I'm halfway (3 months out of 6) through my masters thesis and the problem involves using a few open source software libraries to the analyse data. But essentially the problem is that after the data has been analysed, which is basically fitting data into a function, and the performance is good, I was basically done. There was no original work and anyone could have done that. So my supervisor then wanted me to explain why it performed so well, but the problem is that the algorithm is basically a statistical model and well-documented, with no relation to the data, so I would be just regurgitating summaries and superficial explanation of how the algorithm works, and the source-code basically does what it says. I explain this to him, that this would be a dead-end because the algorithm works with any data, and it was like explaining why a camera took better photos of my car than my drawing, which is down to the camera itself not the car, and my supervisor wants to why the car performs so well with the camera.
I am just stuck and worried on how I was going to write a thesis on this. I have done nothing new and my supervisor still wants me to go deeper to find a link, even though there may not be one. Should I ask an academic supervisor for advice? Should I just stick it out and develop a weak link between those two? Should I swap projects to something more experimental where at least the results are original since it was self-measured?