As the demand for skilled data scientists has grown, university level statistics and data science courses have become more rigorous in training students to understand and utilize the tools that the...

https://www.tandfonline.com/doi/pdf/10.1080/10691898.2020.1847007

How Students Use Statistical Computing in Problem Solving

Introduction: Although the interview is the most important and widely used requirements elicitation technique, novice engineers do not receive adequate training in Requirements Engineering (RE) courses.Objectives: Develop an AI-based interview simulator for helping novice requirements engineers in gaining interview skills.Methods: The research is based on the Design Science Methodology for Information Systems. The simulator is the outcome of six cycles; in each cycle, a proof of concept with additional features is created. Each cycle finishes with evaluation and improvement suggestions.Results: The simulator has been tested with students and results have been promising. The interview simulator understands context-free questions, retrieving the right information related to RE concepts such as goals, tasks, users, benefits, and constraints. The simulator also answers questions based on the context, makes summaries of the conversation, responds to meta-questions, and adds ambiguity and incompleteness to the conversation.Conclusions: The results have been promising. The simulator has been tested with degree and master level students. They were able to create a requirements specification using the simulator, and the feedback has been generally positive. The simulator will be tested in a real RE course in the academic year 2020–2021. Once it proves effective in the classroom, it will be opened to the RE community for free use and improvement.

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Chatbot-based Interview Simulator: A Feasible Approach to Train Novice Requirements Engineers

Pre-requirements specification (pre-RS) traceability focuses on tracing requirements back to their sources. In comparison with post-RS traceability, pre-RS traceability is an under-explored area of research. Likely reasons for the limited studies are the scarcity of pre-RS resources, e.g., recorded requirements elicitation conversations such as interviews or workshops, and the challenges of linking requirements to informal, unstructured text. Building on the increasing use of digital communication tools that allow the recording and transcription of conversations, we explore the opportunity of linking requirements to the transcript of a requirements elicitation conversation. We introduce TRACE2CoNV, a prototype tool that aims at tracing user story requirements back to the relevant speaker turns in a conversation. TRACE2CoNV makes use of NLP techniques to determine the relevant speaker turns. As an initial validation, we take automatically generated transcripts from real-world requirements conversations, and we assess the effectiveness of TRACE2CoNV in supporting the process of identifying additional context for the requirements. The validation serves as a formative evaluation that guides the evolution of TRACE2CoNV and as a inspiration for future research in the field of conversational RE.

Back to the Roots: Linking User Stories to Requirements Elicitation Conversations

[Context] Interviewing stakeholders is the most popular requirements elicitation technique among multiple methods. The success of an interview depends on the collaboration of the interviewee which can be fostered through the interviewer's preparedness and communication skills. Mastering these skills requires experience and practicing interviews. [Problem] Practical training is resource-heavy as it calls for the time and effort of a stakeholder for each student which may not be feasible for a large number of students. [Method] To address this scalability problem, this paper proposes RoboREIT, an interactive Robotic tutor for Requirements Elicitation Interview Training. The humanoid robotic component of RoboREIT responds to the questions of the interviewer, which the interviewer chooses from a set of predefined alternatives for a particular scenario. After the interview session, RoboREIT provides contextual feedback to the interviewer on their performance and allows the student to inspect their mistakes. RoboREIT is extensible with various scenarios. [Results] We performed an exploratory user study to evaluate RoboREIT and demonstrate its applicability in requirements elicitation interview training. The quantitative and qualitative analyses of the users' responses reveal the appreciation of RoboREIT and provide further suggestions about how to improve it. [Contribution] Our study is the first in the literature that utilizes a social robot in requirements elicitation interview education. RoboREIT's innovative design incorporates replaying faulty interview stages and allows the student to learn from mistakes by a second time practicing. All participants praised the feedback component, which is not present in the state-of-the-art, for being helpful in identifying the mistakes. A favorable response rate of 81% for the system's usefulness indicates the positive perception of the participants.

RoboREIT: an Interactive Robotic Tutor with Instructive Feedback Component for Requirements Elicitation Interview Training

—App store-inspired elicitation is the practice of exploring competitors’ apps, to get inspiration for requirements. This activity is common among developers, but little insight is available on its practical use, advantages and possible issues. This paper aims to study strategies, beneﬁts, and challenges of app store-inspired elicitation, and to compare this technique with the more traditional requirements elicitation interviews. We conduct an experimental simulation with 58 analysts, and collect qualitative data. Our results show that: (1) speciﬁc guidelines and procedures are required to better conduct app store-inspired elicitation; (2) current search features made available by app stores are not suitable for this practice, and more tool support is required to help analysts in the retrieval and evaluation of competing products; (3) while interviews focus on the why dimen-sion of requirements engineering (i.e., goals), app store-inspired elicitation focuses on how (i.e., solutions), offering indications for implementation and improved usability. Our study provides a framework for researchers to address existing challenges and suggests possible beneﬁts to foster app store-inspired elicitation among practitioners.

Strategies, Benefits and Challenges of App Store-inspired Requirements Elicitation

Requirements are elicited from the customer and other stakeholders through an iterative process of interviews, prototyping, and other interactive sessions. Many communication phenomena may emerge in these early iterations, that lead initial ideas to be transformed, renegotiated, or reframed. Understanding how this process takes place can help in solving possible communication issues as well as their consequences. In this work, we perform an exploratory study of descriptive nature to understand in which way requirements get transformed from initial ideas into documented needs. To this end, we select 30 subjects that act as requirements analysts, and we perform a set of elicitation sessions with a fictional customer. The customer is required to study a sample requirements document for a system beforehand and to answer the questions of the analysts about the system. After the elicitation sessions, the analysts produce user stories for the system. These are compared with the original ones by two researchers to assess to which extent and in which way the initial requirements evolved throughout the interactive sessions. Our results show that between 30% and 38% of the produced user stories include content that can be fully traced to the initial ones, while the rest of the content is dedicated to new requirements. We also show what types of requirements are introduced through the elicitation process, and how they vary depending on the analyst. Our work contributes to theory in requirements engineering, with empirically grounded, quantitative data, concerning the impact of elicitation activities with respect to initial ideas.

From Ideas to Expressed Needs: an Empirical Study on the Evolution of Requirements during Elicitation

[Context and motivation] Role-playing offer experiential learning through the simulation of real-world scenarios; for this reason, it is widely used in software engineering education. In Requirements Engineering, role-playing is a popular way to provide students hands-on experience with requirements elicitation interviews. [Problem] However, managing a role-playing activity to simulate requirements elicitation interviews in a class is time consuming, as it often requires pairing students with student assistants or fellow classmates who act as either customers or requirement analysts as well as creating and maintaining the interview schedules between the actors. To make the adoption of role-playing activities in a class feasible, there is a need to develop a solution to reduce instructors’ workload. [Principal ideas] To solve this problem we propose the use of VIrtual CustOmer (VICO), an intent-based, multimodal, conversational agent. VICO offers an interview experience comparable to talking to a human and provides a transcript of the interview annotated with the mistakes students made in it. The adoption of VICO will eliminate the need to schedule interviews as the students can interact with it in their free time. Moreover, the transcript of the interview allows students to evaluate their performance to refine and improve their interviewing skills. [Contribution] In this research preview, we show the architecture of VICO and how it can be developed using existing technologies, we provide an online rule-based initial prototype and show the practicality and applicability of this tool through an exploratory study.

Designing a Virtual Client for Requirements Elicitation Interviews

Teaching students problem-solving skills is the biggest challenge for many computing programs. Students often do not consider what questions they need to ask while designing a solution. This paper introduces how technical writing techniques and problem-solving strategies can be combined to help students find those questions and develop programming code through writing instructions and documenting their design process. Preliminary results indicate that this approach helps developing problem-solving skills and improves the student success rate in the first programming course.

Instructional Pseudocode Guide to Teach Problem-Solving

Abstract Requirements are elicited from the customer and other stakeholders through an iterative process of interviews, prototyping, and other interactive sessions. Then, requirements can be further extended, based on the analysis of the features of competing products available on the market. Understanding how this process takes place can help to identify the contribution of the different elicitation phases, thereby allowing requirements analysts to better distribute their resources. In this work, we empirically study in which way requirements get transformed from initial ideas into documented needs, and then evolve based on the inspiration coming from similar products. To this end, we select 30 subjects that act as requirements analysts, and we perform interview-based elicitation sessions with a fictional customer. After the sessions, the analysts produce a first set of requirements for the system. Then, they are required to search similar products in the app stores and extend the requirements, inspired by the identified apps. The requirements documented at each step are evaluated, to assess to which extent and in which way the initial idea evolved throughout the process. Our results show that only between 30% and 38% of the requirements produced after the interviews include content that can be fully traced to initial customer’s ideas. The rest of the content is dedicated to new requirements, and up to 21% of it belongs to completely novel topics. Furthermore, up to 42% of the requirements inspired by the app stores cover additional features compared to the ones identified after the interviews. The results empirically show that requirements are not elicited in strict sense, but actually co-created through interviews, with analysts playing a crucial role in the process. In addition, we show evidence that app store-inspired elicitation can be particularly beneficial to complete the requirements. 

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How do requirements evolve during elicitation? An empirical study combining interviews and app store analysis

Requirements elicitation interviews are the most commonly used technique for requirements elicitation. Through interviewing subject matter experts, facilitating deep discussions, and documenting findings, requirement analysts can uncover the true needs of the system to satisfy stakeholders’ needs. However, during these interviews, the analysts often have to divide their attention to asking the right questions, react appropriately to the stakeholder’s responses, and keep track of the information flow throughout the interview. This is a complicated process, during which analysts could make mistakes and miss important information. For these reasons, it could be useful for them to review their interviews to understand what information has been elicited and what has been missed, what they would need to ask as follow up, and what mistakes they have done during the process. To support these processes, we propose AnnoteREI, a tool that allows analysts to upload the recording of their interviews and create an editable transcript with speaker diarization. The tool offers many functionalities for different purposes: (1) students and novices can use it to identify and tag their mistakes, thus improving their skills by learning from their experience; (2) analysts can annotate any part of the transcript and tag it for later review. For example, they can label a section as a requirement or as a topic that requires more investigation, or a name mentioned by the interviewee as another stakeholder; and (3) researchers can use the tagging system to analyze and collect new data.

Sourav Debnath

Papers

From Ideas to Expressed Needs: an Empirical Study on the Evolution of Requirements during Elicitation

Designing a Virtual Client for Requirements Elicitation Interviews

Instructional Pseudocode Guide to Teach Problem-Solving

How do requirements evolve during elicitation? An empirical study combining interviews and app store analysis

AnnoteREI! A Tool for Transcribing and Annotating Requirements Elicitation Interviews