Debugging is an important activity in software development, yet providing actionable and comprehensible explanations for program failures remains challenging. Automated tools such as ALHAZEN and AVICENNA address this by using distinct strategies: ALHAZEN employs binary decision trees to show failure-inducing conditions...
C. Lazik, Martin Eberlein, Aaron Ziglowski et al.· Message Understanding Confer...· 0 citations
The availability of Large Language Models (LLMs) reshaped scientific discourse at a linguistic level. LLMs are assumed to homogenize academic writing, flattening it into a single generic lexical register. To understand how CHI writing has changed since the public release of LLMs, we analyzed full texts of 14,262 archiv...
Thomas Kosch, Robin Welsch, Michael A. Hedderich et al.· 0 citations
Personas are widely used to represent users' motivations, goals, and needs, yet the perspectives of underrepresented groups are rarely addressed in human-centered design. Large Language Models (LLMs) promise to generate personas from vast datasets, but achieving genuine diversity and accurate representation requires de...
C. Lazik, Charlotte Kauter, Isabella Graßl et al.· IEEE International Requireme...· 0 citations
AlHAZEN and AVICENNA show that effective debugging tools have tradeoffs between accuracy and interpretability to support developers’ decision-making in increasingly complex software environments.
C. Lazik, Martin Eberlein, Aaron Ziglowski et al.· Message Understanding Confer...· 0 citations
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