Tim Puhlfürß

Hans-Alexander Kruse, Tim Puhlfürß, Walid Maalej

Large language models (LLMs) bear great potential for automating tedious development tasks such as creating and maintaining code documentation. However, it is unclear to what extent developers can effectively prompt LLMs to create concise and useful documentation. We report on a controlled experiment with 20 professionals and 30 computer science students tasked with code documentation generation for two Python functions. The experimental group freely entered ad-hoc prompts in a ChatGPT-like extension of Visual Studio Code, while the control group executed a predefined few-shot prompt. Our results reveal that professionals and students were unaware of or unable to apply prompt engineering techniques. Especially students perceived the documentation produced from ad-hoc prompts as significantly less readable, less concise, and less helpful than documentation from prepared prompts. Some professionals produced higher quality documentation by just including the keyword Docstring in their ad-hoc prompts. While students desired more support in formulating prompts, professionals appreciated the flexibility of ad-hoc prompting. Participants in both groups rarely assessed the output as perfect. Instead, they understood the tools as support to iteratively refine the documentation. Further research is needed to understand which prompting skills and preferences developers have and which support they need for certain tasks.

Christoph Stanik, Tim Puhlfürß, Anne Mahler et al.

Preclinical patient care is both mentally and physically challenging and exhausting for emergency teams. The teams intensively use medical technology to help the patient on site. However, they must carry and handle multiple heavy medical devices such as a monitor for the patient's vital signs, a ventilator to support an unconscious patient, and a resuscitation device. In an industry project, we aim at developing a combined device that lowers the emergency teams' mental and physical load caused by multiple screens, devices, and their high weight. The focus of this paper is to describe our ideation and requirements elicitation process regarding the user interface design of the combined device. For one year, we applied a fully digital customized version of the Applied Cognitive Task Analysis (ACTA) method to systematically elicit the requirements. Domain and requirements engineering experts created a detailed hierarchical task diagram of an extensive emergency scenario, conducted eleven interviews with subject matter experts (SMEs), and executed two design workshops, which led to 34 sketches and three mockups of the combined device's user interface. Cross-functional teams accompanied the entire process and brought together expertise in preclinical patient care, requirements engineering, and medical product development. We report on the lessons learned for each of the four consecutive stages of our customized ACTA process.