Revealing the Dark Matter: Connecting Tacit and System Knowledge in Human-AI Collaborations

Software processes rely not only on well-documented artifacts like code, version histories, and logs, but also on team communications and tacit expertise that are rarely captured formally. We argue that effective human–AI collaboration requires shared knowledge spaces that integrate these sources and make their connections explicit. Using differential testing as a motivating case, we describe our initial prototype and the challenges it revealed, and outline a vision for dynamic knowledge spaces that support such collaboration.