Sunday, July 21, 2019

One Month Into Driving a Tesla Model 3

I have been driving my new Tesla Model 3 to work for about a month now. This blog is intended to give my overall impressions so far. My commute is 20-40 minutes each way depending on traffic and most of it is on highways. I've tried as much as possible to use the AutoPilot feature using different options to see what works best for me. (tl;dr, the Tesla Model 3 is incredible and you should buy one)

Upfront, let me say that we didn't get a Tesla for me to drive to work. We bought the Tesla for my wife to commute in. She works in a different city and commutes about 250 miles each way once a week plus a 45-minute drive from "her house" to work. The result of all this driving is that our 2014 Prius hit 160,000 miles before the 5-year loan was even paid off. Given the number of miles she is putting on it, the low maintenance costs of an electric car, paired with the fact that the electric motors should last ~1 million miles is really appealing. The fact that most of her miles are US interstate miles led us to believe that Tesla's Autopilot would also go a long way to making her commute more manageable. Due to the complexities of life though, I get to be the primary driver for the first few months.

Basics

If you have read anything about the Model 3, you know that it is a bit different as a car. Having minimalist controls with pretty much everything on a single touch screen really hasn't been a big adjustment for me. The speedometer is a bit to the right instead of center, but that was easy to adjust to. Everything I actually use on a regular basis is still controlled by the two knobs on the steering wheel or the two stalks that are pretty much ubiquitous on modern cars. Turn signals are pretty much normal. Wipers are automatic and a little button on the turn signal stalk will do a quick wipe or spray the front windshield. You switch from park to reverse to drive with the right stalk and also use it to activate cruise control and Autopilot. I can adjust volume and channels with the small control on the left side of the steering wheel. Cruise speed and following distance is altered with the one on the right.

Honestly, the biggest adjustment in driving is the fact that when you let off the gas pedal it immediately starts regenerative braking. Once you get used to this though, you begin to wonder why every other car isn't this way. I literally only have to hit the brake pedal when I'm coming to a complete stop at a light or stop sign. A huge amount of driving can be done with just one pedal and once you get used to that, driving other cars where you have to use the brake so much becomes annoying.

Now you might be unhappy that you have to keep pressure on the accelerator and can't coast, but this is easily overcome by the remarkably smooth adaptive cruise control (and Autopilot, but more on that later). My previous experience with adaptive cruise control was not pleasant. It was way too timid and would slow down so early behind other cars that I found it really hard to drive with. Tesla's adaptive cruise control is awesome and the only reason I don't use it much is that I activate Autopilot instead.

Control Panel

Things I don't use as much are all on the large flat-panel display. This shows my speed, lane tracking, and a little figure indicating where various cars are around me. By default, there is also a navigation map up at all times that uses Google maps. I've gotten into the habit of setting it to navigate to most places that I'm going as soon as I get into the car. The on-screen keyboard might annoy me except that I rarely type more than four characters before the smart predictions list the place that I'm trying to get to.

This panel also has all the other settings that I would want to play with including mirrors, driving options, security, audio, climate control, etc. I have found that the things I go to a lot are easy and quick to access. The first week I went through the options for driving quite a bit, but after that, I found the settings I liked and rarely went into them again.

At first, I was worried about having things like the mirror controls on the central panel. I'm almost a foot taller than my wife, so we do a lot of adjustments when we swap who is driving. My fear was unfounded though as the Tesla recalls the settings for different drivers and everything except the primary rearview mirror goes back to where I want it just by pressing a button that says that I'm the one driving. Most luxury cars have this type of feature and I can't overstate how nice it is if you have different drivers who need seats, mirrors, and even steering wheels in different places.

The bottom line is that while the central panel might seem daunting at first, you quickly set what needs to be set and most of the options don't get altered after that time. Contrast this to most cars where you have buttons and knobs that you have likely never pressed after driving the car for years. Why did they make a whole knob for that anyway?

Acceleration

I've been a big proponent of autonomous cars since the early DARPA Grand Challenges. When Google first announced its autonomous car project, I was super-excited. This isn't just because I'm a technophile. The main reason is that I have never liked to drive. I know that there are people who say they enjoy driving, but I never understood them. Most driving is boring. There is nothing exciting about my commute to and from work. At least I hope there isn't anything too exciting as that generally implies something very bad when it comes to rush-hour commutes.

Having said this, the Tesla is the first car that I have owned where I think some little parts of the driving experience are fun. This is mainly because of the acceleration. Note that the car we traded in for the Tesla was a Mercedes C300 4matic. It was no slouch in terms of performance, especially compared to the Prius, but honestly, I preferred driving the Prius because of all the other features of the car. The Tesla puts the Mercedes to shame. The thing is, we didn't get the performance version. We just got the long-range upgrade so that my wife could drive 300+ miles on a single charge. We didn't go for the performance upgrade. Despite that fact, the car has serious acceleration. The 0-60 in 4.5 seconds isn't that fast compared to a lot of other Tesla models, but it is still enough to push you back in the seat. More importantly, it will also go from 40-70 on demand when you get on the highway. Our old Mercedes had good acceleration, but there was a noticeable delay between I would put my foot down and when it would really accelerate. In the Tesla, it is automatic and equally effective at whatever speed. Because I'm often watching energy consumption, I try not to drive with a lead foot, but I have to admit that it is fun to shoot off the line every so often.

I also have to point out that because it is electric, it does all of this remarkably quietly.

Autopilot

There is a certain irony to the fact that the Tesla Model 3 is fun to drive in that my real goal with this car is to not actually drive. As I mentioned above, we bought this car to help with my wife's long commutes and I personally can't wait until robotaxis dominate the road providing transportation as a service. While I wait for the true revolution in transportation to arrive, the Tesla lets me get an early taste.

Our two main upgrades were the long-range and full autonomy. Of course, the current Tesla isn't fully autonomous. Still, it does a really good job on highways. Most of my commute is on highways and when my wife drives between cities it is almost all on high-quality, boring interstate. So a lot of this first month of driving the car has been spent pushing the envelope on Autopilot to see how well it does on my commute and other highway stretches.

Every morning, as soon as I get on the highway, I switch on Autopilot. This is done by pushing down on the right stick twice, something like a double click. At the lowest level, this just activates steering that holds the lane to go along with the great adaptive cruise control that I mentioned above. The result is that the car can drive down a highway with basically no input from the driver.

Of course, the current version of Autopilot isn't designed to drive independently. When you turn it on there is a message telling you to keep your hands on the wheel. In addition, every so often it wants you to apply a little torque to the wheel. When I was first testing this out, that message confused me some because I never took my hands off the wheel, and even if I squeezed tight, it wasn't happy. That's because what it is sensing is the torque, not the pressure. I just have to apply a little turning force. Not too much, because if you really turn the wheel you take back over control and Autopilot goes off, though cruise control will remain on until you tap the break.

For the first week or so I would turn on Autopilot but leave my hands on the wheel. This let me get a feel for what the system is currently good at and what causes it problems. The short version is that Autopilot is a really great driver and I'd feel much safer on the road if every car were using it. Unfortunately, they aren't and it is the poorly behaved human drivers that make it so you can't trust Autopilot completely.

It is worth noting that the three miles of highway closest to my house is a construction zone that has lights. Autopilot still does a great job in this area. I turn it one as soon as I'm heading the right direction in the construction zone in the morning and most days I don't turn it off or take control until I exit the freeway near my destination. In the afternoon I do the same thing in reverse on the way home. The success in the construction zone is largely due to the heavy traffic in the area. Autopilot doesn't do lights, so if I'm ever approaching a red light in Autopilot without a car in front of me, I have to take over. Due to the heavy traffic, I think that has only happened to me once.


After the first week, I started testing it without my hands on the wheel. I actually find this to be much more comfortable. As I said before, the sensor that detects if you are touching the wheel isn't based on pressure, it is based on torque. So even when my hands are on the wheel I still get the message telling me to apply pressure to the steering wheel and I have to do something different. With my hands off the wheel, it is just natural to reach up and push on one side when I get that message. I still pay close attention to what is going on around me and I am ready to take over, but my hands can rest on my lap or the console instead of the wheel.

After the third week, I tried turning on the option for Autopilot to automatically change lanes instead of having me verify/motivate lane changes using the turn signal. Unfortunately, the system requires that you apply torque to the wheel every time it does a lane change, so I didn't find this feature to be very useful. I also don't like all the lane change choices it makes, so I've since turned that option off.

It is interesting to note that the main changes I disagree with relate to the Autopilot wanting to move into a faster lane. As I said above, I don't like to drive. When I do drive, I'm a left lane driver because I want to get to my destination and be done with the tedious driving. I'm not a speed demon, but I do get really frustrated with people going slowly in the left lane. With Autopilot I'm a much more relaxed driver. The driving doesn't suck, so I'm not in as much of a hurry to get it over with. I'm okay staying in a lane that is going a few mph below my desired speed and that stop-and-go traffic in the image above doesn't bother me at all.

Autopilot Shortcomings

The Autopilot system isn't perfect and I do still need to pay attention, especially in heavy traffic. For my route to and from work, I've pretty quickly learned where it is likely to run into trouble. There are some specific locations like one place in the construction zone where the lanes swerve to the left and going straight in the right lane becomes is a right turn lane that I don't want to take. I just make sure I'm in the left lane there and I have no problems. There are places where the lane lines are far apart because lanes are merging or splitting where it often needs to be reassured that I'm paying attention. There is also a spot coming off a ramp where it goes a little further left than I would like. It never leaves my lane, but if there is a large truck in the next lane over it will make me uncomfortable. None of these ever cause me to take over now that I know about them.

Merging

The area where Autopilot struggles the most is merging. This should be no surprise. If you are a young driver or have recently taught a young driver how to drive, you know that changing lanes on highways is often one of the things they find most challenging. For Autopilot there are two sides to this. There is the act of Autopilot changing lanes and there is Autopilot dealing with human drivers changing into the lane I'm in.

When we first got the car, we went through the settings and made pretty much everything as conservative as possible. I still have those settings for most things so if the car in front of me slows enough that I could even potentially hit it I get a notification. However, I had to change the option for Autopilot merging. On the most conservative setting, it would never actually manage to change lanes in busy traffic, it was simply too timid. Once I realized this, I moved up the aggressiveness every few days and I have it on the "Mad Max" setting where it does a pretty good job but still can't move over in some places in heavy, stop-and-go traffic. Granted, that is in part because human drivers are jerks who don't yield when they see a turn signal. If I really have to get over in heavy traffic, I do occasionally have to take over to make it happen.

Speaking of human drivers, the biggest challenge for Autopilot is clearly dealing with other cars in merging situations. In normal flowing traffic on the freeway, it is fine. Autopilot maintains a sufficient following distance that people can get in and it does a very good job of slowing and adjusting following distance when that happens. However, in really tight traffic when you have the jerks who are just going to merge over and take off your front bumper, you have to hit the brakes because Autopilot won't notice what they are doing in time. (As I write that I realize that I could be wrong here, it might notice it, but I'm not willing to take the risk so I hit the brakes before it does.)

When I have to turn off Autopilot, it is basically always due to merging. I'd say that 80% of the time it is because I have to yield more than Autopilot would in order to let someone in. (They need to work on Autopilot's recognition of turn signals.) The other 20% is because I need to get into a lane and Autopilot isn't being aggressive enough. Keep in mind when I say this that most days each week Autopilot completes the freeway part of my commute without me taking over at all.

Braking

One of the other features that you have to play with to get comfortable is adjusting the "following distance". You can do this by pushing the right ball on the steering wheel to the left and right. They say that you are adjusting car lengths and it goes from 1 to 4. Based on my experience, that's not what is happening, but that's a good thing. A one-car follow distance at 70 mph would be stupid. Instead, what it seems to change is how aggressive the car is in terms of braking and how close it gets before it starts braking. Early on I was going between 1 and 3 depending on how heavy traffic was. Now I just leave it at 3 because I find that the 1 and 2 options do more heavy braking than I would like.

Driving the Future

In summary, I absolutely love my Tesla. There is a certain irony that the first car I've ever really enjoyed driving is one where the biggest selling point is that it drives itself so that I don't have to. Thanks to Autopilot, I find my commute to be far less stressful. The improvement to my quality of life in that regard is significant.

Perhaps the most telling thing I can say is that I've been wanting to take a road trip with the Tesla. In the past, the words "road trip" could cause me to turn and run in the opposite direction as fast as possible, but having seen how well the Tesla does on freeways, I'm pretty certain that I can now do most of the trip sitting back and just watching my surroundings or interacting with the other passengers. Granted, how well this works will depend a lot on where I'd be driving, but a lot of our potential road trips are across stretches of the western US where there are lots of highways and not many people. The one weakness of dealing with merging humans isn't really a problem in low-density traffic. Instead, I'll just get to deal with route planning around charging options in sparsely populated areas.

In conclusion, every time I take the Tesla for a drive, I feel like I'm driving a car from the future. What is most awesome is that between software updates and additional charging stations I expect the experience will only continue to get better.

Sunday, February 3, 2019

Picking a Language for Introductory CS - Why I don't like Python

Introduction

The purpose of this blog post is to explore issues related to the selection of a first programming language for CS majors. I originally started it with the intention of raising questions related to the rapid adoption of Python that is currently happening in CS departments across the US. However, I decided to make it more general to "score" a variety of different languages. I will generally restrict my comments to languages in the top 15 on RedMonk that I know people have used for introductory programming courses or which I can easily imagine people using for that purpose.

Why It Matters

There are many factors that go into why faculty and departments pick a language for CS1. Different departments and educators will weigh them all differently. The thing that I feel really is important to get out of the way is that we don't teach languages for the sake of languages. Instead, languages are a means to tell computers what we want them to do and, in that way, they are a vehicle for us to teach students the fundamental concepts that we really care about. Those concepts tend to carry across languages and time. Languages and technologies are constantly being created/updated and old ones tend to decline in usage as others replace them. These days, software development is a polyglot activity. No single language dominates the development landscape. Even the most popular of languages appear in well under 50% of the job posting for developers, so there isn't some obvious pick that is 100% essential for students to know. Contrast this with version-control systems where git is currently the clear winner. The decision of what language to use is more challenging.

So what factors should be considered when picking a language for CS1 instruction? My short list for this post is the following:
  • Ability to cover important concepts.
  • Enforcing good practices so students don't develop bad habits.
  • Low overhead for small programs.
  • Ability to solve reasonable problems without introducing too much complexity.
  • Early error reporting.
  • Make it easy to learn other languages.
  • Ability to write things that are interesting in the first semester.
  • Bonus points: be open, multi-platform, and preferably open source.
One thing that I have to point out here is that I'm specifically considering CS1 with the intent that students in CS1 intend to go on to gain a deeper knowledge of programming, software development, and computer science in general. I'm not as interested in the introduction to programming courses that are terminal or specialized for some particular application. Those courses can have very different learning outcomes that vary greatly based on the intended audience. Since I also think there can be advantages to teaching CS1 and CS2 in the same language, I'll occasionally mention things that would more likely be relevant in CS2 than CS1, but for the most part, I will focus on CS1.

Covering Key Concepts

It is critical to remember that in CS, especially the early classes, what we really want to teach are concepts. Languages are vehicles for that teaching, not the end goal. We aren't trying to teach students about Java, Python, or C++, we are trying to teach them about how to structure logic in formal grammars to solve problems in ways that experience has shown will be scalable and maintainable. Granted, CS1 isn't going to get to the point where scaling to large code bases and maintaining software in teams for years matters, but we are laying the foundation for that.

There are lots of concepts that any reasonable programming language will do a good job teaching. Things like functions, conditional execution, and iteration are found across all languages of interest. However, there are some other concepts that I think are important to teach early that not all languages are equally successful at.

Types

The notion of types is significant in all programming languages, but some languages bring it to the foreground more than others to force students to be aware of it. For the most part, this is a difference between statically typed languages and dynamically typed languages. Statically typed languages make students aware of types right of the bat. Dynamically typed languages require a little coaxing. In addition, topics like subtyping and parametric polymorphism can't really be explained or introduced at all in dynamically typed languages. Depending on when OO concepts are covered, this could be a significant omission.

The difference can manifest in subtle ways too. Consider the difference in the REPL experience between dynamically typed languages like Python and statically typed languages like Scala. In the Python REPL, executing the expression 4+5 and the statement print(4+5) produce exactly the same result. In the Scala REPL, they produce very different results, in large part because the Scala REPL shows the types of expressions. So the response to 4+5 is something like res0: Int = 9. This isn't just based on the typing system in the language, it relates to deeper decisions for those who implement the REPLs. For example, the JavaScript console in Google Chrome produces slightly different output for evaluating an expression and printing, but it doesn't show types by default. The creators of the Java jshell utility also made the decision to not show types, despite being a statically typed language, and jshell also has very different responses to the two commands. The Python REPL is particularly poor here in the decision to not distinguish at all between the two. I say this is particularly poor because I know from years of experience that one of the things students often struggle with is the difference between printing and returning values. It is useful to have a REPL that helps to make the distinction.

To make this completely clear, consider the following REPL session in Python 3.
>>> def foo1():
...     return 9
... 
>>> foo1()
9
>>> def foo2():
...     print(9)
... 
>>> foo2()
9

Now compare that to the equivalent Scala code.
scala> def foo1() = 9
foo1: ()Int

scala> foo1()
res0: Int = 9

scala> def foo2() = println(9)
foo2: ()Unit

scala> foo2
9
Which one of these do you think is going to help a student understand that there is a difference between returning a value and printing one?

Constants

This is one that I find to be significant and which many novices might not understand. Most languages have a way to express that a variable can't change. Part of me wants to say that C++ has the edge here because the use of const is so critical to good C++ programming, but C++ also lacks immutable collections, as do most imperative languages. In that regard, functional languages might be better, but pure functional languages don't let students see non-const values. In that regard, Scala gets high marks as a language where an instructor can really explore the role of mutation.

This is an area where Python completely falls down. Making a value constant in Python is way beyond what you can do with a novice. JavaScript was bad here too until ES6 introduced const.

Scope

Speaking of the additions to JavaScript in ES6, I also feel that scope is a significant concept for programming and one that can be challenging for novice programmers to understand. Most of the languages that are commonly used have block scope. The exceptions tend to be scripting languages that were created for writing only small programs and therefore have simplified systems like only having global scope and function scope. The ECMAScript committee realized that this really isn't a good thing for a language being used to develop larger programs, so they introduced let and const basically to replace var. Unfortunately, both Python and PHP still lack block scope. Python also has another very odd behavior for default arguments that is closely related to scoping that I will come back to later.

Private

If OO is done early, I would also argue that private is a really important concept to cover. Other visibility options are helpful, but making members private is a big part of what allows OO to simplify life in large code bases because you can hide away certain details and know that certain parts of state can only be modified in a small region of code. In practice, this matters less if everything is made immutable, but in CS1 the goal is teaching concepts and if a language doesn't have a construct for doing something, it makes it a lot harder to teach.


This is an area where JavaScript has been bad. One could argue that because it was prototype based, JavaScript has always been something of an odd duck in this regard and that teaching students JavaScript to start with was going to make moving to other languages more challenging in terms of OO. However, the newest additions to JavaScript make it feel more like other class-based OO languages, including the ability to declare private members. Python is still weak here as the only notion of private is by convention. I can imagine the student questions asking if something is so important why doesn't the language take it seriously.

Discourage Bad Habits

I firmly believe that the choice of a first programming language is critical for another key reason, it is hard to break bad habits and unlearn things. This rule applies to all things, not just programming. I recall my High School physics teacher telling me that about physics. I have also experienced it myself in the area of programming both from my own learning experience and from working with students. I learned to program in line numbered BASIC with GOTO and if as the primary control structures. Even after moving to Pascal and C, then C++ in college, the remnants of the style I adopted from BASIC were still present for many years.

Many readers will be familiar with the following quote from Edsger Dijkstra.
It is practically impossible to teach good programming to students that have had a prior exposure to BASIC: as potential programmers they are mentally mutilated beyond hope of regeneration.
It takes a long time to break away from the habits instilled by learning to code in line-numbered BASIC. I saw this up close early in my teaching career when I had a student who had also learned how to code that way. I think it took two years to finally convince him that the style of programming he was used to was not a good one. He missed out on a lot of potential learning because he was stuck in that old style.

The thing is, this student could defend his style because he was good at that style and as a result, he could make it work. This is challenging to deal with and I certainly don't think that it is unique to this student.

Dynamic Typing

This is the area where I worry most about students picking up poor habits with modern programming languages. GOTO isn't a problem anymore as many newer languages don't support it, few examples students will stumble across on the web will use it, and because it is a language feature, students can't accidentally start using it without seeing an example. However, abusing types is something that students can easily fall into. Indeed, some languages have major libraries that basically tell students that this is okay.

The simplest example of this is probably heterogeneous collections. In dynamically typed languages, if a student has an assignment where they have to store multiple names and ages, they could easily set up something like ["Pat", 34, "Bob", 40, "Betty", 25]. Even index elements are names and the following odd index is the age. They can easily make this work, and because they can make it work, it can be very hard to convince them that this style of programming by convention leads to brittle code that is hard to maintain and is likely to break.

The challenge of convincing students that they should do the right thing with types becomes especially challenging if they see examples of what they are doing in libraries that they use in class. One behavior that I find particularly disturbing is when functions in dynamic languages return different types based on the values of the inputs. Unfortunately, this isn't all that uncommon in dynamically typed languages, even though it produces code that is nearly impossible to maintain. One example of this is the pandas.DataFrame.xs method in the Python Pandas library, which is used extensively for data science. Statically typed languages tend to make it very clear that this type of behavior is problematic by making it painful to do if it can be done at all. As a result, students are unlikely to do it by accident and then find ways to "make it work."

Cute Tricks

Another habit that I don't want to encourage in novice programmers is "cute trick" types of solutions. These are ways of writing things that tend to produce code that can be hard to read and maintain. One example of this that jumps out to me is using short-circuit logic operators for things other than logic in languages that have "truthy" and "falsey" values instead of actual Booleans.

For example, doing something like result = funcThatIsFalseyOnFailure() || defaultValue instead of using a conditional expression. This type of code hides what is really going on and uses the or operator to do something that clearly isn't just Boolean logic.

But I can teach it the right way...

I feel that I can already hear some readers saying that they don't need languages to help enforce good style because they will teach students to the do things the right way, and they can enforce approaches that avoid the bad habits. Unfortunately, I think that this ignores how much our students learn from sources other than the classroom. Personally, I think it is good to have students learn from other sources. Our real goal should be to set them up for life-long learning. Learning from other students and other resources should probably be viewed as a positive, but we have to acknowledge that when that happens, it is outside of our control. As such, students are going to see and probably adopt whatever a language allows, even if we don't tell them about it. The more popular the language, the more likely this is to happen.

Low Overhead for Small Programs

I've seen some debate about this, but personally, I like languages used in introductory programming classes to have low overhead for small programs. This is an area where scripting languages like Python and JavaScript shine while more traditional languages fall down. Java is particularly horrible in this regard as the traditional "Hello World" program involves a large number of keywords that you can't possibly explain to students on the first day of class. I would argue that this is why teaching environments like BlueJ and Greenfoot were created for Java. Note that I love Greenfoot and have used it for teaching non-majors, but I'd really prefer to just use a language where "Hello World" reads something like println("Hello World."). The only aspect of that which needs explaining is the double quotes for making string literals, and pretty much every programming language is going to have something like that.

I'm also fond of REPLs (Read, Evaluate, Print Loop). Here again, scripting languages shine, though even Java now has jshell. The first day I talk about a language, I like nothing better than to drop the students into a REPL and let them play around. As I mentioned above though, not all REPL experiences are created equal in terms of teaching, some provide more information to help students understand what is going on than others.

I also have to point out that scripting languages aren't the only languages that score well here. Functional languages, both statically typed and dynamically typed varieties, also tend to have both REPLs and support a style that is closer to scripting languages than to languages like Java and C++.

Hold Complexity at Bay

Limiting complexity isn't just about the first day and "Hello World". Every practical language is going to have advanced methods and dark corners. Languages that are good for CS1 don't force the instructor to introduce these before they want. The complexities that I refer to here come in a lot of forms and vary by language. Some are "concessions" that the language creators made early on to make a language more practical. Others are advanced features that benefit professional development, but only complicate life for developers. Some are just elements inherent to the programming model for that language.

I would argue that this is another area where Java falls down. One simple example fits in with the last point, every Java application requires at least one usage of the static keyword, but static is really challenging to explain to novice programmers. A slightly more subtle example is the distinction between primitives and object types in Java. The motivation for this in Java is simple, when Java was created, they needed to have primitives to make the language sufficiently performant, so they sacrificed some of the purity of the object model to put in primitive types and enable programs written in Java to run faster. They had learned from Smalltalk that making every value an object was too big of a drag on performance and would seriously hamper adoption. (I have to wonder if this might also have been a factor in the late adoption of Python, which is slightly older than Java, but didn't see broad adoption until many years after Java had become a dominant language and computers had gotten much faster.)

To understand what I mean about the problem with primitives, consider the issue of converting doubles and Strings to ints in Java. These are things that will inevitably come up in CS1. If you aren't doing this in CS1 it's probably because you have either intentionally or subconsciously created examples that avoid this. If you have double x and you need an int, you would use a C-style cast like (int)x to do the conversion. However, if you have String s and you want an int, you need to use Integer.parseInt(s) to do the conversion.

Later in the semester you probably teach students to use Java collections. Then you get to explain why they can't declare a List<int> and instead have to do List<Integer>. As an instructor, you just have to pray that no student ever tries to create a List<String>[], or an array of any generic type so you don't have to explain why they can't do that.

The thing is, these are all things that are going to come up in CS1 naturally, and they are going to slow you down if you are an instructor. They also aren't anything more than artifacts of the early history of the language. You can see this in how Scala, which is also a statically typed language that compiles to the JVM, handles these same situations. The conversions to integers are done with x.toInt and s.toInt. For some reason, I never have any trouble explaining that to students. If a student chooses to make an Array[List[Int]] there isn't a problem either. Interestingly, Scala can do this without sacrificing speed either. The reason is that Scala's compiler is smarter and it compiles thing to primitives whenever it can, but that is an implementation detail that never comes up in CS1 or CS2 and would only matter if you get to the point where you are trying to do high-performance computing.

I feel that C++ also falls down in the area of exposing complexities early, but mostly in the area of the memory model and memory handling. You really can't push C++ all that far before you are going to have to start discussing references, pointers, and memory handling. These are all things that students really need to learn and understand before they graduate, but personally, I don't want to have them take time out of my CS1 course.

Early Error Reporting

One of the fundamental rules in education is that early feedback is good. It is true for software development as well, whether you are talking about finding out if a change needs to be made to the product you are building or just an error in the code. The earlier you find out about things, the less it costs to deal with it. I believe that the same applies to how students find out about errors. Students benefit from error messages that they get early as opposed to finding out that something is wrong later on.

One of the things I like to discuss in CS1 is different types of errors. I tell them about the hierarchy of syntax, runtime, and logic errors. I point out how syntax errors are generally preferred because they get nice error messages telling them what and where the problem is while logic errors are the worst because they get very little assistance in finding those errors. As a developer, I really prefer languages and programming styles that turn as many errors as possible into syntax errors and produce as few logic errors as possible.

This is another place where dynamically typed languages, like JavaScript and Python, score poorly. Both produce very few syntax errors. Most of the errors that students are going to see will be runtime or logic errors that won't pop up until they run the code and use inputs that cause the offending path to be executed. JavaScript is particularly bad about trying to get things to work so you don't even get many runtime errors and lots of mistakes become logic errors.

In professional environments, developers deal with this by building up a suite of unit tests. While testing is something that I talk about in CS1, if I have to cover testable code and a unit testing framework to help my students do proper development in a language, then the lack of syntax errors becomes something that introduces extra unnecessary complexity early.

Ease of Learning Other Languages

Back in the mid-1990s, before Java had gained momentum, the question of what language a fresh grad was going to use in their first job was as simple as C or C++. At the time, C++ hadn't diverged nearly as much from C as it had today, so a department could easily give students a deep coverage of all the languages they were likely to use in their first job. Even with the rise of Java, a 4-year degree could be structured to introduce students to every language in significant usage.

When I look at the most recent RedMonk language ranking I feel like my students could easily use almost any of the top 20 languages in their first job. Indeed, I have graduates working with languages well below the top 20 in their jobs. When it comes to programming, we now live in a polyglot world. We shouldn't even be trying to teach students every language they might use in their first few jobs because there are too many possibilities. Instead, we need to strive to lay a foundation where students learn a diverse set of languages that will enable them to pick up new languages quickly. Personally, I think it would be helpful if the language they learn in CS1 makes it easier for them to pick up the other languages they are going to exposed to in your program instead of harder. If the instructors in later courses are able to draw parallels between languages features in the language used in CS1 and the next few languages students learn, this can definitely facilitate learning.

So what are the properties of a language that can help students move to other languages? As far as I know, there haven't been any rigorous studies done on this topic. While that is unfortunate, I completely understand why it is the case. Large-scale educational experiments are challenging to do well and are influenced by a lot of different variables. Creating a study where language feature influence was larger than the noise would be very challenging. As such, I'm just going to throw out some ideas.

I feel that a language that leads well into other languages is one that isn't too odd in either syntax or semantics so that students can move to a variety of other languages and paradigms without being surprised by the new language. Let's start with the easy part of this, use a language that isn't too syntactically different from the ones they will learn next. Most people would likely agree that you shouldn't start in APL or J. I see this as an argument against Scheme/Racket as well. (I have to point out that every language will have strikes against it, and what matters is the total weight of pros and cons. For me, the dynamic typing of Scheme/Racket is a bigger killer than the syntax for the reasons listed above, but everyone places different weights on these things and there are certainly some big positives for Scheme/Racket.)

I'd even go further and argue that if a language has too many control structures that aren't seen in other places, that could be a problem as well. For example, Python supports an else clause on loops that you won't see in other languages. The use of Boolean operators as shortcuts that I mentioned above could be seen as a fail here as well.

On the semantic side, I think that scope is again significant. I have been told by a TA from Rice University that when students get to Java in their third semester, after having two semesters of Python, that many find block scope to be confusing. Outside of Python, PHP, and Ruby, block scope is the standard rule, and given that scope is something beginning students struggle with, having a first language that handles it differently than most others seems problematic to me for future growth. Python also has very unusual handling of default parameters to functions. Consider the following code.

>>> def foo(a, b=[]):
...     b.append(a)
...     return b
... 
>>> foo(3)
[3]
>>> foo(4)
[3, 4]

While the scope of b is only inside of foo, its memory is allocated at the same level as the declaration of foo and it is remembered from one invocation to the next. I have yet to find another language that has this semantics for default values to functions.

Again on the semantic side, languages that have very different models of OO from the "norm" is also a red flag for me. JavaScript recently gained classes, but the OO model in JavaScript is still prototype based and very different from every other language in significant use today. Python also has odd elements to its OO model, like the need to list self as the first argument for methods. I can see how some might see this as a nice advantage for teaching showing how the object is available, but when the student moves to another language, it is yet one more thing to handle differently.

Lastly, I feel very strongly that before graduating, students should be exposed to multiple paradigms of programming. If a major never covers anything other than the OO-Imperative style of Java and C++, they will find it much harder to pick up other languages later that either aren't object-oriented or that focus on functional instead of imperative. For this reason, I actually like the idea of multi-paradigm languages for CS1 and CS2. Of course, one has to keep in mind that multi-paradigm can break a lot of the other things we are looking for if it makes things more complex.

Interesting First Semester Assignments

One of the reasons that Java took off in the education space was the relative ease with which students would write graphical code, especially using Applets. The Applets have long since died, but the ability to put interesting assignments into CS1 and CS2 is still very strong. Interesting here can mean many things and goes well beyond graphics today to include things like playing with data, robotics, or socially relevant problems. Regardless of the details, a general requirement is that you have strong library support, and hopefully ways of bringing in those libraries that doesn't overly complicate things. This is an area where JVM languages and Python excel. Really low-level languages, like C, tend to fall down the most here because the amount of code that is required to do anything interesting is often quite large.

Other Factors

There are a number of other factors that impact my thoughts on picking a language for introductory programming. I personally also appreciate languages that run on multiple platforms and are hopefully open source. Those languages generally are better for allowing students to work on their own machines, regardless of their OS, and have tooling that is free of cost. I also really like a language that works well for both CS1 and CS2. I discuss that in more depth in an earlier post.

I also like to use a language and accompanying tools that are used professionally. It doesn't have to be a top 5 language. I'm fine with top 20 because the reality is that no single language dominates today, so whatever language you pick, the ability to learn other languages is more significant than knowing a specific language. The thing is, I'm not all that big on teaching languages and teaching environments in courses for CS majors. The reason is pretty simple, I only have students for 4 years and a limited number of hours and since I want to use the same language for CS1 and CS2, I don't feel comfortable spending that much time teaching something that I know isn't used anywhere outside of the classroom.

We also have to realize that the languages used in industy aren't fixed and what we choose to teach in colleges has an impact on them. One of the things people consider when they pick the language for a new project is whether they can hire enough developers of sufficient talent who know it. The more colleges that teach a language, the more likely it is to be adopted in industry. I'm quite certain that the broad adoption of Java in colleges played a major factor in its dominance in industry. Similarly, the current growth in Python is inevitably fuelling its professional adoption. So when you pick a language to teach, you might ask yourself, "Is this the language I want the software that runs my life to be written in?" If you don't think you want your bank or the elevator you ride in using that language, perhaps you should pick a different one.

Why People Choose Python

The language that seems to be taking over early CS education right now is Python. That is actually what prompted me to write this post because I worry about this particular choice for introductory language. There is no doubt that Python has some real advantages in the fact that it has low overhead in CS1 and that there is broad library support to enable students to do interesting things. Unfortunately, it falls down in other areas. The ones I worry most about are an inability to cover certain topics that I consider significant and the possibility for students to pick up bad habits that the language doesn't prevent.

Perhaps the most standard argument that I hear for Python is that it is "simple" and easy for students to learn. While I completely agree that Python is an easy language for experienced programmers to pick up, it isn't at all clear that being easy for experienced programmers to learn is the same as being easy for beginning developers to learn. Learning to program isn't just about syntax. It is about learning a new way of thinking, how to structure logic, and the general semantics of that syntax. Having a language that enforces more structure and which gives early feedback when rules are broken could actually be very useful for novices. Some evidence for this was provided by Alzhrhani et al. (2018) who found that students in a large data sample struggle more with Python than they do with C++. Indeed, I would argue that moving from a language that provides more error checking to one that provides less error checking is generally easier than going the other way, and that more assistance from the language to write good code is especially beneficial for the novice. Having a language that helps the student to build their mental model of the semantics of programming could actually be more significant than having a simple syntax.

I feel compelled to mention that another common argument for Python is that it teaches indentation. There is definitely truth to this, but I can't say I find this very motivating, especially since Python lacks the block scope that the indented blocks indicate. The reality is that auto-formatting tools for languages without significant whitespace are well developed and companies with strict style guides can easily have them enforced by software. In contrast to this, automatically cleaning up poor type usage in programs written for dynamically typed languages is a far more complex problem.

In some ways, the true challenge of teaching introductory programming using Python is probably summed up well by terms frequently seen in discussions of Python programs. It doesn't take much reading on Python to come across the term "Pythonic". When I asked a Python programmer about functions that return different types based on the argument values, like xs on Pandas' Datasets, I was told that was un-Pythonic. The problem is that Pythonic and un-Pythonic are advanced concepts. When you are dealing with students who are still trying to understand the basics of conditionals, iterations, and functions, they simply aren't prepared to comprehend "Pythonic". Instead of having good coding done by convention, those students need a language that does more to enforce good style.

My Choice for CS1 and CS2

If you've gotten this far, you have already shown great perseverance in our current age of short attention spans. You might be wondering what language I would pick for teaching CS1 and CS2. I actually like Scala as the language for these courses. We've been using Scala in this role at Trinity University for almost 10 years now, and I have very few complaints. As I've said before in this post, no language is perfect, but I find that the pros for Scala definitely outweigh the cons. I have some older blog posts (here and here) where I discuss this in more detail as part of my thoughts from the first ~5 years of using Scala. I'll just list some of the highlights here.

  • REPL and Scripting interface for CS1.
  • Static type checking and lots of syntax errors instead of runtime errors or logic errors.
    • Type errors prevent a lot of issues.
    • CS1 students should never see a NullPointerException.
  • Syntax/semantics allow coverage of things I care about including const/mutability, block scope, OO, subtyping, parametric types, etc.
  • Access to the full JVM for libraries.
  • Expressive syntax that combined with libraries that allow me to give interesting assignments that don't take thousands of lines to code.
  • Solid APIs that include links to the types for arguments and return values.
    • In CS2 I can cover multithreading and networking.
  • I can cover CS1 and CS2 topics without the language forcing me to talk about things I'm not ready to cover yet.
  • Uniform OO syntax without primitives.
  • Fairly standard OO model.
  • Multiparadigm so students have a nice path to C++, Haskell, Java, etc.