arXiv subjects

Joerg Doerr

Publications and source records attributed to Joerg Doerr.

3 recordsLinked to original sources

arXiv2025

Explainability Needs in Agriculture: Exploring Dairy Farmers' User Personas

Artificial Intelligence (AI) promises new opportunities across many domains, including agriculture. However, the adoption of AI systems in this sector faces several challenges. System complexity can impede trust, as farmers' livelihoods depend on their decision-making and they may reject opaque or hard-to-understand recommendations. Data privacy concerns also pose a barrier, especially when farmers lack transparency regarding who can access their data and for what purposes. This paper examines dairy farmers' explainability requirements for technical recommendations and data privacy, along with the influence of socio-demographic factors. Based on a mixed-methods study involving 40 German dairy farmers, we identify five user personas through k-means clustering. Our findings reveal varying requirements, with some farmers preferring little detail while others seek full transparency across different aspects. Age, technology experience, and confidence in using digital systems were found to correlate with these explainability requirements. The resulting user personas offer practical guidance for requirements engineers aiming to tailor digital systems more effectively to the diverse requirements of farmers.

cs.CY↗

arXiv2025

Classification of Quality Characteristics in Online User Feedback using Linguistic Analysis, Crowdsourcing and LLMs

Software qualities such as usability or reliability are among the strongest determinants of mobile app user satisfaction and constitute a significant portion of online user feedback on software products, making it a valuable source of quality-related feedback to guide the development process. The abundance of online user feedback warrants the automated identification of quality characteristics, but the online user feedback's heterogeneity and the lack of appropriate training corpora limit the applicability of supervised machine learning. We therefore investigate the viability of three approaches that could be effective in low-data settings: language patterns (LPs) based on quality-related keywords, instructions for crowdsourced micro-tasks, and large language model (LLM) prompts. We determined the feasibility of each approach and then compared their accuracy. For the complex multiclass classification of quality characteristics, the LP-based approach achieved a varied precision (0.38-0.92) depending on the quality characteristic, and low recall; crowdsourcing achieved the best average accuracy in two consecutive phases (0.63, 0.72), which could be matched by the best-performing LLM condition (0.66) and a prediction based on the LLMs' majority vote (0.68). Our findings show that in this low-data setting, the two approaches that use crowdsourcing or LLMs instead of involving experts achieve accurate classifications, while the LP-based approach has only limited potential. The promise of crowdsourcing and LLMs in this context might even extend to building training corpora.

cs.SE↗

arXiv2024

Towards Crowd-Based Requirements Engineering for Digital Farming (CrowdRE4DF)

The farming domain has seen a tremendous shift towards digital solutions. However, capturing farmers' requirements regarding Digital Farming (DF) technology remains a difficult task due to domain-specific challenges. Farmers form a diverse and international crowd of practitioners who use a common pool of agricultural products and services, which means we can consider the possibility of applying Crowd-based Requirements Engineering (CrowdRE) for DF: CrowdRE4DF. We found that online user feedback in this domain is limited, necessitating a way of capturing user feedback from farmers in situ. Our solution, the Farmers' Voice application, uses speech-to-text, Machine Learning (ML), and Web 2.0 technology. A preliminary evaluation with five farmers showed good technology acceptance, and accurate transcription and ML analysis even in noisy farm settings. Our findings help to drive the development of DF technology through in-situ requirements elicitation.

cs.SE↗