation, analysis and modelling of user requirements remain as one of the least explored and have the least satisfactory scientific foundations. The approach to be explored takes lowlevel requirements, which can be expressed in crosstable format, and uses Formal Concept Analysis to automatically generate a concept lattice. A process model is proposed which uses the lattice to assist in the identification, negotiation and reconciliation of requirements with RE practitioners and stakeholders. The early detection and correction of errors offers the greatest potential for avoiding cost overruns in the development of information systems. Keywords AL01 Knowledge representation; AL04 Knowledge acquisition; FB03 Information Requirements; FC15 IS models; HB26 Simulation and modelling IS; Formal Concept Analysis, Ripple Down Rules 1 REQUIREMENTS ENGINEERING -- A HARD PROBLEM IN A SOFT AREA In rece...