General software issues

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In a world where the digital and physical realm starts to blend that is physical products become networked live acutateable and reconfigurable software architecture / organisation / design or however one may call it becomes even more important than it is already today.

Key issues are:

  • stability (at best error-proofness) correctness; research in Haskell
  • maintainability
  • modularity / extendability / scalability
  • diversity (as options for unexpected dead end routes)
  • optimized-specialisation conserving functionality-expanding-generalisation and vice versa
  • highly complex version management (dependency hell)
  • progressive disclosure human computer interfaces [1]
  • ...

Important challenges

File systems

Currently used tree based and machine local file systems have their limits. Sorting the same data after multiple hierarchical criteria is impossible. To give an example: Assume one owns a lot of image-file text-file sets. The small text files are of high importance (e.g. source code) while the huge images are of relatively low importance (e.g. rendered from source). Archiving different backup levels (location, redundancy level) for file types of different importance isn't possible with the basic functionalities of tree based file systems.

File system indexing usage of meta-data and file tagging only mend but do not solve the problem. Some kind of graph based file systems are needed. Graph databases (like Neo4j?) are interesting but are only crutches if implemented on top of tree based file systems.

The limitation to serial access to mass storage disk space (now changing with random access SD drives) led to the fact that current systems are still hardware near programmed and suffer from a lack of abstraction. Data must be manually serialized for persistent storage. Net based services like Google drive and Facebook already emulate this behavior. Another interesting "on top" approach is yesod with its integrated persistence.

Recently Google made a move in this direction. Google drive allows to create folder structures as an directed acyclic graph!

Graph based file systems would have some similarities too wikis. Both allow one to explain the same thing in different contexts. Compared to file systems Wikis allow one to add elaborate descriptions and documentation instead of just short folder names. Wikis are not built to replace file systems though.

  • broken links can be reconstructed
  • emerging concepts can be auto-detected
  • criterions can be combined by disjunctive and conjunctive normal form (methods from digital logic)
  • there is a partial order - dividing the whole graph from a chosen criterion into three parts (analog to the concurrency cone)

Possible graphical representations

  • super boxes connected with directed arrows to sub-boxes or sub-boxes in super-boxes (mutually convertible). The program yEd gives a niche example of how something like that could look.
  • In the case of sub-boxes in super-boxes the metaphor "depth of a topic" could be taken literally for a 3D-representation.
  • beside the well known child tree in a graph file system an ancestor tree can also be shown
  • like in conventional tree style file-systems the state of all folders collapsed by default and only a view "open" is a necessity. Additionally hiding stretches of overly long paths and same level "sibling" folders might be desirable.

Both situations call for remedy though which is easier in a graph than a tree. Progressive exposure like this breaks the long-standing user developer barrier making users learn by accident and making them become developers by accident. Current mainstream graphical programming languages often suffer from disregarding the importance of hiding subsystem complexity leading to horribly unmaintainable circuit krakens.

Dependency hell

Current packet management systems suffer from the dreaded problem of dependency hell. Especially developers who install a lot of software packages in parallel are affected. Solution approaches include:

Legacy barriers:

A high level (math based) abstraction layer is absolute essential for isolation of most of the plumbing (that is pattern matching parsing and serialization) in software. Keeping data immutable on the scale of whole operating systems and and even the whole word wide web is a necessary requirement for that.

Things that don't get referenced from anywhere anymore can be removed by a worldwide garbage collector.

  • RAM to HDD
  • application to application
  • computer to computer
  • file to file

An approach to implementation is "unison" (Link: [2]).

Software for 3D-modelling and beyond

(TODO: expand this chapter)

Atomistic modelling: From Tom's machine phase blog comes an example of the usage of Nanoengineer-1: DNA origami: from design to product

See: Data decompression chain

See: Visually augmented purely functional programming

Bad software design may undermine the "Disaster proof" property of globally used APM.

Further related information

Avoiding hidden state (a potential source of errors) can be compatible with interactive environments:

Relation of AP Technology to new computing paradigms

What is likely to be a necessity

  • reversible computing

Deleting data produces heat proportional to the operation temperature. In super high density computing this needs to be avoided.

time-reversible computing low level programming language
High level programming language that match reversible computing best are functional programming languages like Liasp and Haskell.

What is absolutely not a necessity but could boost development speed

Widely known:

  • quantum computing (depends on reversible design)
  • neural network compution (deep learning)

Barely known:

Also maybe of interest:

  • ternary (mechanical) logic - higher radix economy but worse in transporting carries - (wikipedia)
  • digital usage of analog mechanical computing mechanisms (a few bit at a time) - link
  • usage of p-adic arithmetic in processors


Alternative computing architectures that boost certain types of problems can often be used for optimization:

  • circutry; layout;
  • optimal molecular topologies for bigger structural crystolecules e.g. brackets (aka Kaehler-brackets); ...


External Links