How Does Users' App Knowledge Influence the Preferred Level of Detail and Format of Software Explanations?Technical Paper
This program is tentative and subject to change.
Context and Motivation: Due to their increasing complexity, everyday software systems are becoming increasingly opaque for users. A frequently adopted method to address this difficulty is explainability, which aims to make systems more understandable and usable. Question/problem: However, explanations can also lead to unnecessary cognitive load. Therefore, adapting explanations to the actual needs of a user is a frequently faced challenge. Principal ideas/results: This study investigates factors influencing users’ preferred the level of detail and the form of an explanation (e.g., short text or video tutorial) in software. We conducted an online survey with 58 participants to explore relationships between demographics, software usage, app-specific knowledge, as well as their preferred explanation form and level of detail. The results indicate that users prefer moderately detailed explanations in short text formats. Correlation analyses revealed no relationship between app-specific knowledge and the preferred level of detail of an explanation, but an influence of demographic aspects (like gender) on app-specific knowledge and its impact on application confidence were observed, pointing to a possible mediated relationship between knowledge and preferences for explanations. Contribution: Our results show that explanation preferences are weakly influenced by app-specific knowledge but shaped by demographic and psychological factors, supporting the development of adaptive explanation systems tailored to user expertise.
This program is tentative and subject to change.
Tue 8 AprDisplayed time zone: Brussels, Copenhagen, Madrid, Paris change
14:00 - 15:30 | |||
14:00 30mTalk | FeReRe: Feedback Requirements Relation using Large Language ModelsEvaluation Paper Research Track | ||
14:30 30mTalk | How Does Users' App Knowledge Influence the Preferred Level of Detail and Format of Software Explanations?Technical Paper Research Track Martin Obaidi Leibniz Universität Hannover, Jannik Fischbach Netlight Consulting GmbH and fortiss GmbH, Marc Herrmann Leibniz Universität Hannover, Hannah Deters Leibniz University Hannover, Jakob Droste Leibniz Universität Hannover, Jil Klünder University of Applied Sciences | FHDW Hannover, Kurt Schneider Leibniz Universität Hannover, Software Engineering Group | ||
15:00 30mTalk | How Effectively Do LLMs Extract Feature-Sentiment Pairs from App Reviews?Evaluation Paper Research Track Faiz Ali Shah University of Tartu, Estonia, Ahmed Sabir Institute of Computer Science, University of Tartu, Tartu, Estonia, Rajesh Sharma Institute of Computer Science, University of Tartu, Tartu, Estonia, Dietmar Pfahl University of Tartu |