Wednesday, May 29, 2019

Teaching the uses and limits of philosophical rationalism for large scale commercial software engineering

By the nature of computer science as an applied math and its use to train our legions of software engineers, software engineering is deeply wedded to rationalism as the primary mode of thought. But that mode of thinking has limits that are not often realized and if taught at all are poorly taught. This short essay looks at the limits of rationalism as it applies to the development of large scale commercial information systems by software engineers steeped in this tradition. It concludes with some recommendations on the adjustment of an undergraduate program that would more directly address this shortcoming and give the new software engineer a larger mode of reasoning about how to approach the task. It also has implications for the graduate school programs and the directions that future PhD students could pursue.

To keep this essay short, it is limited in scope. The focus is on the engineering of large scale information systems within a commercial context. This is for several reasons. First large scale systems cannot be created by a sole contributor. The inability for one person to satisfy the client need without coordination, communication and collaboration with peers creates a change in the techniques that can be applied. What was internal thought must now be shared and what was vague must be made explicit. Since language is the way this is done, the work cannot be done without artifacts and process.

We currently use software dominated computer systems for many purposes including entertainment and machine control to say nothing of research and artificial intelligence. But the historical domination of information systems gives us a rich history to rely on and a shared foundation from which to begin. This paper is largely dominated by that history of information systems that could be specified in formal ways.

An immediate application of any contribution toward the tasks of software engineering is the education of new engineers. This piece will not stray from undergraduate material that one might hope is universal in every university computer science program. Instead it will focus on the interdisciplinary application of that material.

We very briefly remind the reader of the philosophical concept of rationalism and its contrasting concept of empiricism. The two most common process models of software methodology are mentioned, waterfall and Agile-oriented. Then the remainder looks at specific ways in which we try to apply rationalism yet find it wanting in practice. The conclusion looks at these shortcomings and possible ways of restating the issue to offer the most promising alternative to a rationalist approach.

Rationalism has rich roots in philosophy. While it includes the philosophy of mind in this essay we look at the question of what can be taken a priori versus what must be learned. Constrained in this way we can apply that concept to many important software engineering areas including requirements engineering, specification, design and testing. While rationalism is seen in the rationalization of human process (methodology) and the intrinsic nature of formal languages and machines, it is also attempted in specific tasks such as testing and requirements engineering.

Large scale development without some degree of methodology, whether explicit or implicit, is prone to failure and not a good model for engineering. There will always be exceptions but in an educational environment we teach to what can be learned, not what depends upon an exceptional practitioner.

The discussion follows the most canonical progression of software engineering tasks as often taught in the waterfall methodology.

A common characterization of large organizations which consume the majority of commercial software engineering services is that the need or opportunity to improve the organization through some project is realized. While this alone may be interesting it is before the commencement of any recognized software engineering methodology. However we want to note that it is rare for a project to to commence with a completely blank slate either in environment or in the pre-formed attitudes of the people who will make the decisions. There is an a priori set of beliefs, processes, constraints and other limiting factors which will shape the project. Insofar as it is a purely rational process, some idealized process of project management might find the result predictable. The fact that it is not is the very truth we wish to examine.

We lie to undergraduates in the computer science program. We lead them to believe that the clients give clear requirements, (ok, perhaps we help a bit) and from those we create some high level design which is reified until we have the specifications for all the modules to be coded. If we are good at our jobs as software engineers, we take those specifications and implement some algorithm which satisfies the specification. In the best of all possible worlds, that satisfaction can be proven using static analysis giving ultimate certainty that as long as the hardware properly carries out the logic that the valid input will result in the desired output.

The failure of rationalism here is rather clear, clients are rarely capable of thinking in the extremely abstract manner that is needed to reduce a human need to a formal language. Yet they alone possess the domain knowledge and judgement to decide that the completed project is acceptable. They must be satisfied and a failure to properly interpret what is needed cannot be the fault of the client alone. The software engineer is responsible for the extraction of the necessary information.

The primary tool that allows clients and software engineers to communicate is language. This is not just natural language but also the various semi-formal languages of diagrams and models. Large scale enterprises often have extensive documentation of their processes which can form at least part of the corpus of support for a requirements document in a waterfall methodology or a reference for an Agile-oriented process. But these are not formal languages and they all suffer from the common fault of all non-formal languages of potential ambiguity. There is no way to teach how one proceeds with the reductionism of natural

Agile methodologies have successfully challenged the command and control with linear process that is the waterfall methodology. One reason the waterfall is still taught is because it is rational and easy to understand. But the reality is that the pure method was more unsuccessful than successful in the industry. Rather than believe that the business analysts have completely captured all the success criteria for the system under development an Agile process assumes that the requirements are not complete and that only a working prototype will elicit the knowledge of what is lacking in the system. This is a far cry from the methodology of formal methods that depends upon formal predicates that form the needed post conditions the system must satisfy.

"In Nicomachean Ethics, Aristotle claims that to discover the human good we must identify the function of a human being. He argues that the human function is rational activity. Our good is therefore rational activity performed well, which Aristotle takes to mean in accordance with virtue. " (Aritstotle's Function Argument from The constitution of Agency, Christine M Korsgaard, Oxford Univ Press). Many attempts have been made to rationalize corporate governance and process. But the reality of management is that it is often necessary to make decisions in the absence of perfect knowledge and this defines a limit to this attempt at complete rationalization. People will make decisions that if complete knowledge were available might be different. Such is the nature of being human. 

While systems are not humans, a similar argument applies, a good system is one which executes its function well. Function takes on a distinct mathematical definition in computer science and it is important to note that the word is qualified by "well." What does this mean?

In an earlier age we talked about functional and non-functional requirements. Those non-functional requirements are better called qualia in keeping with the work of CMU's Software Engineering Institute. So their argument goes the structure of function in a large system is not unique. There are a large number of ways to organize the ensemble yet still achieve the strictly functional specification in this narrow mathematical sense. But what can change is the other qualia of the system which can be vital to the ultimate satisfaction of the human need which motivated the project.

For large systems, the client becomes more difficult to define in the traditional engineering sense. It can be the person who pays the invoices, the person who casts the tie breaking vote when there is a disagreement over requirements, it can be the person whose business unit will benefit from the new system. Project management texts tend to call all of these stake holders in the project. There is some function of individual satisfaction that will yield a result that will "satisfice" for this collective client. But that is virtually never a well defined function. Again, there is no rationalism in the final decision regarding the intended design of the system, merely a large number of possibly contradictory or overstated requirements from many people who may disagree with each other about the true mission of the project and the system to be built. As in many other human affairs, rationalism often fails to bring consensus to a group.

Even when there is consensus on the various qualia to be achieved by the system, there is no reductionism that gives a linear path to that final design. There are heuristics, expert advise, patterns to follow and other guides but none of these represent the methodical approach that is implied by rationalism. The objective is to balance the many controllable factors to create a whole that again can "satisfice" for the problem to be solved. Much of the work of design methodology has been inspired by the auto industry which deals with a very analogous problem.

Cars must run, steer, stop, etc. These functional qualities are augmented by things like acceleration, comfort, and appeal. Metrics are easily acquired for functional properties but become progressively harder with many other qualities. How does one measure comfort? or appeal? But coming up with the proper collection of properties while remaining within the economic zone of cost and time has always been a challenge and more of an art than engineering science. This too is an area that does not yield to rationalism.

If we had rational specifications for the system to be built, we would also have what is needed to demonstrate at least partial compliance to those specifications. But design is often recursive and exploratory, not some linear process that begins with a complete specification. If a specification for the complete system is ever created, it grows with the design of the solution.

Individuals are not rational creatures. Predicting how attractive any individual will find a particular automobile is difficult although using statistical techniques it may be possible to predict some statistics for that question. Since we left the glass CRT behind and embraced bit mapped graphics we have created a progressive series of interface devices that become ever richer in their visual communication ability along with the other senses. It is not yet possible to see how far the engineering of human computer interface design will go. But one thing has become clear, the average computer science student is poorly prepared to create a usable interface without training. And the challenges of creating a linear method of going from the system design to the human interface are far greater. It requires a knowledge of the human perceptual system, principles of good visual communication as well as an ability to understand the human context in which the system will be used.

We want to believe that a software engineer is so familiar with the language and the idioms of coding that once the specification is given they proceed in some linear way to write the lines of code that will result in that system. We proceed to refute that with analogy to several other similar tasks, non-fiction writing and theorem proving.

Some writing instructors say that writing is thinking. It is not desirable for a program specification to be so reduced as to leave no room for variation. Such a specification is often difficult to work with since it hides some of the higher level knowledge to inform the software engineer how this module functions within the larger system, information that is needed to guide possible decisions to be made while coding. The coding proceeds until there is some question of design that arises that the software engineer does not have enough information to answer. The engineer must research and consider the choices and then proceed. This breaks the linear rationalistic conceit.

As we observed it is rare to have a specification for the complete system. This is the ideal state of a project that applies formal methods and when done, it is possible to methodically prove compliance to some or all of the design even before it is complete. But without that specification the best that can be achieved is a methodical process of module/system/acceptance testing which attempts to present evidence that the system under design/being delivered will perform acceptably. This also recognizes the reality of clients who will often accept less than total perfection, something economists call "satisficing".

Tuesday, May 21, 2019

Leibniz, Locke and Wittgenstein: Rationalism versus Empiricism and the Language Game Synthesis

FSE2016 Panel: The State of Software Engineering Research
Prem 3 Lines of Research - Leibniz, Locke, Wittgenstein
Rationalism epitomized by Gottfried Leibniz
We locate defects by proving a program can fail

Empiricism epitomized by John Locke
We locate defects by finding patterns of human error

Empirical evaluation of static versus dynamic typing
Cost-effectiveness of dynamic analysis (fault localization)
Ordering static analysis warnings by Naturalness
Exploring statistical language models for type inference

Wittgenstein might question semantics. What is a defect? A server? etc. Raises question of the language game. A language game recognizes zones of agreement.

Prior mentions include Goguen, Zave, Osterweil, Shaw, Garlan, Fielding, Taylor, Medvidovic, Qualitative work

Daniel Jackson, The "broken" concepts & rules of Git, Daniel Jackson 2016
The conceptual blending of "developer" and "operator" in DevOps
The architecture of IoT
Formulations of Security Agents, Policies, Principles

Are We Neglecting our Concepts?
Empirical and Formal are OK
"Language Game" areas: Architecture, Requirements, Process? Not so much.
Two risks: Fixations on tired old ideas, and missing out on radical new innovations

Joseph Goguen
The Denial of Error, Softrware Development and Reality Construction, pp 193-202
Four Pieces on Error, Truth and Reality, 1990  accessible at
Value-Driven Design with Algebraic Semotics, draft book with Fox Harrell, 2006, accessible at

Pamela Zave,
The operational versus the conventional approach to software development, CACM, Feb 1984, Vol 27 Iss 2, pp 104-118
Four dark corners of requirements engineering, Jan 1997,  ACM TOSEM vol 6 iss 1, pp 1-30

Leon Osterweil
Software Processes are Software Too, from Engineering of Software: The continuing contributions of Leon J Osterweil, ed Tarr, Wolf, pp 323-344, ICSE9, ICSE97 Proceedings of the 19th pp 540-548

Monday, May 13, 2019

The Semantics of "Bug" in Empirical Software Engineering

In my graduate studies I regularly read papers and hear people talk about software bugs. But I often find that what is meant by the word bug has a very specific meaning in their work. This short essay voices some of my concerns about the implications of this for serious work on software engineering research.

Perhaps the best comment about the meaning of the word bug comes from Dijkstra who famously railed against its use at all. The term apocryphally only came into use because of the physical nature of the post-war computers which used relays that were fouled by insects. It stuck as a way of talking about something that causes an algorithm to behave in an unintended way. But it has the unfortunate connotation of a logic error that is deus in machina, something that was not an error in thought but something that could not be foreseen. It provides far too large a loophole for algorithm designers, who design the algorithms and distances them from their lack of rigor.

In contemporary research the most common definition of bug is a reported defect or error in some bug tracking system. This can be most anything depending upon the context of the system and the human process by which those reports are created. This can include such obvious failures as an abnormal end or crash which causes the uncontrolled termination by the operating system or potentially even a crash of the operating system itself. It could be some unquantified quality that is missing such as too low a MTTF because it is difficult to find the source of the errors. It could be some expected behavior of the system which was not met but on reflection was always an expectation by the client that commissioned the system's creation. But this can also include defects that only exist because a change in the environment or requirements not anticipated at the time of the original creation. These should never be judged as all being bugs of the same category but the practicality of empirical software engineer forces them to become equal. I personally find it difficult to do any meta analysis of research because of this and doubt the results of individual papers that do not properly consider the heterogeneity of the data sources used. Can we find some agreement on the semantics of the word rather than allow it to become any observed deviation from what the end user thinks? I think this is clearly yes. But it opens a Pandora's box of semantics as it forces us to deal with design and specification, not merely the implementation.

A formal definition of bug would probably be some observable deviation of behavior from what was specified. But outside the realm of formal methods there are almost never any well formed specifications that can support this metric. For functional specifications these metrics are infrequent enough but for the previously "non-functional requirements" (software qualities) these are even rarer outside a couple domains such as military or aerospace. Even when they exist I believe the will to keep records to relate a particular code change to the one or more quality specification within its scope is lacking in most commercial products.

One technique that offers a different approach is Test Driven Development (TDD) which packages a code unit with a series of tests that become part of the automated recursion testing. This fixes the shortcoming of having an easily identified specification to match to the code unit. But I have never heard of a recursion testing system that includes the many more difficult tests such as capacity testing or performance testing. An of course testing for usability or maintainability are so difficult that I doubt anyone tries to include them in recursion testing. So while TDD offers some promise it is far from providing a framework for the kind of empirical study that the industry could benefit from.

In my conversations with Silicon Valley software engineers I have noticed a distinct trend away from anything that even bears a resemblance to the old waterfall methodologies. TDD may be the last vestige of a process that requires thought into how to articulate the behavior that is desired. This has been spurred by the strong reaction that consulting practitioners brought to the industry against waterfall methodologies. I have not yet read anything that uses the words I do to contrast the two approaches to system creation so let me explain the contrast I see.

Waterfall was a big-design-up-front. It required deep analysis and frequently caused paralysis through analysis as everyone became afraid of making an error. Agile broke that logjam by insisting on fast turnaround and the delivery of quantitized function.  It encouraged an organic approach where some prototype of the function was continually reworked and accreted function until the gap between the desired system and the delivered prototype "satisficed" and was accepted into production.

But the Agile process has an inherent defect introduced by that process, it discourages a great deal of thought into global issues of system design. For small systems or systems that do not require integration later, this is perfectly acceptable. However many large systems that will accrete their functionality over a long period of time in a partly unknown domain will not lend itself to the kind of immediate insight that allows for prescient decision making from the first prototype. There are some qualities of a system that are only evident in the whole and cannot be found in the constituent parts. These can be emergent or are qualities that can be impaired from the failure of one component. We use the term "software architecture" when we try to discuss design issues of a large ensemble of components that comprise a system. And a failure to properly appreciate the interconnectedness of these components in the beginning can lead to some very painful refactoring later in the project. It is this trend toward needed refactoring coupled with management's reluctance to acknowledge and fund the refactoring by denying the technical debt the system has accumulated. Software engineers can call this a management failure but management will call it an engineering failure as the relationship between the incremental functionality and that cost of implementing that functionality diverge because of that refactoring. At its most dysfunctional an existing system will be abandoned and rebuilt while a project not yet implemented may be canceled.

So I argue that the term "bug" can be indicative of a software engineer who is lacking in the maturity of their profession that comes after many years of watching these forces play out on real projects. I have a tendency to extrapolate from my experience to see this in the current software engineering practice. But I hear enough stories to convince myself that no one has found a magic bullet which helps a software engineer use a process that is in any way analogous to that used by engineers in other engineering fields. The reasons for this alone are interesting but beside the point; we must accept the fact that real software quality cannot be attained when the only focus on the delivered software product is limited to the most easily or badly quantified measures that sweep the subtlety of software defects away in an effort to use an existing dataset and avoid the time and expense of data gathering.

Saturday, March 30, 2019

One of the last posts was about engineering design. Software Engineers are not taught how to design and it is interesting to find so many other engineering disciplines who have devoted time to the process of design. This needs to be addressed in the software engineering education programs. But before we can talk about how to teach it, we must understand it and the good books on the subject are sparse. I am going to devote some posts to trying to organize my ideas and they will be influenced by the books I cited in the prior post, information I learned from Carnegie Mellon's Software Engineering Institute plus thoughts inspired by the architect Christopher Alexander.

Christopher Alexander, for those who do not already know him, is an architect who taught at Berkeley for many years and devoted a great deal of his career to the process of understanding how buildings are created. His PhD thesis was published as a book called Notes on the Synthesis of Form. This was a profound and abstract work that is more inspiring than informing. But it does lay out an argument that design goals create a network of connections between materials and constraints and leads to some form.

Later in Software Architecture in Practice by Bass et al they laid out a similar argument that the functional requirements in a software system do not give it the structure but instead the "qualities", what were formerly called non-functional requirements, that gave it shape. For after all, a functional system can be refactored in any number of ways without changing its functional properties. If the factoring, which is the structure of the system, does not change function, what is changed? This gets explored in their work and others.

As I see it, a key and fundamental difference between computer science and software engineering is that an engineer is generally hired to provide a service, in this case the creation of a software system that will satisfy some need. It is not done for the purpose of scientific exploration but to satisfy a client. Since economic concerns are directly brought forward as constraints, and since the human element plays a pivotal element in the use of any such work for hire, and since such work is more often than not a team effort and not the work of a single software engineer, the very nature of the task is qualitatively different. The ultimate driving force is to satisfy a client's need with economy.

Any problem solving requires a person to see what is not there. To use existing tools and processes requires an understanding of what is there and the creative possibilities of those available resources. Therefore there is always a kernel of creativity in any software engineering project. This can be as minimal as installing a commercial product or as difficult as a green field development for a completely novel and previously unsolved problem. Still the engineer must be able to both communicate with their client and have sufficient mastery of the technology to deliver on the vision that was discussed.

(to be continued)

Thursday, February 14, 2019

How Much Does Wisdom Weigh?

The movie 21 Grams riffed on a 1905 study that claimed that the soul weighed 21 grams. But when someone dies we know we lose information. Secrets are taken, wisdom is lost. Where does it go?

From a purely information theoretic position we know that brain can maintain complex states and that those states encode information. Those secrets, that knowledge of the world and other people disappears once the brain can no longer maintain the complex state it once did. Entropy wins and what we once had as a society is lost.

This fact has been riffed upon countless times in sci-fi with Altered Carbon's sleeves which are merely the meat that houses the information that can be stored and transferred. Putting aside the technical details and challenges of such a technology, it is interesting to me to ponder this invisible world of representation, of things unseen that are only obliquely represented in some state within the brain, the ghosts within the machine.

Within each person lies a universe accessible only to them. Anyone else can infer what is contained within. And our inherent ability to empathize is enabled by sharing a "sleeve" very similar to the other person. As the same species we can imagine what the other person feels. But before humans had the kind of language we had today that ability was severely limited.

Language is the glue that binds people together. Language is the magic that enabled a great leap forward for the species, not necessarily the individual. For with communal behavior our species increased its chances of survival and in fact eventually prospered far beyond what any prior species had. For all of the talk of individualism, human being are very social animals.

The spoken language enabled one very important skill, the ability to pass vital information from one generation to another in a way far more malleable and effective than genetics could. These nuggets of wisdom are called "memes", a label that suggests that they operate like genes.  Putting aside the details of Dawkin's theory, it is clear what is passed is pure information that is transmitted via nurturing in a given society. This was done long before we had a written or perhaps even spoken language.

This universe in our heads is filled with objects. Many of them are representations of objects in the world of atoms. In our heads they inhabit a world of bits that mirror that reality. As human beings who must remain alive, the accuracy of that internal representation is vital and we are gifted with a brain that can do that.

The cultural memes that support the complex societies we form also are found as representations in our minds. The leaders, the people to be feared or pleased, the ideas of what constitutes justice all swirl in that complex dynamical system which encodes those abstract objects in our brain and allow us to function within the society. But they have no weight. A brain that has ceased to function weighs the same as the wisest person they were before their death. The creation of brain state does not require additional mass, only a functional brain. It is pure information.

Language in all its forms is likewise pure information. We need some medium with which to exchange that information, sound waves through air, marks on a clay tablet or paper, magnetic encodings or other solid state devices. But the medium is not the information, only the messenger. And the communications theory of Claude Shannon as adopted by communications theorists is as good as any for capturing how that is done. Taken collectively these waves of symbols are states of matter that have no meaning to the world of atoms but only the world of sentient beings. They are a shadow world.

The Enlightenment saw an explosion of knowledge and an ability to gain significant advantage over our physical world, that world of atoms, for the great benefit of our species. We could create things to extend our power both over the inanimate world but also the world of other living creatures including other men. But the extension of our power and will was largely limited by the reach of our language and life. A dead king quickly loses sway over the people as the power vacuum is filled.

With each new technological innovation we seem to be able to extend a person's power and project it farther in time and space. Whether it is propaganda or wisdom, our language has created new empires and enabled new entities that transcend individuals. We have corporations and political parties that take on a life of their own independent of the people who formed them. They exert great power over the people who interact with them. They are powered and maintained by information as much as by any physical manifestation. And the modern world of information science is morphing into a post-modern world of overt embrace of control not through the manipulation of atoms but of bits.

Public relations specialists, advertising executives, salespeople and so many others in our modern world prosper not by their brawn but by their brain. Our society is now far more driven by finance and sales than it is by manufacturing. What was once the world of factory workers is becoming the domain of automation. And what controls the automation but the world of bits that permeates our society. And those who understand how to master that world of bits are the latest class of people who are seeing their stars rise in the economic battle.

I happen to believe that the modern world that started with the Enlightenment is morphing into a post modern world by the ascendency of the masters of bits. Bloggers, politicians and yes, information specialists now both directly and through their services make an outsize difference in our world. And more often than not, there is no rearrangement of atoms in the world to mirror these tidal waves of social change. Ignoring the mass of pulp that was printed because of the sexual revolution of the 60s, there was little that overtly changed in the world. But the social changes were enormous. We now live in a world of social revolutions that allow most to keep their heads.

Of particular concern to me is the role of the software engineer in this new world order. The market for those who can create machines that automate manufacturing processes is obvious. The ability of those same engineers to create bots which impersonate humans to influence buying behavior or political opinion is just being seen. The ability to project a corporate presence into the rich and interconnected world of human communication is only starting. People are afraid of robots. But it isn't the robots that should be feared by the ability of humans to create social controls and influence that they are not even aware they are creating. For complex systems do not always have predictable behaviors. Like climate change, any complex adaptive system will find an equilibrium but predicting the steady state of that system is beyond our ability. It turns out that any machine that can predict it is unlikely to do so faster than the system itself finds its equilibrium. This in itself can be an alarming realization.

We seem to be largely unaware of the vast networks of bits in which we live. Convergence is quickly occurring yet we still see distinct media and believe that computer systems are merely tools of commerce or communication. We have only the dimmest realization that this shadow world of bits that represents not only the shards of our minds as captured by this vast web but also the agents of others and potentially even agents that become detached from any human that have the ability to live within our information networks to continue doing the will we originally programmed into them regardless of the continuing desire to have that will propagated.

We not only need the tools to actually see this shadow world of information, we need a philosophy to understand how it serves or harms our species and its environment. We need thinkers who can move beyond the modern world of markets and commerce into a new world that is a battleground for hearts and minds, and yes, control over human behavior. I am calling this a post-modern attitude toward software engineering until I happen upon a better phrase.

Friday, November 2, 2018

Arotars in Cyberspace

The basic problem of software engineering is the creation of a computer system which solves some human problem. But the satisfaction of a human problem cannot be reduced in a straightforward way but instead forces a designer to solve many problems in parallel which ofter are in contradiction or interaction with each other. This is a design challenge and has not yet given us a linear way to perform software engineering that at once ensures logical perfection (is functionally correct) while also satisfying what are called emergent properties. This essay begins to layout the structure by which I am approaching this engineering challenge to make it tractable through a series of essays that I hope will coalesce into a book which puts a human face on the role of software engineering in human society.

The Role of Functional Engineering
We have not yet achieved technical perfection in the realm of functional engineering but we have made huge strides in the past few decades in engineering and the education of software engineers. A banking system ensures balanced books and accurate balances for the customers. A hydro-electric plant operating system will ensure safe and efficient operation of the many complex components of the system. But as computer software is the manipulation of abstractions of the real world which only later are transduced into reality, the primary task is one of model building.

The marvel of the modern computer is that of the blazing fast logic circuits that reduce even complex computation into tractable solutions. The process of programming is the reduction of the physical problems into abstract mathematical models, which after all are nothing but logical models of the real world. If the model represents a useful homomorphism to the real world, the manipulation of those abstractions through logical operations will give us control over the physical universe that solves the human problem. In the vocabulary of object oriented programming and design, we create objects for computation that model the real world. We are creating cyber models for the real world.

Software Qualities

The Linguistics of Computer Communication

The Differences Between Human Processes and Machine Processes

Merriam-Webster define avatar as the incarnation of a Hindu deity in human form. This basic definition is extended to mean any concept embodied in human form such as the avatar of charity and concern for the poor, for example. The cyber models we construct are similar to yet the inverse of this definition of avatar since those avatars are physical manifestations of abstract beings. The word seems to have no prior and accepted antonym.

Word origin and history: 1784, "descent of a Hindu deity," from Sanskrit avatarana "descent" (of a deity to the earth in incarnate form), from ava-"down" + base of tarati "(he) crosses over," from PIE root *tere- "to cross over" (see through). In computer use, it seems to trace to the novel "Snowcrash" (1992) by Neal Stephenson. If my understanding of Sanscrit is correct, the opposite of ava- would be aro-. So I backform a noun, arotar to be something from the physical world lifted into the otherworld, in this case cyberspace.

I suggest that the process of software engineering is the construction of these arotars and the success or failure of the complete computer system depends upon the accuracy of this transduction between the real and cyber world.

I want to also suggest that there is reason to go back to a philosophical debate raised by Descartes in his conception of dualism. For sometime we have comfortably accepted the mind as a physical device no different from any other device in our physical universe. Yet the dominance of social constructs in our lives gives us as many objects of an abstract nature as we have in the physical universe, allowing for the abstractions we subject our universe to in making it comprehensible. After all, by the very nature of our brain we cannot comprehend all the grains of sand but subject them to collective nouns which are abstractions of the multitude of individual grains. These social constructs result in some avatars which inhabit our physical universe such as currency which is an avatar for money. But in computer systems these objects are mere informational objects whose representation is almost infinitely mutable so long as we have an agreement about that translation from one form to another. Just as we can exchange currency, we can pour cyber objects from one object into another.

The way we build software models is with the use of specialized languages which we hope are unambiguous and useful for the task of building these models. They must allow us to build structures (nouns) and processes (verbs) and then weave these together into ensembles that will collectively solve a problem in cyberspace that hopefully maintains its correspondence to the human problem in the real world. This parallel operation has such a strong similarity to the substance of mind that Descartes spoke of I question whether we must completely reject dualism as a practical matter and instead admit of a cyberspace that is a stand in for what Descartes may have meant were he familiar with modernity. So I use this as a starting point to explore an alternate way of conceptualizing the software engineering process, if only as a thought experiment. Let's make a leap and think of cyberspace as some alternative universe that is in parallel with our own and intersects through a collection of avatars and arotars that come into existence, cross over that barrier and cease to exist. Does this buy us anything in our understanding of the place and use of computer technology as it is experienced by human beings?

You can take most any major corporation today that does business with customers and you see arotars. They interact with us not as individuals but as cyber entities. It is not our actual attributes they see but the attributes that have been set in those cyber objects in their databases. Anyone who has had problems with identify theft fully understands the implications this has in our lives. It is not the reality of our lives that controls their interaction with us but their belief about us that is formed by their databases. Once those databases have been corrupted, the responsibility falls to us to struggle to bring correspondence back between the arotar and our reality, often at great expense to ourselves. For the corporation, the arotar IS the human being.

This concept has been fairly well explored in science fiction as was suggested by the definition above which used avatar to represent the "embodiment" of the real in the cyber. This was also repeated in the movie of that name. But I am choosing to make a distinction between the physical brought into the cyberspace, what I am calling arotar, with the more traditional understanding of avatar which would be the physical embodiment of some abstract cyber object into our physical and social lives. But with that small adjustment in language the science fiction works have nicely explored the way the mind grapples with this interaction between the physical and cyber worlds.

Human Society, Its Construction and Maintenance

The Creation and Maintenance of the Social Meme Structure
speech act theory
The construction of a social reality that uses abstractions that are not directly found in nature long precedes cyberspace. Those who study linguistic pragmatics recognize that humans use language for that very purpose. We construct new concepts and then use language to communicate those concepts to others. Some of those concepts construct, constrain and mould our society. Human relations, hierarchies , and organizations are formed using these words and people have a predisposition to accept many social conventions without thought. Richard Dawkins offers a compelling argument that these concepts are in fact an evolutionary step for social creatures to break the shackles of our genetic adaptation and instead have bundles of information that are passed from one generation to another which gives the society some evolutionary advantage over other societies in the competition for scarce resources. Whether or not you accept Dawkins memetic argument may not be important to my thesis but it at least offers one way in which we discuss abstract concepts which are communicated with language and have profound influence over a society and its members. This is not possible without language.

The specific ways in which language gave primitive societies an advantage is a subject of speculation. We can assume it enabled more complex forms of coordination among the hunters and gatherers. But even the earliest recorded inferences we make about language suggests it was used for means other than mere survival. There was an oral tradition of stories that served some purpose in their societies. One use was in the telling of stories. Some of these stories purported to be retellings of history. But the standards of accuracy were not strict by modern standards and the telling of an entertaining yarn were as important as staying true to historical facts, even if the memory was accurate. In time this gave way to stories that were complete fabrications and fiction was born.

The Counterfactual, Fiction and the Communal Construction of the Intended World
Even linguists stumble when discussing fiction. When someone is acting a role in a play they do not become the character despite their best attempts. And no audience member believes the player becomes the character, no matter how convincing the performance. There is an odd social transaction that the player(s) and the audience enter into a mode of suspension of belief so as to inhabit this constructed universe of the playwright. The playwright has license to create an alternate universe and populate it with anything that can be conceived by the human mind. I do not think it is too much of a stretch to suggest that from its earliest manifestations that these alternative universes were an early cyberspace.

Of course from story telling and plays passed through an oral tradition humans found a way to fix their language using physical devices like clay tablets and other forms of writing. This innovation in fixing a story helped to constrain story telling making a distinction between that which was meant to be historical from that which was allowed to be fanciful. A written history would belie the fable teller's altered version but still allowed for embellishment where the written account was silent.

While the volume of alternative universes has grown at an every increasing pace, things did not change in any fundamental way until the creation of new technologies for the capturing of visual images. Photography, cinema, sound recording, television and radio have increased the amount of information that can either be recorded or fabricated to inform and entertain. These new media extended the evocative power of story telling to allow an even greater suspension of disbelief and engagement in the alternative universe.

The realization that language and all media can be reduced to flows of information with the most minimal and effervescent of physical trace allows for the convergence that has been long anticipated. Cinema can become virtualized with constructed actors and scenes. Music need never involve physical instruments that manipulate air. While composers may be able to construct music without the realization of the sound, most people can appreciate the realization but not the idea alone. One is the design, the other the construction and implementation of that design.

Software As Memes Trying to Live
Software is in the realm of ideas that at best are realized statically as text or binary files on a disk. However they are mutated into something else once transcribed or translated into the memory of a physical machine. Once there and once given agency, they command the machine to do its will.  The machine is in the realm of the atoms while the software reaches through the machine to create an interaction between the world of ideas and the world of atoms.

One aspect of software engineer that departs significantly from computer science is the realization of the idea using physical machines. Unlike idealized logic machines, a computing device may not behave as predicted. For example all modern machines have the operating system as an intermediary. Various conditions may result in unexpected and illogical results, such as a read failure from storage because of a software failure in the OS. To the application program this cannot be anticipated and makes no sense yet it is a condition that must be programmed for and handled. The contingency of many software instructions must similarly be allowed for and has created a great deal of art. Unless and until we have physical machines that achieve their logical perfection, we must program contingently and always include a fair dose of risk management in those systems.

Software Engineering Through the Lens of Dualism
I believe it is time to revisit and old philosophical position, Cartesian dualism. No modern is willing to suggest that the abstract thoughts of humans is metaphysically separate from the physical universe of atoms. But there can be little doubt that abstract thoughts pass from human to human using language and have little or no physical presence other than their representations in human minds and the various representations they take in the physical universe such as spoken word, written symbols or the various representations they take in computer systems. By themselves they do not interact with the universe of atoms unless mediated by human beings and the effect those utterances have on us.

These abstract ideas can  of course of profound consequence on the world of atoms. One only need consider the choices societies have made since the invention of agriculture. We are now seeing the effect of anthropomorphic climate change based on the behaviors of civilizations over eons of using fossil fuels. This change only happened because of human behavior. And that human behavior was shaped by the accumulated knowledge of how to exploit the natural resources to propagate the species.

One cannot place humans in some category that is completely different from any other animal. There are after all many species which can create profound changes to their environment as well. Populations may grow beyond the limits of their ecosystem and starve. Changes in weather patterns or meteor strikes may present challenges that a species may not survive. But humans are different in their adaptability and their ability to grow in number beyond most any other species. It is this unique ability of humans that must be examined to understand how software engineering plays a pivotal role in our society today. And this requires an examination of the work of Richard Dawkins who coined the word memetics.

Computers as Machines to Propagate Memes
Unlike other animals, humans are far less bound by their genetic destiny than most any other animal. The adaptation of civilizations to the various environments in which humans moved conferred a benefit that could not be possible without some genetic pre-determinism. And that benefit is clearly bound to the ability of humans to form civilizations and work cooperatively. But many other species are social. What makes humans different?

Insect are simple creatures. It is possible to completely decipher their DNA and given time we can create models of their behavior and mimic the relatively simple behavior they exhibit given the stimuli of their environment. Yet despite their simplicity they can become a formidable species. Such is the power of social behavior. Mammalian behavior, especially the ability for complex speech, dramatically improved the ability to communicate and coordinate. Even more important is the ability to transmit information from one generation to the next. Now adaptive behavior discovered by one generation could be passed forward to the next. Now one generation could benefit from the mistakes of the past. How to grow, hunt, build, organize society, etc, was not lost. Civilizations grew and became more powerful. Dawkins called this transmitted information memetics borrowing the analog of the gene to suggest that somehow there are bits of information that the human mind could receive from parents and elders yet carry forward and pass on to their progeny. While the science of memetics is perhaps still a bit controversial and ill-formed, the basic premise that information in a society influences that society and has a remarkable ability to resist change seems hard to refute.

The Dance between the Static and the Dynamic

Communications Protocols through Linguistic Eyes

Sunday, October 28, 2018

Engineering and the Mind's Eye: an annotated bibliography

Engineering and the Mind's Eye by Eugene S Ferguson (1972, MIT Press) is a discussion of a view on engineering that differs from the mainline education of contemporary engineers in most disciplines. It emphasizes the history of engineering in design and the inherent way in which engineering uses visual or even artistic representation to communicate and develop solutions for patrons. While focused on the physical engineering disciplines, I believe it informs an approach to software engineering that is not taught today and which is poorly understood or appreciated.

"The Secret of Design
The design of Italian fortresses illustrates a fundamental tenet of engineering--one so right and proper, so self-evident (once it has been pointed out) and now so ingrained that it has become axiomatic: Whatever an engineer may be called upon to design, he know that in order for the plans to be effective the system being planned must be predictable and controllable. The designer first defines the boundaries of the system (often involving highly arbitrary judgements), just as the fortress designer set his boundaries at the limits of cannon range. Then the permissible inputs to the system and the permissible outputs from the system are carefully determined. Nothing may cross the boundaries unobserved or unaccounted for. There is no place an ideal engineering system for unpredictable actions, either by machines or by people. Thus the assumptions and essential procedures of the fortress designer--the fortress mentality--fit naturally the needs of the modern engineering design."  p 70

This passage is pregnant with implications for application in software engineering and is just one of many passages which discuss civil engineering or other physical engineering traditions but can be mined to gain insight into how software engineering is done by contrast with how we do not follow in this tradition.

The first obvious point is how systems engineering is about the identification of system boundaries with focus on that which crosses the boundaries. This is always taught to one degree or another in a standard CS curriculum but often given only an hour, at most, in the classroom. Yet there is a great deal of subtlety in where these lines get drawn and the very central issue of figure and ground that is assumed. In the 90s a popular mantra in systems consulting was the risk/reward ratio of scoping a re-engineering effort. A large percentage of system's projects are not greenfield development but instead a re-engineering of existing function. There is benefit to the patron in the re-engineering but that benefit is tied to the scope of that effort. A small scope can have only a small benefit while a larger scope may have a larger benefit. If the relationship of cost/benefit and the risk is linear, this is an easy decision to make. But it is rarely linear and it takes a measure of educated judgement to scope the project to maximize the benefit to the final cost.

This kind of management decision crosses the boundary between the technical engineering and the project management and the organization culture and upper management.

Another insightful comment has to do with the predictability of the system. Earlier in the book the point was made that much of modern engineering has to do with the manipulation of mathematical models of the system under design. The ability to accurately model a complex physical system with math varies depending upon the system. And the arc of engineering in any discipline has been the refinement of those models, often with the insight of prior failures. Engineers have always depended upon the knowledge gained from building the scale models or even complete project based on novel ideas and then refining based on experience over time. (Tacoma Narrows is only one example). Complex physical systems are often not completely modeled by the mathematical models. Insofar as we have a solid logical model of a computer system, this would not be a problem. But at some point every computer system will interface with a human being. And our understanding of HCI is in no way modeled using a mathematical model. We simply don't have the tools to do that nor do we even know whether that would be possible.


We have become so accustomed to this subordination of technology to science that it is difficult to realize that the Renaissance engineer, trained as an artist and retaining the artist's use of intuition and nonverbal thought, had significant counterparts in the United States as recently as the nineteenth century. For example, Robert Fulton of steamboat fame and Samuel Morse, inventor of the American telegraph, wee both artists before they turned to careers in technology.   (p 155)


The Crisis in Design
In the late 1950s, the declining ability of engineering graduates to carry ut design projects was becoming painfully evident to employers and to older teachers and administrators in engineering schools.

In 1961, Pay Chenea, head of the mechanical engineering department at Purdue, observed that while the engineering sciences had developed rapidly, design had "slipped back." Perhaps that was because the "faculty of several institutions have concluded that this may be an area of knowledge that cannot be successfully taught and hence have taken steps to eliminate an experience in design from the students' program." Perhaps a better explanation of the slipping back was that given by B. R. Teare, Jr.,m dean of engineering at Carnegie-Melon University:  "Design must compete with the engineering sciences for a place in the curriculum and for academic respectability and prestige."

A 1961 report issued by a faculty committee at MIT fully justified the fears of those who saw a crisis in design. Recent graduates were criticized for "unwillingness an inability" to consider a complete design problem and for preferring to tack only the fully specified problems that could be solved by analytical methods. This tendency was more marked at the M.S and Ph. D. levels, where young engineers "feel at home in solving problems which have numerical answers--the kind of problems used in school for teaching analytical techniques." Also, these "young engineers tend to consider problems which do not involve mathematics at least at the level of the calculus as beneath their dignity--something to be turned over to a technician who [is] without the benefit of a higher education."  (pp 161-162)