
This dataset consists of files relevant to the research paper, "Exploring Creativity in Human-Human-LLM Collaborative Software Design" accepted by the International Conference on Evaluation and Assessment in Software Engineering (EASE) 2026. This lab-based study consisted of 18 pairs of software professionals completing a design task. They were free to use any design methodology and tools and were given optional access to a chat-based tool that leveraged an LLM. The accompanying paper provides details of the research method followed by the study and should be read in conjunction with this data. The research question explored in the study was: "How, and where, does creativity appear naturally when designing with an LLM?" The dataset is provided to share additional data relevant to the study findings and to aid other researchers interested in repeating or running a similar study.
Generative AI, Software Engineering, software design
Generative AI, Software Engineering, software design
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
