Questions

This is a question-driven exploration of future work on Drasil.

1. What artifacts occur in software projects?
   • it would be useful to know the relative frequency, as a very crude means of prioritization
     • what kinds of software projects? A: for the purposes of artifacts, 'any'. The more popular the project, the higher weight it can be given.
     • where to find software projects? A: github (and gitlab and ...)
     • how many projects in sample? A: as many as automated tools will support
     • projects may be missing important artifacts/ infrequent important artifacts, even in large sample? A: how important can an artifact be if it's rare? As an artifact, it can't be. Note that the information that ought to be in such artifacts (example: requirements) really could be missing.
     • does frequency in the sample truly reflect best practice? A: absolutely not. It reflects common practice only.
     • what can we do with this information? A: get a sample of artifacts used in common practice, and reverse-engineer from them the information that they contain.
2. What information is contained in those artifacts?
   • more precisely, how can the information be classified into different kinds?
     • relationships between classifications? is this information useful? A: the definition of the kinds would be tailored to be useful, by construction. So yes, it is entirely possible to created kinds that may be 'explanatory' from a human perspective, but be useless from the point of view of generate everything.
     • frequency of information between artifacts? which classifications? information shared between classifications? A: like the above, any classification would be for the purpose of making generation easier.
3. How would one encode those different kinds information (so as to be re-usable)?
   • ex: math expressions, sentences, semi-structured sentences, semi-structured sets of these, 'theories', etc.
     • best practice? A: As this is currently not done (from the perspective of generation) outside of code generation, there isn't really much 'best practice' that exist. All current practice is experimental.
     • other practices? novel ways of encoding such information? A: some people use ontologies. Others use DSLs. Some config files are kind of related. Some people embed this information directly in generators. There's a hodge podge of stuff.
     • optimize? A: way too early to think of that. It needs to 'work', as a binary optimization problem...
4. What is the provenance of that information?
   • provenance will reveal meta-artifacts that may not typically be in projects, but exist nonetheless
     • how to trace? A: don't know
     • categorize provenance? A: don't know
     • categorize meta-artifacts? A: don't know
     • what to look for? A: don't know
     • how to formalize such information in the projects? A: don't know
     • how can this help in future projects? A: don't know
5. When taking all the information in a set of of artifacts as a coherent whole,
   • what is the joint information?
   • what is the unique information?
6. How does one transform encoded information into the form it appears in final artifacts?
   • i.e. this is a reverse operation to the encoding step(s) above
7. How to store a 'database' of such information so that it can be reused?
8. How does one specify a set of transformations that makes up a 'recipe' from information to artifacts?
9. How does one make that set of transformation modular?
10. Observation: a software system (like Drasil) is itself a software project - could Drasil be encoded in Drasil?
    • inspired by compilers, micro-passes is probably a good way to go

Stuff to add to above:

• new backends (Matlab, C, Rust, ...)
• testing as a cross-cutting approach
• version control
• workflow, idealized (Drasil) process
• very different domains
• embedded computational knowledge
• best practices
• generalized definition of developer productivity