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Patterns of Developer Adoption of LLM-Generated Code Refactoring Suggestions
Large language models (LLMs) have gained widespread popularity and have steadily improved over time, enabling software developers to …
David Schön, Faiza Amjad, Tehreem Asif, Ranim Khojah, Mazen Mohamad, Francisco Gomes de Oliveira Neto, Philipp Leitner
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Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code
Modern software development increasingly involves the use of large language models (LLMs) to generate code. Despite their rapid …
Ranim Khojah, Francisco Gomes de Oliveira Neto, Mazen Mohamad, Julian Frattini, Philipp Leitner
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Emotional Strain and Frustration in LLM Interactions in Software Engineering
Large Language Models (LLMs) are increasingly integrated into various daily tasks in Software Engineering, such as coding and …
Cristina Martinez Montes, Ranim Khojah
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Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-Physical Systems
In safety-critical software systems, cybersecurity activities become essential, with risk assessment being one of the most critical. In …
Fikret Mert Gültekin, Oscar Lilja, Ranim Khojah, Rebekka Wohlrab, Marvin Damschen, Mazen Mohamad
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Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice
Large Language Models (LLMs) are frequently discussed in academia and the general public as support tools for virtually any use case …
Ranim Khojah, Mazen Mohamad, Philipp Leitner, Francisco Gomes de Oliveira Neto
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From Human-to-Human to Human-to-Bot Conversations in Software Engineering
Software developers use natural language to interact not only with other humans, but increasingly also with chatbots. These …
Ranim Khojah, Francisco Gomes de Oliveira Neto, Philipp Leitner
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Evaluating the Trade-offs of Diversity-Based Test Prioritization: An Experiment
Different test prioritization techniques detect faults at earlier stages of test execution. To this end, Diversity-based techniques …
Ranim Khojah, Francisco Gomes de Oliveira Neto, Chi Hong Chao
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Evaluating N-best Calibration of Natural Language Understanding for Dialogue Systems
A Natural Language Understanding (NLU) component can be used in a dialogue system to perform intent classification, returning an N-best …
Ranim Khojah, Alexander Berman, Staffan Larsson
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