John Allen, WPE-2008 Presentation

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WPE-2008 presentation by John Allen

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John Allen, WPE-2008 Presentation

  1. 1. Here are the annotated slides from my W.P.E. presentation. Any and all comments are welcome at jallen@engr.scu.edu. The obvious –and usual–remarks about copyright (and copyleft/copywrong) are expected to be honored. john 1. This was a real filing cabinet –without the palm tree—but that’s another story. The point it that it describes the structure of this talk … the “beginning, middle, end” if-you-will …while hitting some points that did not get into the two-page abstract. 2. First, the obligatory introduction. The slide pretty much says it. I’m not a philosopher; not an engineer; and not a historian. Just a curmudgeon. From wanderer to engineering to math to logic. Finally to C.S. about 1964. But not just any C.S. It was McCarthy’s version and at Stanford, with their very strong program in logic and foundations of mathematics.
  2. 2. 3. Now the talk. It’s my decade-long voyage through intellectual and mathematical history and philosophy with the goal of unwinding the development of modern engineering and engineering education. The overarching goal is to make the case that software is destined to develop similarly. History and Mystery are interlinked. They deal with mathematical physics and the interlinking with several Western cultures, and how/why different societies dealt with the transformation of the new mathematically-based results into engineering. The Ballast section is the heart of the matter. It’s an answer to a question I had: Can we bring rationality and discipline to software development; something similar to the structure developed over some 300 years of traditional engineering? My answer is yes –but only if we want to.
  3. 3. 4. There’s a difference between training and education –just as there’s a difference between construction work and engineering– and the following McCarthy quote expresses the challenge for software. There are equally apt quotes from Christopher Strachey … or Peter Landin ... or Tony Hoare 5. I was going for a “Glorious Revolution” picture since the Glorious Revolution was an entry point to the Age of Enlightenment, but Compleat Revolution was the best I had.. But first, some mathematical history.
  4. 4. 6. The critical event that supports modern science and engineering happened in the late 16th century: the introduction of symbolic algebra by Francois Viete. There were some interesting –even mysterious– interchanges between Viete in France and Harriot in England, but Viete began the adventure. Rather than solving specific equations, Viete’s use of symbolic parameters allowed the statement of general solutions and required the development of symbolic algebra. You can reduce (2+3)-2 by arithmetic but reducing (a+b)-a requires symbolic algebra including the notion of valid symbolic transformations. ****need a better example*** 7. Just for comparison, pre-Viete algebras –besides being equation- specific– were notationally clumsy. Think Roman versus Arabic notations.
  5. 5. 8. But the really powerful idea that was unleashed by Viete is described here in its original form. He understood exactly what he had done. 9. And now computational simulation also fits the diagram. 10. Looking back 400 years, the structure of his innovation is what we now take for granted in all of our mathematical modeling.
  6. 6. 11. But there’s something lurking --and usually unsaid-- in this diagram: that the relationship between the subject-matter and the model is somehow faithful. The most explicit statement of this relationship is due to Kurt Godel. This representation relationship is more applicable than the specifics of his Incompleteness results. The relationship says that you can’t just put some bullshit down on paper and say “it works!” That’s called programming. Or in mathematics a conjecture. And Godel did program. Godel numbers are an example of concrete data structures, for example. But he did more. Here what’s required is some justification –some range of applicability—for your assertion that the representation is somehow faithful. That’s his representation theorem. It’s an explicit demonstration that a representation fulfills its intended purpose. 12. And here’s John McCarthy again. Original Lisp was called Meta- expressions. The language’s domain was Symbolic-expressions –or S-exprs. Steve Russell noted that McCarthy’s Godel-like work in representing M-exprs as S-exprs resulted in a notataion that –while
  7. 7. “weird” – was human-readable; something that Godel numbers were not. So McCarthy “numbers” became a programming language. 13. A little cultural history 14. Nothing occurs in a vacuum. The mathematical and scientific innovation beginning in the late 17th century occurred in and around the Enlightenment. And actually there were several varieties of the big “E” –Radical, Moderate, and (of course) Anti-E. (The Eels) The national flavor of E greatly influenced how scientific ideas were assimilated. More specifically, the Radical E of France supported the experimentation with scientific engineering … of course it also lead to the French Revolution, but that’s another story. The English, Scotch, and American versions were more “Moderate” –bordering on “Anti” – and this spilled over into their attitude toward theory-versus-practice and by extension to their attitude toward new ideas in engineering. Hacking versus thinking. Practice versus theory.
  8. 8. The defining difference was how Descartes was interpreted. It turned on whether one questioned everything or ended the questioning when it came to “altar and/or throne,” and the status quo was given a pass. Btw: In the 17th century Descartes kept his head by saying that rational thought let us see how god’s mind worked. But by the 18th century the cat was out of the bag, and the radicals asked the obvious rational question: “why god?” 15. Some quick specifics. I like mysteries. How did Newtonian mathematical physics end up in France’s Academies?. Why France and not England?
  9. 9. 16. Quickly. Engineering developed in scope and geographical extent rapidly. Science-based engineering then progressed from France to Germany, to England, and finally the U.S. It’s interesting that the attitude toward E ideas, follows a similar pattern in terms of Radicalism. Recall Kant’s 1784 newspaper article What is Enlightenment? It was radically tinted. 17. Though Descriptive Geometry was a “killer app” it wasn’t deep theory. What situation “proved” theory’s worth? What put calculus on the engineering calendar? The “forcing event” happened with the Transatlantic cable. One simply cannot rely on seat-of-pants practice to discover cable-breaks under 2 miles of water. Game; set; match.
  10. 10. 18. Of course engineers get educated. And the style of education directly results for the style of engineering practice –theory-driven in France; practice-based elsewhere. 19. Monte Calvert expressed the division very aptly as a cultural issue --“shop versus school.” Shop-culture versus school-culture is another name for practice- versus theory-driven. Or in the early days, Moderate versus Radical. Clearly traditional engineering education is school-based. And software engineering is shop-based. I think software engineering needs to change radically. And soon.
  11. 11. 20. Here’s current software engineering. 21. To make the case for a new approach we need a “forcing event.” Something like the transatlantic cable did for electrical engineering, but for software. Something that “the practical man” cannot do.” A prime example involves the insecurity of software and a potential solution: to specify expectations and then require verifiable justification from those who claim to meet those expectations. Current practice cannot address this issue –and has no hope of doing so. We believe that Proof- Carrying Code offers hope.
  12. 12. 22. Here’s a short description of the technique. So how do we get there? How do we supply the mechanisms? There’re theory-based notions, of course. They’re the Ballast. – 23. 24. The Ballast is based on some implicit –sometimes explicit – assumptions of engineering. Namely, that properties of a construct are related to the properties of the components. 25. The ideas can be expressed as inference rules.
  13. 13. 26. But now we’re face-to-face again with Viete and symbolic notation and how to interpret its content. 27. That manipulation can appear in many forms … symbolic reduction, computation … …whatever. But regardless, the critical feature is property-preservation. 28. The issue of denotation versus sense is both logical and philosophical. Since I’m not a philosopher, I’ll go straight to the
  14. 14. logical. And we’ve all seen a Denotational Logic in one form or another. 29. Since Sensational Logic is not so well-known, here’s a thumbnail history. Brouwer was a Dutch mathematician. Heyting was his student. Kolmogorov worked independently in Russia. Curry was an American logician. Kreisel, Scott, and Howard were at Stanford in the ‘60s –I ran into these ideas from K. Martin-Lof as a philosopher, mathematician, computer scientist brought the ideas out of the logical realm and began to import them into computer science as “type theory.” 30.Classical truth is illustrated by truth tables and Tarksi’s notion of truth for predicate calculus (better: cylindric algebras, or Halmos’ algebraic logics). Not really interesting for our purposes.
  15. 15. 31. Here’s the Real Ballast. The BHK interpretation. To the Intuitionist, a declaration of truth without its justification is vacuous. This attitude is reminiscent of Descartes and the Radical Enlightenment: accept nothing without justification. But we need to answer “Is this semantics compostional?”
  16. 16. 32. Indeed! There’s a simple translation of Intuitionistic truth to something we can apply. And its semantics is compositional! 33. Here are some Natural Deduction rules demonstrating the two logics. The Sensational rules are the ones of interest. Note modus ponens. 34. Now it’s a simple step from constructive truth to programming languages that now contain some basic assertional mechanisms that we can exploit to create large-scale applications that can fit the PCC model. ML is Scheme with strong types.
  17. 17. 35. As always the difficulties are in the details. We need heavy-duty tools, not just theory. It took decades before the fundamentals of Newton’s mathematical physics became realistic engineering tools. But here help is on the way. The natural extensions of simple constructive type theory allow us to express conditions like “buffer overflow cannot occur.” 36. And we have a Representation Theorem, not just a “numbering.” Though it can’t do the hard part –handle the full specification problem– it does guarantee simple syntactic coherence. This says that the program is “well-typed.” It means that the program’s collection of type assertions is consistent. For example, we don’t ask that x be an integer (x:int) and x also be a Boolean (x:bool). The hard part requires the parasite to supply a proof; the host has it easier since proof checking is a simpler task. But that is as it should be.
  18. 18. 37. And back to Godel numbers. We have morphed proofs and propositions into programs and security assertions. 38. Finally; the point of this exercise is to allow the back-and-forth to occur without executing the code. And that goes back to a property that’s required of the language: it’s the property we mentioned early, now given the name Subject Reduction. Subject Reduction allows us to check assertions about dynamic properties without running the program. This is critical for something like Proof-carrying Code. Subject Reduction says that throughout the reduction process the type of the expression is unchanged. And in the limit, the value’s type is the same as that of the original expression. So, for example, if we show that the original program does not violate array-bounds, then throughout its execution the code is also safe, and no run-time bounds checks need to be included.
  19. 19. 39-40,41. done!!

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