LadderBot: A Requirements Self-Elicitation System

Abstract

Context] 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.

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“…Rietz et al [17], show how to apply the principles of the laddering interview technique for requirements elicitation. The laddering technique consists of issuing a series of questions that are based on previous system states (i.e., previous answers and previous questions).…”

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“…Our work is also based on previous studies on challenges/exercises for teaching computer science, in particular related to IT security (Švábenskỳ et al 2018;Hulin et al 2017;Chapman et al 2014;Mirkovic and Peterson 2014;Leune and Petrilli Jr 2017;Tabassum et al 2018). The present work also makes use of artificial intelligence (AI) methods; in particular, it makes use of the lettering interview technique (Rietz and Maedche 2019). To evaluate our approach in terms of research questions, we follow best practices on survey design and follow standard existing analysis methodologies (Groves et al 2009;Drever 1995;Harrell and Bradley 2009;Wagner et al 2020 In their work, Graziotin et al 2018 argue that happy developers are better coders.…”

“…Nonetheless, we draw inspiration from the conceptual framework, which we adapt to our scenario. Rietz et al 2019, show how to apply the laddering interview technique's principles to requirements elicitation. The laddering technique consists of issuing a series of questions based on previous system states (i.e., previous answers and previous questions).…”

“…6, collects the results of the previous analysis steps, runs an AI engine based on the laddering technique, and generates the feedback to be sent back to the participant. Figure 7 shows the implementation of the AI engine using the laddering technique (Rietz and Maedche 2019).…”

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“…Much of the process can be painstaking and repetitive [46]. This challenge is further aggravated with access to more massive datasets with new possibilities for scalable data collection [36,42], causing coding to lose reliability and become intractable [1,6].…”