Tag Archive: testing

Mar 26 2017

Book review: Working effectively with legacy code by Michael C. Feathers

legacy_codeWorking effectively with legacy code by Michael C. Feathers is one of the programmer’s classic texts. I’d seen it lying around the office at ScraperWiki but hadn’t picked it up since I didn’t think I was working with legacy code. I returned to read it having found it at the top of the list of recommended programming books from Stackoverflow at dev-books. Reading the description I learnt that it’s more a book about testing than about legacy code. Feathers defines legacy code simply as code without tests, he is of the Agile school of software development for whom tests are central.

With this in mind I thought it would be a useful read for me to improve my own code with the application of better tests and perhaps incidentally picking up some object-oriented style, in which I am currently lacking.

Following the theme of my previous blog post on women authors I note that there are two women authors in the 30 books on the dev-books list. It’s interesting that a number of books in the style of Working Effectively explicitly reference women as project managers, or testers in the text, i.e part of the team – I take this as a recognition that there exists a problem which needs to be addressed and this is pretty much the least you can do. However, beyond the family, friends and publishing team the acknowledgements mention one women in a lengthy list.

The book starts with a general overview of the techniques it will introduce, including the tools used to address them. These come down to testing frameworks and the refactoring tools found in many IDEs. The examples in the book are typically written in C++ or Java. I particularly liked the introduction of the ideas of the “seam”, a place where behaviour can be changed without editing the code and the “enabling point” – the place where a change can be made at that seam. A seam may be a class that can be replaced by another one, or a value altered. In desperate cases (in C) the preprocessor can be used to invoke test-time changes in the executed code.

There are then a set of chapters that answer questions that a legacy code-ridden developer might have such as:

  • I can’t get this class into a test harness
  • How do I know that I’m not breaking anything?
  • I need to make a change. What methods should I test?

This makes the book easy to navigate, if not a bit inelegant. It seems to me that the book addresses two problems in getting suitably sized pieces of code into a test harness. One of these is breaking the code into suitable sized pieces by, for example, extracting methods. The second is gaining independence of the pieces of code such that they can be tested without building a huge infrastructure up to support them.

Although I’ve not done any serious programming in Java or C++ I felt I generally understood the examples presented. My favoured language is Python, and the problems I tackle tend to be more amenable to a functional style of programming. Despite this I think many of the methods described are highly relevant – particularly those describing how to break down monster functions. The book is highly pragmatic, it accepts that the world is not full of applications in which beautiful structure diagrams are replicated by beautiful code.

There are differences between these compiled object-oriented languages and Python though. C#, Java, and C++ all have a collection of keywords (like public, private, protected, static and final) which control who can see what methods exist on a class and whether they can be over-ridden or replaced. These features present challenges for bringing legacy code under test. Python, on the other hand, has a “gentleman’s agreement” that method names starting with an underscore are private, but that’s it, and there are no mechanisms to prevent you using these “private” functions! Similarly, pretty much any method in Python can be over-ridden by monkey-patching. That’s to say if you don’t like a function in an imported library you can simply overwrite it with your own version after you’ve imported the library. This is not necessarily a good thing. A second difference is that Python comes with a unit testing framework and a mocking library rather than them being functionality which is third-party added. Although to be fair, the mocking library in Python was originally third party.

I’ve often felt I should programme in a more object-oriented style but this book has made me reconsider. It’s quite clear that spaghetti code can be written in an object oriented language as well as any other. And I suspect the data processing for which I am normally coding fits very well with a functional style of coding. The ideas of single responsibility functions, and testing still fit well with more functional programming styles.

Working effectively is readable and pragmatic. I suspect the developer’s dirty secret is that actually we wrote the legacy code that we’re now trying to fix.

Aug 20 2016

Book review: Test-Driven Development with Python by Harry J.W. Percival

test-drivenTest-Driven Development with Python by Harry J.W. Percival is a tutorial rather than a text book and it taught me as much about Django as testing. I should point out that I wilfully fail to do the “follow along with me” thing in tutorial style books.

Test-driven development (TDD) is a methodology that mandates writing tests first before actual code that does stuff. The first tests are for the desired behaviour that will be presented to the user.

I was introduced to software testing very early in my tenure ScraperWiki, now The Sensible Code Company. I was aware of its existence prior to this but didn’t really get the required impetuous to get me started, it didn’t help that I was mostly coding in Matlab which didn’t have a great deal of support for testing at the time. The required impetus at ScraperWiki was pair programming.

Python is different to Matlab, it has an entirely acceptable testing framework built-in. Test-driven Development made me look at this functionality again. So far I’ve been using the nose testing library but there is a note on its home page now saying it will not be developed further. It turns out Python’s unittest has been developing in Python 3 which reduces the need for 3rd party libraries to help in the testing process. Python now includes the Mock library which provides functions to act as “test doubles” prior to the implementation of the real thing. As an aside I learnt there is a whole set of such tests doubles including mocks, but also stubs, fakes and spies.

Test-driven Development is structured as a tutorial to build a simple list management web application which stores the lists of multiple users, and allows them to add new items to the lists. The workflow follows the TDD scheme: to write failing tests first which development then allows to pass. The first tests are functional tests  of the whole application made using the Selenium webdriver, which automates a web browser, and allows testing of dynamic, JavaScript pages as well as simple static pages. Beneath these functional tests lie unit tests which test isolated pieces of logic and integrated tests which test logic against data sources and other external systems. Integration tests  test against 3rd party services.

The solution is worked through using the Django web framework for Python. I’ve not used it before – I use the rather simpler Flask library. I can see that Django contains more functionality but it is at the cost of more complexity. In places it wasn’t clear whether the book was talking about general testing functionality or some feature of the Django testing functionality. Django includes a range of fancy features alien to the seasoned Flask user. These include its own ORM, user administration systems, and classes to represent web forms.

Test-driven Development has good coverage in terms of the end goal of producing a web application. So not only do we learn about testing elements of the Python web application but also something of testing in JavaScript. (This seems to involve a preferred testing framework for every library). It goes on to talk about some elements of devops, configuring servers using the Fabric library, and also continuous integration testing using Jenkins. These are all described in sufficient detail that I feel I could setup the minimal system to try them out.

Devops still seems to be something of a dark art with a provision of libraries and systems (Chef, Puppet, Ansible, Juju, Salt, etc etc) with no clear, stable frontrunner.

An appendix introduces "behaviour-driven development” which sees sees a test framework which allows the tests to be presented in terms of a domain specific language with (manual) links to the functional tests beneath.

In terms of what I will do differently having read this book. I’m keen to try out some JavaScript testing since my normal development activities involve data analysis and processing using Python but increasingly blingy web interfaces for visualisation and presentation. At the moment these frontends are slightly scary systems which I fear to adjust since they are without tests.

With the proviso above, that I don’t actually follow along, I like the tutorial style. Documentation has its place but ultimately it is good to be guided in what you should do rather than all the things you could possibly do. Test-driven Development introduces the tools and vocabulary you need to work in a test-driven style with the thread of the list management web application tying everything together. Whether it instils in me the strict discipline of always writing tests first remains to be seen.

Oct 31 2015

Book review: Effective Computation in Physics by Anthony Scopatz & Kathryn D. Huff

ecipThis next review, of “Effective Computation in Physics” by Anthony Scopatz & Kathryn D. Huff, arose after a brief discussion on twitter with Mike Croucher after my review of “High Performance Python” by Ian Micha Gorelick and Ian Ozsvald. This in the context of introducing students, primarily in the sciences, to programming and software development.

I use the term “software development” deliberately. Scientists have been taught programming (badly, in my view*) for many years. Typically they are given a short course in the first year of their undergraduate training, where they are taught the crude mechanics of a programming language (typically FORTRAN, C, Matlab or Python). They are then left to it, perhaps taking up projects requiring significant coding as final year projects or in PhDs. The thing they have lacked is the wider skillset around programming – what you might call “software development”. The value of this is two-fold – firstly, it is a good training for a scientist to have for careers in science. Secondly, the wider software industry is full of scientists, providing students with a good grounding in this field is no bad thing for their future employability.

The book covers in at least outline all the things a scientist or engineer needs to know about software development. It is inspired by the Software Carpentry and The Hacker Within programmes.

The restriction to physics in the title seems needless to me. The material presented is mostly applicable to any science, and those working in the digital humanities, undertaking programming work. The examples have a physics basis but not to any great depth, and the decorative historical anecdotes are all physics based. Perhaps the only exception to this is the chapter on HDF5 which is a specialised data storage system, some coverage of SQL databases would make a reasonable substitute for a more general course. The chapter on parallel computing could likewise be dropped for a wider audience.

The book is divided into four broad sections. Including in these are chapters on:

  • Command line operations;
  • Programming in Python;
  • Build systems, version control, debugging and testing;
  • Documentation, publication, collaboration and licensing;

Command line operations are covered in two chunks, firstly in the basic navigation of the file system and files followed by a second chapter on “Regular Expressions” which covers find, grep, sed and awk – at a very basic level.

The introduction to Python is similarly staged with initial chapters covering the fundamentals of the core language, with sufficient detail and explanation that I learnt some new things**. Further chapters introduce core Python libraries for data analysis including NumPy, Pandas and matplotlib.

Beyond these core chapters on Python those on version control, debugging and testing are a welcome addition. Our dearest wish at ScraperWiki, a small software company where I worked until recently, was that new recruits and interns would come with at least some knowledge and habit for using source control (preferably Git). It is also nice to see some wider discussion of GitHub and the culture of Pull Requests and issue tracking. Systematic testing is also a useful skill to have, in fact my experience has been that formal testing is most useful for those most physics-like functions.

The final section covers documentation, publication and licensing. I found the short chapter on licensing rather useful, I’ve been working on some code to analyse LIDAR data and have made it public on GitHub, which helpfully asks which license I would like to use. As it turns out I chose the MIT license and this seems to be the correct one for the application. On publication the authors are Latex evangelists but students can chose to ignore their monomania on this point. Latex has a cult-like following in physics which I’ve never understood. I have written papers in Latex but much prefer Microsoft Word for creating documents, although Google Docs is nice for collaborative work. The view that a source control repository issue tracker might work for collaboration beyond coding is optimistic unless academics have changed radically in the last few years.

I’d say the only thing lacking was any mention of pair programming, although to be fair that is more a teaching method than course material. I found I learnt most when I had a goal of my own to work towards, and I had the opportunity to pair with people with more knowledge than I had. Actually, pairing with someone equally clueless in a particular technology can work pretty well.

There is a degree to which the book, particularly in this section strays into a fantasy of how the authors wish computational physics was undertaken, rather than describing how it is actually undertaken.

To me this is the ideal “Software development for scientists” undergraduate text, it is opinionated in places and I occasionally I found the style grating but nevertheless it covers the right bases.

*I’m happy to say this since I taught programming badly to physics undergraduates some years ago!

**People who know my Python skills will realise this is not an earthshattering claim.

Jun 28 2013

Testing, testing…


This post was first published at ScraperWiki.

Data science is a distinct profession from software engineering. Data scientists may write a lot of computer code but the aim of their code is to answer questions about data. Sometimes they might want to expose the analysis software they have written to others in order they can answer questions for themselves, and this is where the pain starts. This is because writing code that only you will use and writing code someone else will use can be quite different.

ScraperWiki is a mixed environment, it contains people with a background in software engineering and those with a background in data analysis, like myself. Left to my own devices I will write code that simply does the analysis required. What it lacks is engineering. This might show up in its responses to the unexpected, its interactions with the user, its logical structure, or its reliability.

These shortcomings are addressed by good software engineering, an area of which I have theoretical knowledge but only sporadic implementation!

I was introduced to practical testing through pair programming: there were already tests in place for the code we were working on and we just ran them after each moderate chunk of code change. It was really easy. I was so excited by it that in the next session of pair programming, with someone else, it was me that suggested we added some tests!

My programming at ScraperWiki is typically in Python, for which there a number of useful testing tools. I typically work from Windows, using the Spyder IDE and I have a bash terminal window open to commit code to either BitBucket or Github. This second terminal turns out to be very handy for running tests.

Python has an internal testing mechanism called doctest which allows you to write tests into the top of a function in what looks like a comment. Typically these comprise a call to the function from a command prompt followed by the expected response. These tests are executed by running a command like:

 python -m doctest yourfile.py

This is OK, and it’s “batteries included” but I find the mechanism a bit ugly. When you’re doing anything more complicated than testing inputs and outputs for individual functions, you want to use a more flexible mechanism like nose tools, with specloud to beautify the test output. The Git-Bash terminal on Windows needs a little shim in the form of ansicon to take full advantage of specloud’s features. Once you’re suitably tooled up, passed tests are marked with a vibrant, satisfying green and the failed tests by a dismal, uncomfortable red.

My latest project, a module which automatically extracts tables from PDF files, has testing. It divides into two categories: testing the overall functionality – handy as I fiddle with structure – and tests for mathematically or logically complex functions. In this second area I’ve started writing the tests before the functions, this is because often this type of function has a simple enough description and test case but implementation is a bit tricky. You can see the tests I have written for one of these functions here.

Testing isn’t as disruptive to my workflow as I thought it would be. Typically I would be repeatedly running my code as I explored my analysis making changes to a core pilot script. Using testing I can use multiple pilot scripts each testing different parts of my code; I’m testing more of my code more often and I can undertake moderate changes to my code, safe in the knowledge that my tests will limit the chances of unintended consequences.