Keep in mind that I don’t have time to read several 1000-page tomes
about abstract programming.

So are you asking for someone to give you a five step check list that will make you a skilled programmer? That’s not going to happen!

As with any other discipline, if you want to get good at programming you have to spend time and effort practicing and studying. You learn to write clear, elegant code by writing a lot of code and carefully reading other people’s code. Some of those 1000 page tomes will actually save you time by summarizing the hard lessons other folks have learned. It’s delusional to think that you can become a skilled programmer as a painless side effect of getting a physics Ph.D.. It’s not that you can’t come out of a physics Ph.D. with mad programming skills, it’s just that it will cost you time and trouble.

Code Complete is a good introduction to the mechanics of software development, including advice on how to write and structure clear, maintainable code. Yes, it is a huge tome, but it certainly not as dense as say, Dirac’s “Principles of Quantum Mechanics” or MTW’s “Gravitation”. Code Complete is as close as you are going to get to a five step checklist to writing better software.

Matlab, VIM, C, MPI, and Valgrind are excellent tools to know. You don’t mention using a version control system. If by some fluke you are not already using a version control system you must start using one immediately. Version control is also a god-send for writing your thesis. Other basic tools you should know are a debugger, an execution profiler, a logging framework, and a unit testing framework. You don’t have to read a 1000 page book for each of these. Work through the online tutorials to get the basics and then start working with them. Delve more deeply into the documentation as your needs become more sophisticated.

Advising you about learning computer science fundamentals (as opposed to software construction fundamentals) is more difficult. You don’t specify what problem you are working on, whether you are developing new algorithms or applying existing algorithms. Depending on your research problem a survey of the basic data structures and standard algorithms might be helpful. Other problems would benefit more from a solid background in numerical analysis. If you do want to learn the basics of algorithm analysis there several good texts. The Algorithm Design Manual and Introduction to Algorithms spring to mind. There are also a couple of good introductory courses available online now: Design and Analysis of Algorithms, and Algorithms.

My background is a little similar to yours-I was a physics graduate who was self taught programming. After I graduated though I took on a few IT jobs and ultimately became a software engineer; including a bit of time working on OpenGDA (software used to run experiments at various synchrotron sites).

The main thing I learned about the questions you have while I was getting here is that it’s much easier to get these skills from other people than to try to pick them up yourself. An experienced mentor can easily help you identify where your code is weak or where common patterns and practices can help you. While I learned how to write C and Objective-C by myself, I didn’t know exactly what I didn’t know (if you see what I mean) until I was working with other people on the same code. The fact you’re asking here for advice means you’re doing better than I did already :-).

Now, where do you find a tame professional software engineer? I recently joined MentorNet, a system that partners experienced programmers with protégés.

But you don’t have to go for a formal system like that. Finding a local programmer meet-up group (or where your university’s software engineering department goes after work on Friday) is a great place to start.

No Royal Road to Software

In ancient times, Euclid was asked a questions like yours by his student King Ptolemy. His response: “There is no royal road to geometry.”

You mention that your supervisor would laugh if he knew how much time you spend trying to write code like a professional developer. Others answered your questions with a laundry list of things to learn ranging from source control to Design and Analysis of Algorithms.

They fall short of your goal:

“I need to spend time on actual physics”

Concert Pianist or One Man Band?

The world moves too fast for people to dabble. If you want to be a concert pianist, don’t divide your time learning instruments to become a one man band.

My concept for the role of a PhD in physics on medium to large projects is as an idea leader for system definition, expert in theory, subject matter expert during use case creation, and end user / judge for results generated by software artifacts. Work closely with the best software engineers you can.

How do I assess if my work is also good from a programmer’s perspective?

If you want to set the bar high, start here:

Software Architecture in Practice, Len Bass, Paul Clements, Rick Kazman

Look for the chapter “Understanding Quality Attributes”. Beyond code, it considers usability, modifiable, performance, security, availability, reliability, testability, maintainabiilty, and portablity (not can you carry it, but can you port the design from one platform to another). All need specific measurable goals. Similar references include:

http://msdn.microsoft.com/en-us/library/ee658094.aspx

http://www.sei.cmu.edu/reports/95tr021.pdf

Your Goals vs. C and C++

Like FORTRAN, these are hard and old languages. Positive indicators for C/C++ include:

• Application with hardware, embedded systems.
• Existing project you want as a starting points.

There are a lot of people doing web development, data visualization, and big data. Many are motivated to find or make other languages. For example, physicist Sir Tim Berners-Lee made his success with HTML (but is little know for physics). Evaluate your goal vs. your programming language.

Consider Using Matlab

Matlab has a great installed base, is specialized for math and sciences. It has tools for data visualization. It allows scientist and mathematicians to express problems in the problem domain rather than the solution domain. Matlab makes a Parallel Computing Toolbox and Distributed Computing Server products.

I expect Matlab’s success is due to using multidisciplinary teams with people who are experts in physics, math, electronics and instrumentation, operating systems, programming languages, software development, software testing, software architecture and design. The analogy may be a stretch, but why would you put yourself out there alone, starting with a hammer, chisel, and rasp to make something when you have a 3D printer available? As Newton might ask, why not stand on someone’s shoulders?

How do I assess if my work is also good from a programmer’s perspective?

• Is it correct? Does it produce correct results in all cases?

• Are other people able to read and easily understand your code?

• When your supervisor says “Great, now make it also do X…” do you have to rewrite a lot of code?

• When you’ve written a program, does it become a tool that you can use over and over, or is it a use it once and throw it away kind of thing?

If you can answer yes, yes, no, and ‘yes, I try to make tools rather than one-off calculations’, then you’re doing pretty well already. A good deal of what we do as programmers is meant to help with the kinds of things listed above.

You will be able to go a long way in Physics without knowing anything about “professional” style (speaking from experience). But I have seen many people waste endless time because they lost track of what they where doing or after having grown their code for a couple of years just got lost in it’s complexity (even in academia there is no “throw away” code, but things stick around much longer than you think initially).

I would suggest you get a head start into algorithms and data structures, e.g. with this course. After that you should be able to think about performance on a more productive level and be able to follow up with e.g. articles on Wikipedia.

After that get used to what is available in the core of your language, e.g. for C++ cppreference.com. I would also strongly recommend you to read the Effective C++ series by Scott Meyers and Accelerated C++ by Koenig & Moe. At least for C++ this will give you a solid foundation on the language side.

In parallel you should try to get to know your tools well. It isn’t unlikely that you will develop your code under Linux, so try to learn how to get more diagnostics (warnings) from your compilers (at least gcc and clang). Also learn about static analysis tools like cppcheck or clang’s scan-build. Learn how to make these tools an integral part of your development process, e.g. by integrating them into your build setup (yes, you should use at least GNU make, or even better something like GNU autotools/cmake/…). You should also add profiling tools to your toolset. For C++ I would strongly recommend you to learn everything you can about valgrind which can profile on a very low level (it also can help you find resource leaks).

All this will help you focus on what you care most about (your research) instead of wasting time finding bugs or doing useless optimizations. Of course this is almost impossible to sell to an advisor, but they (and you) will be impressed but the speed with which you be able to get reliable results.

You mentioned C and C++, but for numeric calculations I cannot recommend Python with numpy and scipy enough. It allows you to write in a clean pretty clean language on a very high level (you can even work interactively), while still taking advantage of extremely optimized routines implemented in C, C++ and FORTRAN. Also, interfacing your own C or C++ code with Python is almost trivial.

Your programs will be completely different from commercial source code, therefore many good practices and approaches won’t apply in your day-to-day source code development. But there is a good way to learn a few tips and tricks.

Let some good software developer review your code and optimize it together. It will give you much more experience and will teach you good practices. Also review source code written by other people. Search for open source projects on sourceforge or github and read their source code.

But most of all, think whether you actually need to learn anything new in order to accomplish your goals. Doing unnecessary stuff just to make the code look prettier won’t add any value to your applications.

As far as becoming a better programmers is concerned there is no magic bullet. If you’re self-taught the key is self-awareness, which it sounds like you have. However, learning to code well mostly comes down to reading and practice.

Being critical of your own code is one of the best ways to get better. Always be asking yourself:

• Will this be easy to change?
• Is this easily testable?
• Can I simplify this? Can I easily understand this when I see it again in 3 months?

My other suggestion would be don’t lock yourself into C/C++. While those are good languages that are used for a reason, they require you to do a lot of things that are not subject matter related. Look into Matlab, I’d be surprised if the university doesn’t have that available for you. Consider a scripting language like Python. Strongly consider picking up a functional language like Haskell – the paradigm is very mathematical in nature and would likely fit your problems like a glove. In short explore some other languages/paradigms. Even if they don’t become a permanent tool in your belt they’ll make you a better programmer.

You may also want to look into some algorithm design. I suspect having gotten the job, you’re already relatively up to snuff on this, but algorithms are incredibly important when doing numerical analysis. In fact, I would suspect, there are resources specifically geared towards numerical analysis algorithms.

Never lose sight of your primary purpose in writing the code. You need to get things done. Becoming a better programmer is one method to do that. Selecting the right tools for the job is another.

First,”elegant” is a relative term. Abstraction might seem elegant to you but to another C aficionado, it might seem unnecessary. Anyways,to answer your question, you should try posting your code for review on http://codereview.stackexchange.com.
Digressing from the main point, some unsolicited advice based on my own experience. If you can get all your work done with just C, then why do you want to code it in an abstract way? By this, do you want to enable others to resuse your code? If you really have a solid reason to switch to C++, go for the abstraction and learning C++ and OO concepts. Otherwise drop the idea. In my humble opinion, should n t you aim at your code being more readable and your scientific results reproducible than giving it OO abstractions? I myself had this kind of obsession to learn OOPS and code “elegant”ly. But C++ will take time to master.You will have to learn memory management since garbage collection is not automatic in C++. Take my advice since i worked for a research lab myself and lost lots of time learning C++ and OO, instead of concentrating on the main work at hand.

Considering your mention of lack of time to study theory.

If you’ve looked back at your old code after a few months and wondered “what kind of an idiot wrote that code”, you’re making progress.

How did you progress? By seeing better code written by others. A person never knows the value of ‘elegance’ or ‘good’ code unless they see it adding value to their work. Rather than read theory, I would encourage you to keep your eyes open to code written by others in your field of work. Keep your eyes open to concepts being discussed on stackoverflow (C++ tag). Spending just fifteen minutes a day of such searching can randomly expose you to concepts which can help you. It can show you code that is better written than your code. That’s when you follow up on Wikipedia and find out more about it. Such learning that comes out of curiosity, will be much longer lasting and useful to you than theory which you will forget when you wake up the next day.

Also consider trying out languages like MATLAB or Python.

As a physicist turned programmer myself, I found my physics background most helpful in forming the right metaphors for understanding software concepts. This perspective also made learning programming more fun for me and helped me develop the sense for “elegance” in software, which you seem to strive for.

I’ve described the important and under-appreciated role of metaphors and analogies in software in my CUJ column “Patterns of Thinking–names, metaphors, better programming, and the politics of the language”. For example, the OO concepts of class inheritance is often compared to passing traits from parents to offspring in a family. This is an incorrect analogy. The correct analogy for class inheritance is biological classification of organisms (e.g., a class RedRose is a kind of Flower, and a Flower is a kind of Plant).

Or take for example the software concept of a hierarchical state machine. A good metaphor here is the concept of a bound quantum system such as the hydrogen atom. As you recall, the states of an atom are numbered by three quantum numbers |n, l, m>, exactly because they are “nested” (hierarchical). This metaphor shows you how to understand that states nest within states (just like states of angular momentum (l) nest in the energy states (n)) and also you immediately see that state nesting is always reflection of some symmetry of the system.

Another interesting analogy from physics is the “actor model of computation”, which lately has been re-discovered due to multi-core CPUs and the distributed computing in the “cloud”. I found it helpful and fun to think of events exchanged by stateful actors (a.k.a. active objects) as virtual bosons, such as photons in QED, or gluons in QCD. This metaphor explains the fundamental asynchronous nature of communication, the run-to-completion event processing (quantum leap), and strict encapsulation of active objects, which can only interact with each other via the explicit intermediate artifacts.

Anyway, developing a system metaphor is a recommended practice in XP (eXtreme Programming), and as a physicist you will have an edge in coming up with good metaphors. You will also gain a sense for “elegance”, because your software will inherit the conceptual integrity from good metaphors you apply.

I can tell you that the biggest gains I have made in terms of how I approach solving problems have all been achieved through learning functional languages and parsers. Both discoveries were made by accident. So I’m telling you now if you are truly serious about becoming a better programmer then you need to learn about the various techniques involved in writing a compiler, e.g. parsers and parser generators, and you need to learn how to compose computations with higher order functions.

An excellent resource for the parser and compiler stuff is PL101: Create Your Own Programming Language. I still haven’t found a good intro to functional programming but I hear really good things about SICP.

A graduate in Computer Science doesn’t know how to code well when they graduate; they are not so much in demand when they leave University. Only if they get the experience.

The answer to your question is you need to learn Design Patterns. I programmed in Java, .NET and now I work as a PHP, Javascript and MySQL programmer. .NET by the way has a very large level of abstraction, e.g. ASP.NET. It means you can skip the abstraction learning. Languages like Perl, PHP, etc. have a low level of abstraction.

Read Head First Design Patterns, it is a good book. It is quite a comprehensive book. That is all what you will need.