In this note, I develop my personal view on the scope and relevance of symbolic computation in software science. For this, I discuss the interaction and differences between symbolic computation, software science, automatic programming, mathematical knowledge management, artificial intelligence, algorithmic intelligence, numerical computation, and machine learning. In the discussion of these notions, I allow myself to refer also to papers (1982, 1985, 2001, 2003, 2013) of mine in which I expressed my views on these areas at early stages of some of these fields. It is a great joy to see that the SCSS (Symbolic Computation in Software Science) conference series, this year, experiences its 9th edition. A big Thank You to the organizers, referees, and contributors who kept the series going over the years! The series emerged from a couple of meetings of research groups in Austria, Japan, and Tunisia, including my Theorema Group at RISC, see the home pages of the SCSS series since 2006. In 2012, we decided to define "Symbolic Computation in Software Science" as the scope for our meetings and to establish them as an open conference series with this title. As always, when one puts two terms like "symbolic computation" and "software science" together, one is tempted to read the preposition in between - in our case "in" - as just a set-theoretic union. Pragmatically, this is reasonable if one does not want to embark on scrutinizing discussions. However, since I was one of the initiators of the SCSS series, let me take the opportunity to explain the intention behind SC in SS in this note. Also, this note, for me, is a kind of revision and summary of thoughts I had over the years on the subject of SCSS and related subjects.