Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings - 2025 IEEE 33rd International Requirements Engineering Conference Workshops, REW 2025 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 99-106 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9798331538347 |
| ISBN (Print) | 979-8-3315-3835-4 |
| Publikationsstatus | Veröffentlicht - 1 Sept. 2025 |
| Veranstaltung | 33rd IEEE International Requirements Engineering Conference Workshops, REW 2025 - Valencia, Spanien Dauer: 1 Sept. 2025 → 5 Sept. 2025 |
Publikationsreihe
| Name | Proceedings - IEEE International Requirements Engineering Conference Workshops |
|---|---|
| ISSN (Print) | 2770-6826 |
| ISSN (elektronisch) | 2770-6834 |
Abstract
In today's digitized world, software systems must support users in understanding both how to interact with a system and why certain behaviors occur. This study investigates whether explanation needs, classified from user reviews, can be predicted based on app properties, enabling early consideration during development and large-scale requirements mining. We analyzed a gold standard dataset of 4,495 app reviews enriched with metadata (e.g., app version, ratings, age restriction, in-app purchases). Correlation analyses identified mostly weak associations between app properties and explanation needs, with moderate correlations only for specific features such as app version, number of reviews, and star ratings. Linear regression models showed limited predictive power, with no reliable forecasts across configurations. Validation on a manually labeled dataset of 495 reviews confirmed these findings. Categories such as Security & Privacy and System Behavior showed slightly higher predictive potential, while Interaction and User Interface remained most difficult to predict. Overall, our results highlight that explanation needs are highly context-dependent and cannot be precisely inferred from app metadata alone. Developers and requirements engineers should therefore supplement metadata analysis with direct user feedback to effectively design explainable and user-centered software systems.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Software
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Mathematik (insg.)
- Modellierung und Simulation
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- BibTex
- RIS
Proceedings - 2025 IEEE 33rd International Requirements Engineering Conference Workshops, REW 2025. Institute of Electrical and Electronics Engineers Inc., 2025. S. 99-106 (Proceedings - IEEE International Requirements Engineering Conference Workshops).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - From App Features to Explanation Needs
T2 - 33rd IEEE International Requirements Engineering Conference Workshops, REW 2025
AU - Obaidi, Martin
AU - Qengaj, Kushtrim
AU - Droste, Jakob
AU - Deters, Hannah
AU - Herrmann, Marc
AU - Schmid, Elisa
AU - Schneider, Kurt
AU - Klünder, Jil
N1 - Publisher Copyright: © 2025 IEEE.
PY - 2025/9/1
Y1 - 2025/9/1
N2 - In today's digitized world, software systems must support users in understanding both how to interact with a system and why certain behaviors occur. This study investigates whether explanation needs, classified from user reviews, can be predicted based on app properties, enabling early consideration during development and large-scale requirements mining. We analyzed a gold standard dataset of 4,495 app reviews enriched with metadata (e.g., app version, ratings, age restriction, in-app purchases). Correlation analyses identified mostly weak associations between app properties and explanation needs, with moderate correlations only for specific features such as app version, number of reviews, and star ratings. Linear regression models showed limited predictive power, with no reliable forecasts across configurations. Validation on a manually labeled dataset of 495 reviews confirmed these findings. Categories such as Security & Privacy and System Behavior showed slightly higher predictive potential, while Interaction and User Interface remained most difficult to predict. Overall, our results highlight that explanation needs are highly context-dependent and cannot be precisely inferred from app metadata alone. Developers and requirements engineers should therefore supplement metadata analysis with direct user feedback to effectively design explainable and user-centered software systems.
AB - In today's digitized world, software systems must support users in understanding both how to interact with a system and why certain behaviors occur. This study investigates whether explanation needs, classified from user reviews, can be predicted based on app properties, enabling early consideration during development and large-scale requirements mining. We analyzed a gold standard dataset of 4,495 app reviews enriched with metadata (e.g., app version, ratings, age restriction, in-app purchases). Correlation analyses identified mostly weak associations between app properties and explanation needs, with moderate correlations only for specific features such as app version, number of reviews, and star ratings. Linear regression models showed limited predictive power, with no reliable forecasts across configurations. Validation on a manually labeled dataset of 495 reviews confirmed these findings. Categories such as Security & Privacy and System Behavior showed slightly higher predictive potential, while Interaction and User Interface remained most difficult to predict. Overall, our results highlight that explanation needs are highly context-dependent and cannot be precisely inferred from app metadata alone. Developers and requirements engineers should therefore supplement metadata analysis with direct user feedback to effectively design explainable and user-centered software systems.
KW - app reviews
KW - data mining
KW - explainability
KW - requirements engineering
UR - http://www.scopus.com/inward/record.url?scp=105020949171&partnerID=8YFLogxK
U2 - 10.1109/REW66121.2025.00017
DO - 10.1109/REW66121.2025.00017
M3 - Conference contribution
AN - SCOPUS:105020949171
SN - 979-8-3315-3835-4
T3 - Proceedings - IEEE International Requirements Engineering Conference Workshops
SP - 99
EP - 106
BT - Proceedings - 2025 IEEE 33rd International Requirements Engineering Conference Workshops, REW 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 September 2025 through 5 September 2025
ER -