Updating a Systematic Review about Selection of Software Requirements Elicitation Techniques

Quality of software producís is closely related to the elicitation requirement process. Several studies point out that elicitation techniques achieve different results when applied in different contexts. This paper presents some recommendations about the situations in which elicitation techniques are useful. Recommendations are based on a previous systematic review, which was updated and expanded with 13 new empirical studies and more than 60 new empirical results. The aggregation process generated 5 new evidences and modified 4 existing ones. In the previous review, it was found that interviews were one of the most adequate techniques in most situations. The new evidence supports the same conclusió

LadderBot: A requirements self-elicitation system

Digital transformation impacts an ever-increasing amount of everyone’s business and private life. It is imperative to incorporate user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by users that are novices at contributing requirements to IS development projects. [Objective] We need to develop RE systems that are capable of assisting a wide audience of users in communicating their needs and requirements. Prominent methods, such as elicitation interviews, are challenging to apply in such a context, as time and location constraints limit potential audiences. [Research Method] We present the prototypical self-elicitation system “LadderBot”. A conversational agent (CA) enables end-users to articulate needs and requirements on the grounds of the laddering method. The CA mimics a human (expert) interviewer’s capability to rephrase questions and provide assistance in the process. An experimental study is proposed to evaluate LadderBot against an established questionnaire-based laddering approach. [Contribution] This work-in-progress introduces the chatbot LadderBot as a tool to guide novice users during requirements self-elicitation using the laddering technique. Furthermore, we present the design of an experimental study and outline the next steps and a vision for the future

Designing AI-Based Systems for Qualitative Data Collection and Analysis

With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS. Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. Secondly, there is a need to provide tool support enabling the analysis of large amounts of qualitative interview data. Once again, while modern technologies, such as machine learning (ML), promise remedy, concrete implementations of automated analysis for unstructured qualitative data lag behind the promise. There is a need to design interactive ML (IML) systems for supporting the coding process of qualitative data, which centers around simple interaction formats to teach the ML system, and transparent and understandable suggestions to support data analysis. Thirdly, while organizations rely on online feedback to inform requirements without explicitly conducting RE interviews (e.g., from app stores), we know little about the demographics of who is giving feedback and what motivates them to do so. Using online feedback as requirement source risks including solely the concerns and desires of vocal user groups. With this thesis, I tackle these three challenges in two parts. In part I, I address the first and the second challenge by presenting and evaluating two innovative AI-based systems, a chatbot for requirements elicitation and an IML system to semi-automate qualitative coding. In part II, I address the third challenge by presenting results from a large-scale study on IS feedback engagement. With both parts, I contribute with prescriptive knowledge for designing AI-based qualitative data collection and analysis systems and help to establish a deeper understanding of the coverage of existing data collected from online sources. Besides providing concrete artifacts, architectures, and evaluations, I demonstrate the application of a chatbot interviewer to understand user values in smartphones and provide guidance for extending feedback coverage from underrepresented IS user groups

Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general

A Review of Requirement Engineering Process Models, Tools & Methodologies

As we are living in the Era of Computer Science and almost all individuals and the organizations are completely relying on software systems. The requirement engineering is the most vital and important aspect in the success of any software engineering project. Requirement Engineering is a set of different process that works at different levels, which are incorporated at individual and organizational level Projects We need to consult different sources to find requirements. We need to involve personals from the different fields to find a set of quality requirements. The security issues undergoes as soon as early in the 1st phase of requirements. It is shown from the studies that if we consider Quality Process of Requirement Engineering at the phase it results in saving of million dollars. This paper contains the details study and comparison of different RE Process Models and Requirement Elicitation techniques. This paper elaborates the vital aspects of different Requirement Engineering Process models that help in the selection of appropriate model for the Requirement Engineers and practitioners working in the industry. This Paper also Focus on the giving a detailed view of Elicitation techniques and comparative study including the characteristics and their strengths and weakness. Some strengths and weakness found during detailed study are also structured and listed that is also the helpful for the Appropriate selection of RE Process model

Eliciting expert knowledge to inform training design

To determine the elicitation methodologies best placed to uncover and capture the expert operator’s reflective cognitive judgements in complex and dynamic military operating environments (e.g., explosive ordinance disposal) in order to develop the specification for a reflective eXplainable Artificial Intelligence (XAI) agent to support the training of domain novices. Approach: A bounded literature review of the latest developments in expert knowledge elicitation was undertaken to determine the ’art-of-the-possible’ in respects to uncovering an expert’s cognitive judgements in complex and dynamic environments. Candidate methodologies were systematically and critically reviewed in order to identify the most promising methodologies for uncovering expert situational awareness and metacognitive evaluations in pursuit of actionable threat mitigation strategies in high-risk contexts. Research outputs are synthesized into an interview protocol for eliciting and understanding the in-situ actions and decisions of experts in high-risk, complex operating environments. Practical implications: Trainees entering high-risk operating environments can benefit from exposure to expert reflective strategies whilst learning the trade. Typical operator training focuses on technical aspects of threat mitigation but often overlooks reflective self-evaluation. The present study represents an initial step towards determining the feasibility of designing a reflective XAI agent to augment the performance of trainees entering high-risk operations. Outputs of the expert knowledge elicitation protocol documented here shall be used to refine a theoretical framework of expert operator judgement, in order to determine decision support strategies of benefit to domain novices

Requirements Engineering as Creative Problem Solving: A Research Agenda for Idea Finding

This vision paper frames requirements engineering as a creative problem solving process. Its purpose is to enable requirements researchers and practitioners to recruit relevant theories, models, techniques and tools from creative problem solving to understand and support requirements processes more effectively. It uses 4 drivers to motivate the case for requirements engineering as a creative problem solving process. It then maps established requirements activities onto one of the longest-established creative problem solving processes, and uses these mappings to locate opportunities for the application of creative problem solving in requirements engineering. The second half of the paper describes selected creativity theories, techniques, software tools and training that can be adopted to improve requirements engineering research and practice. The focus is on support for problem and idea finding - two creative problem solving processes that our investigation revealed are poorly supported in requirements engineering. The paper ends with a research agenda to incorporate creative processes, techniques, training and tools in requirements projects

Conceptual knowledge acquisition in biomedicine: A methodological review

AbstractThe use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain