I am having issue in understanding the meaning of Philosophy in technical area. Suppose I have novel project that could be even patentable, can that be considered as a topic for PhD? The project is software oriented and can be developed on any standard computing machine such as PC/Tablet/Mobile phone. When I approached one professor for accepting me as his candidate for PhD, he started telling me that there is no Philosophical approach in my topic and it is just a project work. My topic was mainly related music synthesis involves developing algorithms to develop music from certain text based script files. Also no one did any work on specific topic that I opted.
Producing software, on its own, is generally not sufficient for a Ph.D.
The purpose of a Ph.D is to create new knowledge. If you want to connect this to the underlying 'Doctor of Philosophy' concept, philosophy is literally the love of knowledge. No matter your field, to get a Ph.D you need to convince a committee of faculty that you have created or discovered new knowledge by the standards of your field.
If the software work advances the state of the art by creating new knowledge, then it can drive a Ph.D, but the degree is awarded on the basis of the new knowledge (and communicating that knowledge), not the software. There are at least two ways this can arise in a software project:
- New knowledge (new computational techniques, new mathematical results, new user interaction techniques, etc.) are needed in order to make the software possible.
- The software can be used for experiments that produce new knowledge.
Either of these is fine, but the degree and dissertation rest on the knowledge.
As far as how much knowledge - my rough rule of thumb, which was taught in the Intro to Research class when I started my Ph.D, is that a dissertation in computer science should contain sufficient new research to carry 3 good papers (full papers in high-level conferences), along with the supporting material that didn't fit in the published papers.
My own Ph.D was heavily software-driven - it was largely the result of building and applying new software tools for recommender systems. However, this process resulted in the creation of new knowledge:
- Building reconfigurable, web-capable machine learning software with dependency injection
- New techniques for dependency injection to enable that software
- A re-evaluation of commonly-accepted configurations for common algorithms, finding that several of them are sub-optimal
- New research showing that different algorithms make different erroneous recommendations
- A user study, powered by the software's interchangable algorithmic capabilities, finding differences in how users perceive the output of different recommendation techniques
Those are the things that formed the basis of earning my Ph.D. The software was an important part, but I didn't get the degree for building the software.