Computer software deals with two kinds of information:
The term control-flow is used to describe the flow of control information. Interestingly the term “data-flow” does not refer to the structuring of data information, but to a single kind of control-flow.
Currently, PLs[^pl] deal with mostly one kind of information - data - and tend to ignore the other kind of information - control.
Typical PLs, such as Python, conflate control information with data information. The language supports creation of data structures as first-class entities, but tends to hide control structures behind various forms of syntax, like for loops and if statements.
Some languages, like Scheme, provide Continuations that tend to combine data and control into a single datum.
Operating systems wrap control-flow into ad-hoc Continuations called threads[^stack].
Object-Oriented programming dealt with structured data design, but created unstructured control-flow design[^override].
The reaction to the problems caused by this perspective was to remove all semblance of control, culminating in FP[^fp] languages.
[^pl:] PL means Programming Language
[^fp:] FP means Functional Programming.
[^override:] Overrides and super create control-flow dependencies that break the encapsulation of control information. Inheritance is useful for data construction, but anathema to structured control-flow construction.. Getters and Setters are data-access operations, which can be separated from control-flow operations. Blocks are closures.
[^stack:] Operating system threads conflate various issues that make Operating Systems appear to be high-art and magic. The (hidden) use of a global variable via the CALL and RETURN instructions is an example of such conflation. The Stack is a global variable (an optimized list). Relational and FP languages attempt to escape the use of The Stack.
Separation of Concerns
Ideally, control-flow design should be separated from data design.
Data structuring is devoid of control information.
Control-flow structuring should, likewise, be dataless - devoid of data information[^ssl].
[^ssl:] See S/SL (in References) for an example of a data-less language.
Locality of Reference
Ideally, the description languages for data construction and for control-flow construction should provide locality-of-reference.
Simplifying, this means that all aspects of a design aspect should be visible to the reader in a single glance.
This used to mean that all information related to a portion of the design fit on a 24x80 display. Today, it means that the information must fit in a window.
“Locality of Reference” also implies that there be no information leaks.
Currently, such leaks are called “dependencies”[^locality].
[^locality:] If you can’t see it all in one window, then it is not-local. Libraries, as we know them, have this problem. Import and export syntax is often used to reduce the impact of such non-local references.
Dependency-managers hide such data leaks (and non-locality of reference).
Dependency managers allow programmers to cope with information leaks, but build up a dependency debt that usually needs to be addressed in the future.
An insidious form of information leak is the use of functions! Programmers write code that contains hard-wired calls to specific functions with hard-wired input parameters and hard-wired outcomes (return values and exceptions, all strongly typed). DLLs[^dll] are an attempt to break out of such hard-wiring using indirection. [IMO, the solution lies in the use of indirection and structuring of control flows (e.g. structuring component-based systems as trees).]
[^dll:] DLL means “Dynamic Link Library”, often seen with file extensions such as
Concatenative languages deal with hard-wired parameters and return values, but tend not deal with hard-wired function names and type names.
In computer science, it has often been the case that problems have been solved through the use of nesting - aka scoping.
Structured progamming was invented to alleviate the problems of spaghetti control-flow arising from the use of assembly language to program computers.
Basically, structured programming prescribed nesting of control-flow as a solution to the problem of spaghetti control-flow.
Today, most PLs provide only structured programming constructs like
while, etc. and eschew unrestrained control-flow constructs like
GOTO is needed in Denotational Semantics and has been rebranded as
CPS. CPS arises from the concept of first-class functions. Functions are only loosely-structured, using packages and dependency managers. Types, likewise, are only loosely-structured in this way.
Further elaboration can be found at https://en.wikipedia.org/wiki/Structured_programming.
The problem of global variables was solved using nesting.
The terms scoping and local variables tend to be used instead of the term nesting.
The problem of global variables was, in fact, a problem of locality-of-reference. Global variables were not considered to be a problem until programs grew to be “too large” (i.e. they didn’t fit on one 24x80 screen or one window).
The “real” problem is one of spaghetti dependencies. How to stop programs from becoming “too large”?
Object Oriented Programming
OOP prevented a solution for structuring data and divorcing data from implementation details.
In the process, OOP included control-flow in with data and conflated data-relative operations, such as getters and setters with non-data-relative operations.
Inheritance is a useful way to organize data.
Inheritance is a poor way to organize code. In fact, I argue that one should use the opposite of inheritance - I call it composition - to organize code. Composition is seen in StateCharts - the parent statemachine can override operations of children statemachines, which is exactly the opposite of inheritance (where children methods can override parent methods).
Note that this is not a binary programming choice - one or the other (inheritance vs. composition). Conflating the two possibilities leads to accidental complexity. PLs should allow for orthogonal desription of data structures vs. control-flows.
Syntax is, currently, the manner for dealing with control-flow descriptions.
I believe that control-flow description is orthogonal to data description.
From this perspective, one should use two languages for any program -
- a data description language
- a control-flow description language
and, that neither language should contain syntax for the other kind of description. One could use a third language to plumb programs together, for example, like
/bin/bash. (I contend that syntax is cheap, languages for plumbing programs together can be designed to be “better”, and more austere, than
The trend in FP is to use pattern matching to separate control information from data information.
Separation of concerns is achieved by relegating all control-flow to an engine that is not part of the description language.
Pattern matching is well-understood, albeit under a different name - “parsing”.
Denotational Semantics is the field of describing programming languages using functional notation.
Control-flow, in Denotational Semantics, is most often handled through the use of
GOTOs (rebranded as CPS, first-class-functions, continuations, etc.).
As mentioned earlier, “data flow” refers to a style of control-flow, not to data structuring.
In the data-flow style, the operation of a component is suspended until all of its inputs have arrived. The classic example is a
result = b + c.
Result is computed only when, both inputs,
c are available.
In contrast, FP requires that all inputs arrive at a component (a function) at the same time. The component is never suspended and it operates immediately and returns its results all at the same time. Complications caused by this perspective are being resolved through the use of features like futures. Note that FP assumes that there is a single happy path, i.e. the function succeeds in returning a value(s) and everything else is considered to be an exception. Programmers are discovering the limitations of this model as they delve into distributed programming, e.g. internet, blockchain, p2p, etc.
The current model for control-flow - syntax - is based on assumptions related to 1950’s computer hardware - e.g. a single CPU[^cpu] and expensive memory.
These assumptions are being broken as programming evolves towards distributed architectures and internet solutions.
[^cpu:] Hence, the name central processing unit.
Deep Recursion, Long-Running Loops
The 1950’s model of computing has resulted in the notion that programs can contain deep recursion and long-running loops.
Neither of these constructs is appropriate for distributed programming. Programmers can create distributed programs, but ths is harder due to accidental complexities introduced by the underlying assumptions.
Notably, the assumptions have led programmers to using full-preemption and operating systems.
Full preemption has caused many accidental complexities, e.g. the Mars Pathfinder disaster https://www.rapitasystems.com/blog/what-really-happened-software-mars-pathfinder-spacecraft[^1].
[^1:] This problem was later repaired with the band-aid called “priority inheritance”.
Programmers conflate the various uses of state and lump them together.
I discuss this further in https://guitarvydas.github.io/2021/03/30/State,-Analysis-of.html.
State machines suffered, early on, from a problem called state explosion.
An small example of state explosion is demonstrated in Fig. 20 of the paper below.
StateCharts - developed by David Harel - solve the state explosion problem using nesting.
I discuss StateCharts further in https://guitarvydas.github.io/2020/12/09/StateCharts.html and https://guitarvydas.github.io/2021/02/25/statecharts-(again).html.
Note that many “successes” in programming have been built on top of the state paradigm, e.g. operating systems, YACC, LEX, REGEXP, etc.
State has been conflated with several issues, including control-flow, the global stack, etc.
Threads are one way to lasoo these issues and hide them.
Notably, threads isolate the stack used by one program from other programs (using hardware assist, wrapped by libraries called operating systems).
As programmers approach distributed architectures, the limitations of threads become more apparent.
Threads are more like assembly-level operations provided on a single CPU than a high-level concept useful for programming distributed systems.
See the References section for S/SL - a dataless language that was, ostensibly, used for constructing compilers (aka “big” programs).
As mentioned earlier, CPS is a re-branding of the concept of GOTOs. In fact, CPS is more “powerful” than GOTOs.
The problem with GOTOs was not the GOTOs themselves, but the unstructured use of GOTOs.
PL designers conflated two issues - data construction and control-flow construction - which resulted in accidental complexities.
Creating languages that address both problems at once creates difficulties and unnecessary complexity.
Components are scalable only if they are not inter-related.
Scalable components cannot have dependencies on one another.
Currently, most PLs provide a handful of hard-wired types and a way for programmers to define further types.
Programmers are accustomed to writing specialized code to further validate data.
This is but another form of type checking.
The fact that three forms of type checking exist (hard-wired, programmer-defined, input validation) is a tell that the concepts of type checking are overkill and non-uniform.
Currently, most PLs create names that are absolute and global to the whole application.
Modules, packages, etc. have been invented to constrain the use of such names.
At present, I believe that dependencies are a first-order problem.
Hidden dependencies exacerbate the problem.
Languages must be designed to allow construction of independent units. (For example, calling functions by-name should be disallowed).
Package managers and build managers (like make) facilitate our use of dependencies, instead of making such dependencies more obvious.
Libraries that depend on other libraries (ad infinitum) contain hidden dependencies build up dependency debt.
I believe that diagrams show dependencies more readily than textual code.
Whiteboards are found in every software shop.
This observation is a tell - it indicates that something about current PLs is inadequate.
(My suggestions follow).
Software components are asynchronous by default.
Synchronous components are the exception, not the rule.
Software components “live forever” (like web servers).
Components that wake up and die are the exception, not the rule.
Software components can be supplied inputs at different points in their lifetimes.
Components that need all inputs at once are the exception, not the rule.
Componenets that need all inputs delivered in single blocks are the exception, not the rule.
Software components can produce outputs at various points in their lifetimes.
Components that provide all outputs at the same time are the exception, not the rule.
Components that provide all outputs in single block are the exception, not the rule.
Exceptions are not exceptional.
Exceptions produced by components are the same as all other outcomes produced by components.
The problems and the solutions dictate which outcomes are considered to be erroneous. Software Architects design solutions that produce the desired outcomes.
Software components have input and output ports.
Most current PLs have APIs that imply synchronous operation.
One Universal Type
Components are plugged together port-to-port where ports have a universal, simple type, e.g. message.
The above does not imply that type-checking is discarded.
Types checking is done in a pipeline, from simple to more complex.
Type checkers are, already, interpreters that filter incoming information and raise errors.
Type checkers become regular software components, with one input and two outcomes (data to be checked, data that satisfies the check, error condition, respectively).
Multiple checks can be inserted into the pipeline, suiting the problem-at-hand.
Not all software components need to fit this simple - one-in-two-out - model.
Components are built in layers.
No layer contains more units than can be comprehended, e.g. 7±2.
Components can, themselves, contain layers, recursively.
Long running loops and deep recursion are not allowed.
Long running loops and deep recursion can be broken into smaller steps by sending continue messages to the looping component(s) (or self-sending such messages).
Compilers can insert yields into loops (say, the bottom of a loop) and new syntax can be used to flag deep recursion for automated breaking-up.
This is, essentially, mutual multitasking.
Most programmers quote Windows 3.11 as a failed attempt at mutual multitasking, but do not balk at the idea that applications may contain bugs (esp. early on).
Preemptive multitasking is a special technique - an exception, not the rule - that needs to be employed when building operating systems. There are very few programs that need to use this technique, e.g. the software called Linux, Windows, MacOS, etc.
Applications do not need preemptive multitasking internally. There is no need to pay the cost of preemption[^2] when it is not needed.
[^2:] Preemption does have costs, e.g. (1) hardware facilities, (2) accidental complexities, (3) overkill use of l libraries (often called “operating systems”), etc.
Diagrams are a way to visualize multiple outcomes.
Diagrams are a way to show nesting and locality of reference.
Diagrams can visualize information leakage.
Diagrams make it difficult to draw leaky components, especially when everything (e.g. function calls) is made explicit.
Example Diagram Scenario
A software component is represented as a box.
Software components are asynchronous.
Lines represent message flow paths.
Software components contain input and output ports.
Input ports are small green circles.
Output ports are small yellow circles.
A Dispatcher routine invoked ready components in a random order.
All names are relative to components.
Components have 5 namespaces:
connections between components
A component refers to a component that is contained in it by using a name (e.g. “inner”) or and index (1, 2, 3, …), for example:
Likewise, it can refer to a named input “in” as, for example: