- grade school - list manipulation, loops, strings and if statements
- middle school - servers and clients, oop, types and validation
- high school - parsing, streams, byte code and efficiency
On other hand, that programming is a fundamental skill is an assertion that's being repeated by many without evidence. It's a useful skill that will become more useful as we go along. Yet, something like 80-90% of America, including top 1%, know nothing about programming despite their job success or quality of life. There's no direct tie in. Meanwhile, they use reading/writing and mathematics on a regular basis. So, I think people should stop making this claim as most people will mentally tune out upon seeing something like that.
This brings to a bigger point where software isn't engineered despite prior work showing how to do it. That started with Margaret Hamilton and Dijkstra in the 1960's where both of them used modularity, layering, and interface checks to build flawless systems. By the 1980's, Hansen had safe concurrency (Concurrent Pascal), Mill's Cleanroom let teams hit almost no defects first try, LISP machines let you live-edit, incrementally-develop running systems w/ no compile phase, Schell had a NSA-resistant kernel (GEMSOS) that ran other things as processes/containers on virtual CPU's (sound familiar?), and Wirth's simplicity + safe language approaches let ETH run on their students' OS's. Each of these approaches led, with small teams, to systems that reached high levels of security, reliability, extensibility, and (for Lisp machines) productivity.
Yet, the techniques that built them are largely not used by mainstream. The negative results are predictable. So, we have two problems: (a) getting nonprogrammers into programming so they can solve problems with some approach and toolset; (b) getting programmers into engineering so they can build robust, maintainable systems using lessons learned from the past. Like other engineering fields do. So far, we're doing better at (a). Some good news at least.
Far as high assurance, the level of assurance is usually applied to a high level design or some small, critical component. The cost reported for LOCK was about 30% extra on top of a solid lifecycle. Same percentage for security costs of one Special Access Program. A small team did an EAL7 IPsec VPN in relatively short time. I'd love to know cost overhead of Praxis Correct by Construction. seL4 cost a few million for highest extreme.
So, applied correctly, even high assurance methods result in a reasonable premium for certain industries. Niche as you said. Time to market is another story with high assurance killing that. Yet, medium assurance is nearly as fast and around same price as solid lifecycle while paying dividends later.
So, the cost and lead time objections are a misconception if we're aiming for medium level which is merely high quality. It benefits long-term better than alternatives. Only exception is bottom-dollar deals and one offs where it barely working is acceptable.
To put this more plainly, "I can get another if I break it, so a clay cup trumps a grail." - Mirza Asadullah Khan Ghalib, classical Urdu and Persian poet from the Mughal Empire ... quote #1 in my fortune db @ https://github.com/globalcitizen/taoup
For things like Cleanroom and PSP/TSP, they were first-time users of the process. Not long-time. Just decent developers shown a better way of getting results. In high assurance, it varied from first-time to experienced users. First time users usually gained the benefits of the method but with difficulty. There'd be at least one specialist aiding the team. Best results came from people who had done it a while. So, for high that's partially true and for stuff I push on commercial sector it's the opposite.
"A certain minimum team size is probably required to make many of these processes viable; many real world systems are written by solo coders. "
That's a legit critique. They can still benefit as I experienced myself and some others. It requires one to divide the work into periods where you wear various hats. It takes discipline to externalize one's own work and attack it objectively. Good news is that inspection, testing, static analysis, model-checking, design-by-contract... these all catch problems that are objective more than subjective. Still get plenty of results. There's been one or two threads on here where even solo people do QA on their own stuff with good results.
Still open-ended problem worth some experiments to see how much can be done at what effectiveness for a solo coder juggling work. So, good point.
"in reality it is relatively trivial to scavenge "
It's so trivial we had today yet another HN thread about distributed locks showing how people were copying one implementation and screwing it up. Then there was disagreement in the comments. There's threads like that on various critical things at least once a day or so on HN. Shows it's trivial to do and hard to do right for many things. Better to make re-usable, strong solutions to the various problems we keep encountering where possible. Can always mix high, medium, and average assurance components as a compromise where we have X goals but only Y resources. Pieces can improve over time.
"Indeed, the implementation of such is becoming near free many cases due to convergence on SoA/microservices."
I've yet to see one go 5 years under heavy load without downtime much less the 17 years VMS clusters did or 30+ years for some IBM mainframe installations. For security, things are more abysmal. I'll agree many solutions are popping up that can do amazing things. Robust, scalable, fairly secure, and near-zero-downtime systems don't seem to be among them in this nearly-free, mainstream market you're talking about. A rarity in the market in general.
So, there's still room for both medium and high assurance software. To be clear, I'm only pushing medium because even first-timers did a better job than most do today by eliminating the problem areas & doing great detection. Similar cost, too. Only hoping for high-assurance to be selectively applied to high-impact stuff that doesn't change often. A recent example was a formal verification of SCOOP: Eiffel's race-free, easy-to-analyze, concurrency model. It found issues in the model that were fixed. Three other analysis, ranging from specific to general, eliminated deadlocks, livelocks, and performance-hit respectively. That's the kind of situation where a significant investment of resources can keep providing benefits with almost no effort. Otherwise, medium assurance aka commercial + sane extras.
""I can get another if I break it, so a clay cup trumps a grail.""
Nice quote and good principle in much of current economy. Not so much my personal data or business I.P. when a byzantine failure takes out it and my hot standby. I think the comparison mainly applies to non-critical services and hardware. ;)
Regarding the quote, I don't think you quite took the spirit of it: the idea is that, given a replaceable component with failure expected, we can manage reasonably to achieve the same outcomes for less overall cost - ie. do not build the one true system to solve all availability and security challenges (which would be very expensive (the old "pick two" adage)), but rather build a system which makes use of the properties of components intelligently to reduce overall hassle (as measured in thought, hassle, time, cost, third party dependencies, untested code or processes) to achieve the same objectives, just as reliably/securely.
I think the main reason there are less modern systems running for 15+ year timeframes is that the centralized models of yore only remain valid within traditional large organizations that are slow moving: finance, government infrastructure, military, etc. Even there, they are being dismantled, as COTS hardware + FOSS topple traditional mainframe vendors through cheap and highly scalable clustering. The paradigm has shifted: today we have components like the Linux kernel and sqlite which despite not generally running a single instance for >15 years (though they are definitely capable, and with Linux it has been done; sqlite is not old enough yet!) they do run in harsh environments with limited resources in hundreds of millions - nay - billions of instances, extremely reliably.
I'll try to keep it in mind. There's a few people doing stuff in public if you want to play with it. Plus lists to stuff to try. Here's a few.
One person did an overview of Cleanroom and examples in Python, etc here:
I just pulled a list of techniques that were empirically proven to increase robustness into a Pastebin:
Note: One can pick and choose among these to find right work/benefit ratio. I'd like to see lots of experiments on mainstream software to get data on what's 80/20 rule. I'm betting on sane subsets of safe languages, code reviews, usage-driven testing, interface checks (even asserts), and fuzz testing maybe. The first four are widely available and take little work.
Case study of Altran's Correct-by-Construction approach showing great metrics:
Note: Altran does plenty of medium and high assurance work. Their method can do either. I'd rate Tokeener as Medium-High if just focusing on their part of it. Note that they got it done with 3 part-timers in less than a year with code-level proofs. Quite a leap from millions spent on teams of geniuses doing proofs in Gypsy, etc two years at a time back in the day. :)
"Regarding the quote, I don't think you quite took the spirit of it: the idea is that, given a replaceable component with failure expected, we can manage reasonably to achieve the same outcomes for less overall cost "
I'm not questioning the spirit so much as the implementation.
" do not build the one true system to solve all availability and security challenges (which would be very expensive (the old "pick two" adage)), but rather build a system which makes use of the properties of components intelligently to reduce overall hassle"
That's what I advocated. With medium assurance, it cost almost nothing extra so apply it to the components. With high assurance, if used at all, it can be used on just the most important and is by some niche players with good cost/benefit results. It's just that...
"to achieve the same objectives, just as reliably/securely."
...this has rarely proven to work. There's an old rule that you can't make high robustness systems by composing low-robustness components. They experience both individual and byzantine failures. That's why we see the companies building on them experiencing downtime that shouldn't happen on top of unavoidable downtime which I don't judge. So, certainly break the problem into smaller ones and compose simple (or cheap) solutions where possible. You just have a baseline of quality you need for the components. Commonly expressed as "weakest link" or "Garbage In, Garbage Out."
"I think the main reason there are less modern systems running for 15+ year timeframes is that the centralized models of yore only remain valid within traditional large organizations that are slow moving: finance, government infrastructure, military, etc."
Someone else speculated the same thing. There's some truth to it. Yet, there's overlap between the kinds of services running on them and in the modern, downtime-prone infrastructures. The stuff works on robust systems of old times but fails on new ones. What's the key difference? One was designed to be robust and one is a composition of pieces of known to unknown quality. Garbage in, garbage out.
"as COTS hardware + FOSS topple traditional mainframe vendors through cheap and highly scalable clustering."
They're definitely cheaper and faster. That's been the case since Beowulf clusters I built back in the day. Yet, their uptimes still haven't reached what OpenVMS hit in the 80's with bullet-proof clustering. Are you truly saying hardware and admin labor is so free right now that we can just throw dozens to hundreds of servers in 3-5 locations at every businesses' problems while ignoring the architectural, interface or code quality of any of it? I'll take QNX 2 here and 2 there over a bunch of Linux boxes any day so long as budget will allow the license. I'll have predictability, performance, self-healing, damage containment, and live updates all in a few megs of well-written code w/ POSIX apps.
See the difference between that and mainstream systems? Good architecture still matters. System/38 (AS/400), OpenVMS, NonStop, QNX, BeOS... all great architecture that squeezed way more out of any given piece of hardware, software, or admin effort. OpenVMS was also an example of medium assurance that was point of our discussion. Their Red/Blue iterations did 1 week development & test writing then 1 straight week of review and fixes with regression tests over weekends. Results were boxes so reliable admins sometimes forgot how to reboot it and such. Still not common on Windows and Linux boxes despite the great strides in reliability that nearly a billion dollars worth of labor bought those bad architectures. I like to poke at Linux folks that MINIX 3 eliminated most crashes in a few years with a small team while UNIX architecture took decades to mostly do the same thing. Architecture & assurance activities matter.
"though they are definitely capable, and with Linux it has been done"
I'd like to see evidence of that. The mainframes, AS/400's, VMS, NonStop's... these run all sorts of workloads without software repairs for years. Heavy workloads. I've investigated numerous claims of individual Linux boxes doing the same. It was almost always someone's barely-utilized, hobbyist web server, DHCP server, etc. I want to see the equivalent of a mainframe or VMS server running everything in a business 24/7 without a crash in years. Optionally that system getting lost due to never needing maintenance with people scouring the building looking for it. That was a recurring problem with pizza box servers and embedded cards running aforementioned OS's. ;)
"they do run in harsh environments with limited resources in hundreds of millions - nay - billions of instances, extremely reliably."
Again, a source for this. They certainly run regular workloads they're optimized for quite reliably. The cloud vendors optimizing on specific HW configurations with all kinds of disaster tolerance appear to run them reliably by hiding the failures. Yet, surveys of industry on their servers' downtime contradicts the overall claim with all kinds of downtime still in mainstream stuff. Plus, they show recent Windows servers ahead of Linux in reliability and AIX usually stayed on top. They don't include the stuff I mentioned due to maybe market share or perhaps the contrast would make the others look bad. ;)
Nonetheless, adopting medium assurance methods to build our components or using components built that way is a solution. It was proven... is used... in many commercial systems that have higher quality at acceptable price and performance. Outside one-offs, the only reason it's not happening is lack of knowledge or will to do it. Mainstream devs were told about the stuff for years so they just don't give a shit. Surprisingly, Microsoft is ahead in efforts as they implemented SDL, formal methods on drivers, integrity controls, app protections, safe language use, and so on. I would love to see that level of commitment on BSD and Linux side given I use both. Least they sometimes accept fixes from academics who apply various tools in their spare time or with grant funding. ;)
I will take a look at http://infohost.nmt.edu/~shipman/soft/clean/ tomorrow (evening here).
I agree with your assertions around reasonable use of prior work. I personally came to similar methods designing security-oriented systems (eg. at Kraken), eg. by strictly defining a protocol in a formalistic manner with a language suited for that purpose, then generating interfaces for client systems ... and even application-level enforcement proxies that can be inserted either during testing or (preferably) even automatically during live deployment. This amounts to automated application-level middleware, for SOA-type systems, direct from protocol spec.
Enforcement proxies ensure that broken implementation A and broken implementation B may only talk correct subset C of any given protocol, block and alarm on noncompliant communications, make otherwise restricted communication between nodes extremely difficult for attackers to use to horizontally expand any foothold established through security breaches. Assertions may be enforced in real time. Logging may occur there also. Very useful stuff. Not actually that hard to do, in practice. By contrast, the current norm of arbitrary HTTP POST with barely enforced JSON is a leaky bucket of slop.
Altran link 404s for me.
The internet's basis in decentralization roughly equates to FOSS clustering equates to torrents equates to the wisdom and utility of failable components, I do not think you can claim in good faith that embracing failure has "rarely proven to work".
Therefore, I'd like to know the source of your "old rule" that you can't make high robustness systems by composing low-robustness components, because in the face of just the above evidence it looks like bullshit. Get enough redundancy in a system and it will operate with high resilience, as long as the orchestration algorithms are well tested. Redundancy is virtually free today in hardware, software, network connectivity, legal jurisdiction and a host of other ways. Not to take advantage of this reality is to have one's head firmly in the sand. To take advantage brings benefits beyond merely security and availability, such as system mobility, peer service provider independence, legal jurisdiction independence, cheaper sourcing, easier scaling, etc.
Some of the rest of your post seems to me to be comparing apples and oranges, bygone eras to the present, monolithic transaction-oriented mainframes to general purpose complex COTS disposables, so I don't feel it warranted to discuss further.
With regards to Linux uptimes, you never specified workload, and that's hard to assure in hindsight, so you've created an impossible requirement to disprove your assertion. You do recognize however that many standard load Linux systems have run for 5+ or 10+ years with no problem, as I too know well as I've had some. Further, with changes in hardware (more work in RAM than ever, less unpredictable disk failure, higher quality cooling, more recognition of component lifetimes, ECC RAM, etc.) I expect that net COTS system longevity is increasing rather than decreasing, and Linux will not crash before the hardware in all but very unlucky cases... particularly with easily scripted, reasonable load testing.
My quote about sqlite and Linux running in harsh environments with limited resources in hundreds of millions - nay - billions of instances, extremely reliably, was primarily referring to mobile devices. Power challenges, many power cycles, physical bashing, temperature variations, heavily intermittent connectivity, dynamic clock speed migrations, moisture, etc. Furthermore, they are far more complex in terms of software stack than 1980s/early 1990s transaction processing oriented mainframes.
They still apply based on similar stuff published since then. Several are best practices for robust systems even today. The HR question should've been addressed by fact that medium assurance techniques take either little or no training: mostly just effort and accountability.
"This amounts to automated application-level middleware, for SOA-type systems, direct from protocol spec."
Great work. An example of the higher end of what I'm talking about. I did something similar and kept checks on in production. This lets me laugh about these obvious vulnerabilities like protocol downgrades while others scurry around trying to patch them. Common problem that tools can help us do better so we use tools to do it better. I'd like to see more think that way.
I cleared history and tried Altran link again. It works on my end. Try this one again:
Note: I'd really rather give you one that shows the use of the method like on the CA they built. Yet, the Altran transistion and effect of time means links are disappearing off the net slowly. Happened to old language studies, too, that weren't put into IEEE or ACM.
"The internet's basis in decentralization roughly equates to FOSS clustering "
Not quite. When a site goes down, it goes down. I have to manually look for mirrors, archive.org, etc. One can use load balancers, CDN's, etc to cluster such things. That's not default, though.
" equates to torrents"
The default at the time was FTP or HTTP. This led to problems. Following my recommend, a person sat down to think of how they'd meet their goals differently. A combo of good architecture and coding led to BitTorrent.
"I do not think you can claim in good faith that embracing failure has "rarely proven to work"."
My overall claim is that you benefit by taking extra time to apply lessons learned to architecture, design, and coding to increase effectiveness (esp quality). The two cases you cited support my point that this creates benefits rather than your original case of cheap labor throwing together whatever they can download. Most people who are stitching together components with good long-term results are using components or cookbooks made with my approach. So one may even precede the other.
Far as embracing failure, high assurance has been on that for a while. Hell, the concept even presupposes failures across the lifecycle. Most such systems are designed as interacting FSM's that incorporate failure states with a fail fast and fail safe approach. Later on, it was taken further with "recovery-oriented computing" funded by DARPA, etc. Google that phrase with words "survey" and/or "security" for some interesting architectures.
"I'd like to know the source of your "old rule" that you can't make high robustness systems by composing low-robustness component"
It came Bell of Bell-Lapadula model. Probably another person, too, though can't recall name. The DOD funded high assurance safety & security research leading up to Orange Book A1 systems over a period of three decades. Some systems were built to right principles from the start. Some tried to retrofit things like UNIX for both availability and security. All the security plus some availability retrofits failed with pentesters finding more and more obscure failures or vulnerabilities. These often resulted from interactions of overly complex, stitched-together components. That's because combining two complex programs essentially makes a third, complex program. Being unable to prove reliability or security of input components makes it difficult to say anything about output.
Note that this is true for security more than availability. HA solutions show that, depending on failure type, one can get at least three 9's out of crap systems. The HA had to be well-designed and coded. Fault-tolerance took solutions like NonStop or Stratus straight designed for them. So, both took my model to varying degrees either at middleware or whole-system level for availability. Many of these still failed at upper layers of stack, occasionally lower ones, due to Byzantine failures due to how data moves throughout the cluster & corrupts stuff. Led to Byzantine-tolerant schemes, recovery-oriented architectures, and MILS architecture for secure composition of stuff like Linux. These efforts had mixed success with the problem hard enough that DARPA & NSF are still funding smart people trying to find & prove anything about a solution up-front.
So, I think my claim is well-grounded in evidence. Availability often applies on common cases that really smart people... carefully designing and coding as I advocate... optimized the hell out of. Same at various layers of the stack. These things work well enough that average users will get results unless they stress them unexpectedly. Others are solving root problems where they can with better systems and software. Think RDBMS clusters vs Hadoop stuff vs Google's F1 RDMBS. The best method is composing a combo of what exists... in common, battle-hardened configuration/usage... with higher-quality stuff while incrementally replacing what you can as better stuff appears. This was high-assurance strategy called "incremental MLS" designed to address high costs and slow time-to-market of full, custom MLS. Worked wonders applied to web servers, databases, etc.
" You do recognize however that many standard load Linux systems have run for 5+ or 10+ years with no problem, as I too know well as I've had some. "
Most I've seen didn't. Yours did. Just collecting data to support or refute their claim. It's certainly possible. The question is how probable. It's close to 100% for some solutions in real-world loads. Those are pricey for sure but they work by design that can be emulated. I'd like to know where Linux's design & code puts it.
"comparing apples and oranges, bygone eras to the present, monolithic transaction-oriented mainframes to general purpose complex COTS disposables"
The 2016 mainframes, NonStop clusters, IBM i's, etc run similar workloads with high availability and throughput on mainframes due to Channel I/O. The modern COTS "disposables" in cloud sector are even setup to emulate mainframes with many CPU's, virtualization, metered CPU/memory use, some I/O offloading, management software, etc. It's getting more apples to apples all the time. Their reliability just still isn't there with whole datacenters going offline here and there. The management tech, on other hand, has caught up in the lights out datacenters maybe even exceeded prior work.
You want to talk disposables, though. So, let's compare good architecture on a budget vs common architecture. In embedded sector, there's uptake of Java subsets to run safety-critical applications. Common deployments are microcontrollers or PowerPC boards with weak CPU's running a watered down JVM. The JOP people and Sandia's SSP team each just built a JVM on silicon w/out any abstraction issues that would hurt them. Everything down to OS code is Java bytecode. Results were low cost, low watts, acceptable performance, and high safety. The Azul Systems Vega processors did something similar for premium, enterprise market with hardware-assisted pauseless GC and tons of cores doing native Java. A fraction of that could be implemented inexpensively with standard cell model and sold as plug-in cards to disposable market.
There's doing what's typical but keeps causing problems vs doing something different that addresses needs & root problems from ground up. Not always an option but is more than people would admit.
"I expect that net COTS system longevity is increasing rather than decreasing"
That's debatable. You cited good evidence to back it up. The counter evidence is that the ridiculous levels of complexity in modern hardware and problems on deep-submicron nodes mean things fail in little ways more often. Overall, your claim holds as hardware is very reliable given complexity level and they've forced the failures into a few, predictable spots. We occasionally get surprises like RAM fault paper. Yet, clustering covers basic stuff pretty well.
"My quote about sqlite and Linux running in harsh environments with limited resources in hundreds of millions - nay - billions of instances, extremely reliably, was primarily referring to mobile devices."
You should've said that. Yes, embedded stuff is easier to do that with. Lots of embedded devices run for years. Especially with clock gating etc where most of the chip stays off most of the time only coming online to do its work. Quite effective in mitigating transient errors. Let's see. My Galaxy freezes up on cameria here and there. Just failed to recognize SIM card yesterday requiring battery reset. Otherwise, very reliable as you say given complexity of stack.
Yet again, as it's almost a trend in this discussion, were SQlite and Linux done in the cheap/free people stich together whatever exists method you espoused or clean-slate solution that carefully designed or reviewed changes as I push? I'm cheating as I know Linux kernel process is pickier & lower defect than average. I also know SQLite has a strong review process designed to keep complexity to a minimum and quality up. Two more examples leveraging my recommendations. :P
I have come to believe, after years of teaching, that you cannot _teach_ curiosity, but you can help create environments that foster a students desire to learn and explore. As such, the point of education is to expose and empower children (people, really) to have the tools they need to explore and discover the things that they _want_ to understand and master. You really can't _make_ someone enjoy "x", where "x" is the passion of the teacher.
Programming, to me, is like writing, painting, or mathematics. In so much that it is a multi-faceted discipline. Coding is to writing as typing is to penmanship. There are _many_ reasons to write; from jotting down a grocery list to writing an epic novel, just as there are many reasons to code; from bulk automation/text transformation to diving into deep artificial intelligence. Just as there are many reasons to paint, and many reasons to do math. The "how" is dependent on the "why".
Most people don't learn to mix oil paint simply for the joys of mixing oil paints, most people don't learn penmanship simply because they long to see a well formed letterform. Few people learn the how to take the functional derivative, simply because. It's a means to an end; they learn to do it because they need to express something _else_.
I don't expect every child in my english classes to go on to write a great non-fiction masterpiece or an epic novel. In fact, most of them wont, but I _do_ think its important that they are exposed to the fundamental skills involved; penmanship, vocabulary, basic composition, in the event they _do_ want to write, or code, or compute, or paint, or whatever...
I think "school" is exposure to the mechanics, and individuals who desire more will seek out the rest.
Show me what can be accomplished--show me the best of what can be done--and let my imagination run with that.
The mechanics are generally simple and can be learned online or through routine practice. It's the passion, the love, of the subject that teachers can uniquely give their students, whether that's history or painting or programming. Show us the joy behind it, and we'll seek out the rest.
I know that mechanics and passion aren't mutually exclusive, but the question ultimately comes down to what teachers should prioritize. What's their goal? Is it to teach how to add, multiply, and divide, or is it really to inspire a lifelong love of mathematics in their students' lives?
But, I do disagree about teachers "giving passion"; I don't think thats possible. I think its possible for a good teacher, at the right time, to ignite a students curiosity in a subject. But that spark _has_ to come from the student, not the teacher. I don't care how passionate you are as a teacher, if the student doesn't give a shit, it's not happening. It's the teacher's job to fan it into flames of mastery, not create the initial sparks of interest. Although, it seems that most of the time these days, it's the teachers job to quell the sparks of interest from students and get them "back on topic".
But even still, as a teacher, I think you have to accept that making that impact is not going to happen to 100% of the students in the class, probably not even 25%. And it's questionable if the teacher really had all that much to do with it; would the student have sought out information on their own outside of the context of the classroom? Hopefully... Would they have found their own set of teachers, mentors, resources to express the thing burning a hole in their head? Again, hopefully. Would it have taken them longer to do those things? Sure.
I think, as a teacher, the hardest thing to do is not to take too much credit for the accomplishments of your students. You didn't do much, other than help orient them in the right direction, they did the hard part.
It didn't click for me that I could be a "real" programmer until after I dropped out of college (was studying Latin/Classics, no science or math background). I started building my own games and game-helper apps, in Python, while working an IT helpdesk. Observing people interact and engage with things I built gave me the confidence to pursue a career in web development, using MOOCs (MITx series was excellet) and project-based tutorials to self-educate. I stepped up to tackle more real-world problems outside the scope of my helpdesk position, mostly day-to-day scripting and org automation.
So, educational strategies like Human Resource Machine really tick the right boxes for me. If I had played that game years ago, I think I would've gravitated towards comp-sci much earlier. I needed a strong reward system (coworker's gratitude) to propel me through advanced topics, and game-ification of edu is all about treadmilling through a reward track.
I hold the title of software engineer today, without ever having completed a formal comp-sci course outside of MOOCs. I don't think this narrative is uncommon, especially among women or first-generation programmers. I clearly demonstrated the skills necessary to be a successful developer: linguistics and programming share many common themes. But I was funneled towards a silly Latin program, because I and my academic advisers thought my non-existent math/science background precluded me from computer science.
Progamming has seeped into the vernacular of day-to-day problem solving, and I think this is the angle that intro courses should seize.
The anecdote the OP gives, "learn coding so your really boring job [no offense to OP] will be less painful" is useful. Even better for high-school kids: "learn coding so you can Instagram all day at your job while your program works for you." I'm not condoning this, and obviously it's better to actually inspire true curiosity (extremely hard), but I think this kind of message would work for a lot of kids.
I'd rather have not every step of development guided by institution, just in case the methods are maybe just a little bit flawed. Maybe, just in case. Arguably, learning problem solving is a problem to be solved, so it seems almost impossible to teach, anyway.
My formal background is computer science, so somehow I can resonate with the author. My 1st programming language is C, though, not Scheme/Lisp. Damn I hate messing with pointers :D
So, what is the best (most feasible) way to teach non-CS folks to program, then?
This whole process is grounded in something real, and something that is more fun than crunching numbers or building a fake CRUD app.
I'm curious about the spate of games that directly incorporate programming. Maybe they have been more effective than I think, but for the most part, they just aren't really that fun, IMHO. They lean too hard towards the edu, and not enough toward the tainment... Particularly in comparison to, say, building a custom StarCraft map with triggers and conditions or a Quake level, that you can go play with your friends after.
My first introduction to programming was game development. Around early 2007, Bungie ( the game studio that made Halo ) released a bunch of devcasts in which a handful of the developers would all sit around, shoot the shit, and talk about building the game. I naturally thought it was awesome and looked for tutorials on Youtube. Unfortunately I didn't stick with it due to the steep learning curve and decided software development was too hard ( If only I had a mentor/teacher at the time ).
A few years later though my friend and I wanted to bot the game Runescape to make some money. The most ban-proof way at the time was to use this program called Simba and you wrote scripts with a pasacal-like language. Everything was based on the pixel color of something and most of the bots were simple procedural programs. Due to the simple nature of the scripts, you could easily write something without a ton of prerequisite knowledge and see if it worked or not. The biggest problem with this method was color and object detection, how do you know if what you're clicking on is a tree or some grass? At the time, the hotshots in the community were mostly people enrolled in cs programs and building neat algorithms to detect stuff. The only way to compete with them was to learn the fundamentals of cs. so of course that's exactly what I did!
Gaming provides a fantastic playground for learning because it's fun and there are very clear rewards for learning more ( Learn matrix manipulation, get better object detection, make more gold ). I think teachers would do well by getting games into the classroom. They really do open the imagination when paired with programming.
They ran into some issues, and have no idea how the code base works and can't fix it. I think (on my experience) schools teach you syntax, and lots of history. Not much application.
- programming is commanding a machine to do what they want.
- computers speak a non-english language so everything they write is translated before the machine follows it
- a machine is only limited to its hardware. You cannot save without a harddrive, you cannot communicate without an antenna of somesort.
Instructing a machine can come in a variety of forms
- Robotics - commands reinterpreted into physical movement
- User Experience - commands reinterpreted to produce graphics or sound
- Logical - Encoding and decoding, list management and prediction, mapping commands to actions, language grammer
If anyone disagrees or has more to add, let me know
If they are good at programming, they will do this quickly, and in a way that the computer does not have to do any (or many) unnecessary steps.
However I also observe a programmer must understand: If they can program, they can understand the computer, at least in some circumstances and at some level.
If they are good at programming, they will also be good at understanding others.
I believe mathematics teaches people to understand by helping them recognise when they do not yet understand.
To know when you have convinced someone, and yourself have been convinced, is clearly not exclusive to mathematics: Writers, artists, lawyers and lobbyists, all specifically develop these two kinds of understanding, and it is this line of thinking I am currently exploring.
No: I am not trying to say someone knows how to program if they can program.
In fact, I suspect strongly the opposite: Most people who can program do not have any idea how they program, let alone how to program.
I am saying there are at least two essential skills which are "programming", and not just the one you brought up.
There are more: One must also have a command of the grammar and syntax, but I find that despite how much time people spend on it, this speaks more about how complex languages are, than it does about programming, and so I did not bring it up.
> Are you interviewing individuals/doing this scientifically or is this a personal theory you test when given the opportunity?
I'm a professional and not an academic, and yet a big part of my role is to grow programmers, so it's a little more than a personal theory, but a lot less than a study.
Teach them CS first.
Start with the history. Make a connection from maths to CS - Logic. Show that representation is meaning. The Code is only an abstraction but it is the only way to communicate the abstract ideas, never mind there's different forms of code. Try to code to understand complexity, that the complexity is the reason that "Programming" is bigger than school. I don't mean run-time complexity, but that is an important bit.
The code is just one of the real-world applications of programming, it's secondary to problem solving, but useful enough at that. It's well enough defined in the mathematical sense to be taught as a formally structured, rigorous generality (Theory). It's highly developed and abstract enough to go a long way with intuition applied to immediate problems (Practice). Recurse, apply code to code. Dont' forget the disciplined mantras: RTFM, KISS DRY ...
Preferably it shouldn't even be a single class. Just like everyone has to learn about numbers, matter, elements or cells, it should be important to teach logic as the very basis on the search for boolean truth. Tie in self modifying code with evolution.
Instead of just introducing calculators in math, introduce python, Matlab, R, all of them at appropriate times. As a prime example of applied CS that's relevant to about any teenager with a smartphone, explain the graph theory that they are the data in.
It all depends on the children being on an appropriate level already when they enter elementary school. That depends on the knowledge of the previous generation. Therefore it's a slow progress, that picks up every iteration. There generally isn't a one size fits all program to teach programming, it depends on individual parameters. The ideal OTOH is very narrow minded and far from optimal, given many more pressing needs. Biology is booming, isn't it? Should we ask the same question in Bio, how to teach non-Bio folks to do research? It's coming along, the only hindrence I see is dangerous half knowledge and knowing to much.
If you make programming a math class, people will hate it just the same way they hate math.
Bringing Python or R in to graph the meaningless functions that we graphed on our TI-83s doesn't add much value - what you are doing is still pointless. If you want to teach high-school-level pre-calculus, I think the best bet would be to offer a 3D graphics course. Jamming math into people's heads without showing them why it would ever be useful is about as effective as jamming a camel through the eye of a needle. You've got to trick people into learning math.
> You've got to trick people into learning math
That's the one I'm opposed to because everyone has different preferences, so this tricking is incompatible with the one to many approach in industrialized education.
On the other hand, I completely agree that the lack of application is often a drag and game-programming was my favorite application, but I did that on my own time and it wouldn't have been fun if I was mandated to do so.
Also, purity in maths is motivated by the variability of applications. Purity is an ideal, of course, but it's one about keeping it simple, which is nice.
As I was saying in the OP, the applications should be individually obvious and hence voluntary. If that could be incorporated into class, that would be great, but the reality looks different. Of course my experience is biased.
I didn't come up with syntax semantics duality myself and I can't really explain it, because it's actually rather mathematical.
i think this alone probably turns away the majority of would be computationally literate individuals
as i was being taught programming i was so ridiculously underwhelmed by a program writing out hello world
here i am on this machine that can play my favourite movie, entertain me with a beautiful interactive gaming experience, and connect me to the world's information and i am supposed to be wowed by 'hello world'
i only understood the power of hello world when i became interested in embedded systems
so much time setting up the ide and connections and circuits and somehow i managed to make this previously idle pile of organised metals print hello world
that was a a profound experience but this was years after i had become a competent programmer and had the understanding of physics to appreciate what was happening
if you want to teach programming then you have to show people that the process can help them do something they want to do
there used to be classes to solely teach typing without looking
everyone i knew hated it, it was a pain for everyone in the class, then instant messenger came out and suddenly everyone was a professional typist banging out 60wpm
if social media was only available to people through bash scripts i assure you everyone would be able to program in a few weeks
because that is what people want to use a computer for right now
but that would be regressive at this point
so i'd suggest the future of programming education should be folded into mathematics education
learning times tables? write a script to express that table
even better would be to pull problems from a web api, solve, and post
I agree with this sentiment in the general case. But it reminded me of something from when I was in high school
I was taking some sort of electronics class that had rudimentary programming and the first assignment was to just get the computer to print your name. I was working with another student and this student was amazed when it worked. Amazed just that they got the computer to do anything. I was baffled by this, so much that I still remember it.
If this was a marketing course and the assignment was to sell people on the idea of learning to program, "Hello World" introductions would get a failing grade.
What's distinct about Hello World is that it's a ritualized version of learning how to write a print statement. Teachers sell it as an important rite of passage, but that just isn't how students feel about it (and they shouldn't). I think this disconnect only causes students to disengage.
Granted, if the rest of the course was just like it, I agree it would be terrible, but the first lesson itself is actually not that bad.
this is interesting
and trying to dream up languages that fail, or refuse, or do anything else but communicate, is fascinating.. what would a language like that look like, what would it be capable of?
i do like the idea that conventions supersede standards, but hello world seems even counter intuitive to the programming convention of efficiency
i said in the original comment that i think programming should be folded into mathematics education, in time parallel
stead trying to compartmentalise education into disparate parts we should be showing everything's interconnected nature
And how connected do you think computer science and mathematical education should be? I think they should definitely be different subjects, but they are definitely related. I'd be really surprised if I found a cs student from a decent program who graduated without realizing how connected they are. I'd expect the average cs student to have more programming experience than the average math major. But I think most math majors have some of the same general skill sets, and would have done well in cs classes.