The answer to your question in the narrow sense is "yes". However, I'd like to answer what I perceive as a gist of your question. Because the short answer to that is yes, but.
Software or method?
Basically, the situation as I infer it, is: You have some problem. Typically it emerges from some applications, such as biomedicine, material sciences, chemistry, etc. Solving the problem might involve some lab work, but it does not suffice. To actually solve the problem, you need some software. Now, you wrote the software and ask how is it publishable.
The major point is that classical computer science and close fields (mathematical software, for example) has been for very long focused on methods. It's not about why, it's not about how technically, it's about the theoretical way to solve the problem. Now, this does not mean that there is no implementation, backing up the theory. In an overwhelming majority of cases there is one. But publishing the code, especially as a separate entity is a relatively new (but welcome!) development.
There were times, when reproducibility in computer science meant: take a BSc student, give them a paper, let them implement it for months, now you have an implementation that you can compare with your own approach.
The languages die, but ideas don't.
I can name two understandable reasons for such a strange (for outsiders) mindset of a computer scientist. Firstly, for a long time the actual idea what to do, the thing we can concisely formulate as an algorithm or describe in a paper, was much shorter than the actual low-level code implementing the idea. There is a lot of bookkeeping, technical overhead, and maybe even some ingenuous tricks – interesting on their own merit, but not contributing to the general high-level idea. Computer science was and in part still is focused on such bird-view ideas, though ingenious hacks are also publishable nowadays.
The second reason is that the practical details of the implementation age ungracefully. This includes some technical solutions and also the programming language the implementation is written in. Stretching a bit, it's both easier and more eternal to describe a way to compute a singular value decomposition in terms of linear algebra calculations than an ancient Fortran implementation of DBDSQR.
The trend described above is changing. I see more and more papers that refer to GitHub repos with the accompanying code. This is good. It serves the reproducibility. Less poor BSc students having to implement other's papers. But what people still publish in CS are more high-level descriptions, theoretical considerations, and results of practical evaluation. But not the code as is.
Notice, that your fellow biologists, geologists, chemists, and so on might very well appreciate the working product. "Clone this github repo and plug in your data" works as a charm.
Still, if there is a high degree of scientific novelty in your software and if you want to publish it in a computer science venue in a broad sense (there are some journals for publishing code, as other answers state), you might be much better off, if you publish the method and accompany the method description with a link to GitHub, where the actual software is deposited.
Oh, and there is the third component: the data. Again, there are some journals where you can publish scientific datasets. But the general development is to put the data into a repository (such as Dryad or Zenodo, it's separate question, really) and link it in the paper.