A Whole New Project: A JVM

Ever since I started out in programming, I've wanted to undertake a programming project that was developed with the rigorous approach used in mission-critical software: write out the requirements; enforce traceability between requirements between requirements, code, and tests; and, of course, do rigorous testing. 

The main problem has been finding the time to dedicate to such a project. There is a reason that the agile movement eschews this approach: it is the opposite of agility--it relies on an unchanging product definition, relies on extensive documentation, and does not accept the concepts of failing fast and releasing often. It's a whole different mindset to "fail never and release when ready."

In the light of these constraints, the ideal project is one with a well-defined set of specifications. I've decided to meet that need by writing a simplified version one of my favorite pieces of software: the Java Virtual Machine (JVM). 

The specs for much of the JVM are published in detail and updated by the Java team at Oracle with every new release. You can find them here. On the basis of these docs alone, the JVM is the best documented virtual machine in commercial use. There are many additional resources available, such as the excellent articles by Ben Evans and Aleksey Shipilev (both of Red Hat) on how the innards of the JVM work. And, I should add the source code to the JVM is publicly available. 

My project is entitled Jacobin and can be accessed at jacobin.org, which for the time being (and possibly permanently) points to the Jacobin project page on GitHub. There you'll find a detailed write-up of the project status.

Choosing a Language

I have spent the last eight months researching the JVM--reading the docs and articles and doing exploratory coding in various languages with which to write the Jacobin JVM. My requirements for the implementation language are simple enough: it must have decent tools and a viable ecosystem, it must compile to native code on the three major platforms (Windows, Mac, and Linux), and it must have built-in garbage collection (GC). The latter requirement is important. The JVM performs garbage collection, but I don't want to write a garbage collector. They are exceedingly difficult tools to write and, especially, to debug. By using a language that does its own GC, a huge amount of work has been removed from the project.

Three languages meet my requirements: Dart, Swift, and Go. I've written several thousand lines of code in the first two and have eliminated them from consideration. Here is why. Dart is a lovely language, but it's slow (even when compiled to binaries), its ecosystem is wanting, and the kind of threading it does is a poor match to the JVM. The problem with the ecosystem is exemplified by the nearly complete absence books on the language since Dart 2.0 came out a few years ago. Almost all written tutorials are way out of date. Those that are current focus, without exception, on Flutter--the UI toolkit that dominates the use cases for Dart. As a result, it's not easy to learn Dart in depth unless you want to focus primarily on Flutter. The Dart team should really address this. As to the threading model, it is based entirely on single-channel message passing: there is no shared memory. The JVM must perforce share memory between threads and so even if Dart were faster and the docs were up-to-date, it would not meet my needs.

Swift is a truly beautiful language. It's rich in features and has a lot of the type-checking and code safety rules of Rust, but without the endless head-banging that Rust entails. I would have loved to write the JVM in Swift, but it has several drawbacks: it doesn't run on Windows and its libraries are intimately tied to the Mac. Let me clarify. There is an official version of Swift for Windows, but it's maintained entirely by a single engineer at Google. There are effectively no docs for this version and the installation instructions don't work no matter how much tweaking and configuration I have done. The second problem is that while Swift is trying to become a language that works beyond just Apple platforms (for example, it runs fine on Linux), this worthy goal is far from especially when it comes to libraries. Consider that the equivalent of libzip (which is a core library in most languges--it is used to compress/decompress data using the zip format) is maintained by a third party on Github on a project that has at present 22 stars. The collections library has at most a handful of basic data structures, etc. Unless I want to write many of these libraries myself--which I have no desire to do--I am forced down the same road as Node developers: grabbing bits of functionality here and there from different contributors, many of which have unknown code quality. The alternative is to use Apple's Cocoa frameworks on the Mac, which would make my project Mac-only. In sum, until Swift grows its non-Mac ecosystem, it's not a viable option for this project--much to my chagrin.

This leaves Go, which is an easy-to-learn language that runs well on the major platforms and has a flourishing set of libraries, many of which are maintained by core Go developers. While it checks all the boxes, it presents its own challenges. For example, it's the only one of the languages that is not object-oriented and the transition from thinking in objects (after all, Java is my home language, so to speak) to using an imperative style of coding requires some rewiring of how I approach problems. In addition, the standard Go tools have weaknesses. For example, the testing framework is minimal--there is nothing like JUnit in terms of range of features. In the language itself, return values for errors and the lack of generics both feel a little crude, especially to someone coming from Java. Nonetheless, it looks like the best option for my project.

There was one other language candidate: Java. That is, write a JVM that runs on the JVM. I don't find this interesting at all. The code for the JVM is currently mostly written in Java and I'll be consulting it frequently--so what would I do then? Cut and paste? Rewrite the code in my preferred style? It's hard to see how that's an advantage.

What's Next?

In the next few months, I'll continue writing requirements and traceability docs and work through various Go books to transition from beginner Gopher to advanced, so that coding can proceed apace, rather than through constant searches. By that time, I should be in good position to rewrite the 2500 lines of Java-bytecode parsing routines I wrote in Swift, finish that parser, and then begin working on building the execution environment. 

In my next blog post, I'll write about the benefits of such a project and how personal projects like this deliver unexpected rewards.

In the meantime, if you want to show your interest or support, follow the project on GitHub or give it a star, so that I know I'm not working alone in a dark alley.

The Most Important Book of The Year

Continuous Delivery

by Jez Humble and David Farley

I have reviewed many books on this website and I have gone through numerous others as part of my work on the Jolt Awards, but it’s been a very long time since I’ve read a book as useful and likely game-changing as Continuous Delivery.

The basic premise of the book is that we need to move past continuous integration into a fuller cycle of activities that go beyond build and test. Specifically, this new orientation calls for building and testing on all platforms, creating and deploying the final deliverables for all platforms—with every check-in. The benefit of this approach is that the development organization at any given moment always has: 1) immediate feedback on deployment issues, 2) a deployable binary; 3) a completely automated process to build, test, and deploy on all platforms.

This simple concept—a kind of continuous integration on mega steroids—has profound repercussions, all of which make your process better. The first and most important is that you have to automate everything downstream from the coding. And the authors mean everything. The most common point where people hem and haw about automation is deployment. But Humble and Farley make it clear you have to “bring that pain forward,” and fix the process so it can be automated. (If you don’t have any idea how you might refine and automate deployment, think virtualization. Can you emulate your current systems on virtual machines and then progressively simplify deployment of the software to the point of automation? Good, you’re on your way.)

But the mechanics of deployment may be the least of your challenges (And here, the book’s name could be viewed as misleading: Deployment is only one aspect it covers.) You also have to build, run, and test the software on every platform you ship on. You’re not reasonably going to be able to do that if you have to change configurations and manually reset values for different platforms. The authors guide you to finding the one path that gets you across the river Jordan without spending 40 years in the desert of bit twiddling. The key is to use a single codebase and move the platform dependent stuff into configuration files. This is non-trivial, but the authors offer plenty of good advice.

Testing is another topic Humble and Farley explore in great depth. Testing in the context of continuous delivery is not just running unit tests and a regression suite. No , this is running all tests—unit, integration, UAT, and so on. How to automate them effectively occupies probably the largest chunk of the book. Even if you don’t accept the continuous delivery concept, this section is worth the price of admission. It’s mind-expanding, in ways that the hundreds of articles we’ve all read about agile testing on Digg and Reddit never touch on. You see very quickly how much more automation you could do and how to get from your miserable semi-manual existence to the smooth flow of full and continuous automation.

What impresses about the book is how the authors consistently work through hard problems. They are not daunted by them and there is no attempt to pas over them with hand waving. Hard things are examined in detail with a perspective that derives from the authors’ own extensive experience.

I have literally never read a better book on process. I believe that going forward, this book will redefine agile process and CI; and it will have as much influence as--I have to go back to 1999, here--Fowler’s book on Refactoring did on code.

Bluebeam's PDF Creation Tool Suite

I use a variety of PDF tools in my editorial work. I frequently create, mark up, manipulate, and combine PDFs. In addition, I contribute to the open source Platpus typesetting project, whose major output format is PDFs. And the PDF plugin is my specific bailiwick. So, over the years, I've come to know a thing or two about PDFs, as well as the limitations of PDF tools.

The standard for PDF tools has been Adobe's Acrobat suite. But this suite is expensive, somewhat quirky, and at times works poorly with other tools. Acrobat plugins to Microsoft Office and Internet Explorer are especially unreliable, and they frequently make their host programs behave erratically. I always uninstall them.

This means I need to use other options to convert Word documents to PDF. There are several common solutions out there, none but one of them is completely satisfactory. For example, the Microsoft Office PDF plugin does not embed all fonts, nor does it give you the option to do so. It does not embed the Base14 fonts.

This is a design error (that is common). Here is its history. For many years, Adobe guaranteed that Adobe Acrobat Reader would provide 14 fonts (the so-called Base14 fonts) in all implementations. These fonts were Times Roman, Courier, and Helvetica typefaces (each in regular, bold, italic, and bold italic—so 12 fonts) plus a Symbol and a Dingbat font. The rule was you did not need to embed these fonts in PDF documents, because Acrobat Reader would supply them. This scheme never worked very well. Its first limitation was that not all Times Roman fonts looked the same, so the same document could look strikingly different on two different computers. A few years ago, Adobe quietly discontinued supporting Base14 fonts in Acrobat Reader. The result is that if you're creating a PDF for distribution, you must embed all fonts, even the old Base14 fonts, if you want it to maintain your original format and layout.

The Microsoft Office plugin does not have this option, so as a result PDFs you generate with it are not guaranteed to look correct on other systems. And, in fact, they frequently do not.

The PDF generator that come with Adobe Acrobat (not the Reader, but the paid tools) works better. It does offer an option to embed all fonts. However, in Word documents with many links, it fails to identify all links. And so rather than be clickable, the links show up as pure text.

To remedy this, I tested various Word-to-PDF tools and found none that consistently met all requirements until I ran into Bluebeam PDF Revu, a tool I had not previously heard of.

The first thing I noticed was that Bluebeam's plugins were stable and they worked correctly. The second thing I discovered was that Revu found all links in documents and by default, it embedded all fonts. So far, so good. The attention to small details in its PDFs are part of Bluebeam's DNA—it was designed as a tool for CAD users, so correctly rendering every detail of a document is a specialty.

Like the Adobe Acrobat toolbox, Revu provides editing capabilities, with better text mark-up tools than Acrobat. It also enables you to construct your own menu of tools for faster access to frequently performed operations. Form handling, digital signatures, etc. work exactly as expected. Multi-document processing can also be automated with the product. Adobe Acrobat Pro—the comparable offering from Adobe—retails at $449 list, and $350 at Amazon. The academic version of Acrobat can be found for the same price as the full Bluebeam Revu ($149) product. So, if you want the full range of options, better implemented than in Adobe's offering, and at a lower price, have a look at Bluebeam PDF Revu. (They offer a 30-day free trial.)

Keeping LOC and Tests in Balance

The proliferation of metrics in software development threatens to take important quantitative measures and bury them beneath an avalanche of noisy numbers. Consequently, it's important to look for certain ratios and trends among the numbers to inform you whether a project is healthy. One tell-tale relation links LOCs and number of tests. These two values should grow in direct proportion to each other.

The included diagram presents the ratio of these two values for Platypus, the OSS project I work on.

As you can see, except for a few dips here and there, these numbers have stayed in lock step for the last 18 months. And, as you might expect, code coverage from these tests has similarly remained in fairly narrow range--right around 60%.

The most typical violation of this ratio is, as you would guess, a jump in LOCs without a corresponding rise in tests. This is something managers should watch out for. With a good dashboard, they can tell early on when these trend lines diverge. This is frequently, but not always, always indicative of a problem. (For example, it could be that a lot of code without tests was imported to the project.) Whatever the cause is, managers need to find out and respond accordingly.

(For the record, the tests counted in this diagram include unit tests and functional tests.)

The Limitations of TDD

During the last 12-18 months, TDD has broken into the mainstream, it seems. And now, we're starting to see some backlash, as its limitations become better understood. Here is a sample discussion from Artima.com. Cédric Beust, who wrote the commentary, is not some unknown guy with a weird name. He wrote the TestNG unit testing framework, which is second only to JUnit in popularity. He also wrote the book, Next Generation Java Testing, which is probably the best book on pragmatic software testing that I've read in a long time. Here goes...

> That's an interesting point. Are you, in effect, saying
> that unit testing is overly emphasized, and at the expense
> of other forms of testing?

This has also been my experience, although to be honest, I see this problem more in agile/XP literature than in the real world.

This is the reason why I claim that:

- TDD encourages micro-design over macro-design
- TDD generates code churn

If you obsessively do TDD, you write tests for code that you are pretty much guaranteed to throw away. And when you do that, you will have to refactor your tests or rewrite them completely. Whether this refactoring can be done automatically or not is beside the point: you are in effect creating more work for yourself.

When I start solving a problem, I like to iterate two or three times on my code before I'm comfortable enough to write a test.

Another important point is that unit tests are a convenience for *you*, the developer, while functional tests are important for your *users*. When I have limited time, I always give priority to writing functional tests. Your duty is to your users, not to your test coverage tools.

You also bring up another interesting point: overtesting can lead to paralysis. I can imagine reaching a point where you don't want to modify your code because you will have too many tests to update (especially in dynamically typed languages, where you can't use tools that will automate this refactoring for you). The lesson here is to do your best so that your tests don't overlap.

--Cedric Beust

My Interview with Alexander Stepanov and Paul McJones

InformIT.com has posted my interview with Alexander Stepanov (of STL fame) and his co-author Paul McJones. Their just-released book, Elements of Programming, tries to map algorithm implementations back to symbolic logic and algebraic theorems, thereby--in theory--improving their design and correctness.

In the discussion, we broach many topics that derive from this approach to programming.

Groovy Books

I have been using Groovy to write functional tests for Platypus, the open-source typesetting project I work on. I am likely to make Groovy the default scripting language for Platypus in the next milestone. In the process, I've had to come up to speed on Groovy and I've been reading through and looking over the various Groovy titles on the market. Here's my take.

The Groovy bible today, without the slightest doubt, is Groovy in Action which at 650+ pages is also the most detailed book. Its principal limitation is that Groovy has undergone several revisions since it came out. Because of this, a second edition is being written. Early access to e-drafts of that edition are available here, although little as yet has been published.

If you'd like a shorter and more up-to-date introduction to Groovy, I recommend Programming Groovyby Venkat Subramaniam. At less than 300 pages, it's a quick read, provides all the needed info quickly, and covers all the highlights, with a good balance of detail.

Many people consider Grails to be the killer app for Groovy. It's a web framework that rides above Spring and Hibernate and removes much of the complexity of using those components. If you are learning Groovy to use Grails, then Beginning Groovy and Grailsis an excellent choice. It's clear, approachable, and teaches you enough Groovy to be able to follow the tutorial on Grails.

Once you get comfortable with basic Groovy, you'll quickly find yourself pining for a book of recipes that shows you how to quickly get basic tasks done using Groovy metaphors. There are two somewhat flawed recipe books on the market. The first is Groovy Recipesfrom Scott Davis, a well-regarded lecturer in the Groovy area. While calling itself a recipe book, it frequently diverges into tutorials and odd humor--both of which are obstacles when trying to find information. Some important topics are not covered at all, such as testing--which is one of the major areas where Groovy benefits Java. Database access is also not covered. In other areas, Davis' explanations seem to lack an understanding of what the user would be looking for. Nonetheless, I have successfully used some of Davis' recipes in my work. A good alternative is Groovy and Grails Recipesfrom Bashar Abdul-Jawad. This title is a true recipe book and very readable. The Groovy portion is too short, however, and an important section on file recipes (which does appear in the Davis book) is omitted. However, if you're learning Groovy to get to Grails, this is the best choice. And Abdul-Jawad does a good job understanding what readers are looking for.

Ideally, O'Reilly would publish one of its trademark comprehensive recipes book and we could all settle on that. However, when I contacted O'Reilly about upcoming Groovy titles, the company indicated it had none in the immediate pipeline.

That's pretty much it for Groovy books; although there are several others that focus exclusively on Grails. One publisher, Apress, seems to dominate that Grails market. The two titles above that cover Grails are from Apress as is the Definitive Guide to Grails, written by Graeme Rocher, who designed Grails. In the past I've been skeptical of Apress books due to wide variations in their quality, but the Groovy/Grails titles I've examined have been consistently of high quality.

As Groovy gains a wider audience, I expect more titles to emerge from all the technical book publishers.

The Fan programming language: compile to Java and .NET

I have recently been playing with Fan, a programming language that reminds me a lot of Groovy, but has additional capabilities, such as actors. Its binaries run either on the JVM or .NET. Below is my recent column in SDTimes about the language. 

In recent times, we are seeing an extraordinary proliferation of new languages. On one hand, thousands of domain-specific languages (DSLs) have been spawned by the advent of tools that facilitate their creation. On the other hand, we find an equal surge in full-scale, general-purpose programming languages.

 The renaissance of these larger programming languages derives from several advances: 1) a renewed interest in dynamic languages and their benefits; 2) hardware that’s fast enough to run dynamic languages rapidly; and 3) the existence of two run-time environments—the JVM and the .NET CLR—that are widely used, well understood, and fast. As a result, we have an embarrassment of language choices that was inconceivable a decade ago.

In this column, I have previously highlight various interesting options among these languages: Ruby, Groovy, D, NetRexx, and a few others that elegantly address specific problems. Recently, I have been spending time with the Fan programming language, which while still early in its development cycle, is more finished and mature than most new languages at this point in their development.

Fan is a dynamic, OO language that runs on the JVM and the .NET CLR. It does this by generating intermediate code (called fcode) that is dynamically translated into Java bytecodes or a .NET DLL at startup. This step introduces a slight pause, after which programs run at full “native” speed for the given environment.

New languages arise because a developer needed to solve a problem that was not addressed well by common alternatives. The developers of Fan, a pair of brothers—Brian and Andy Frank—worked on embedded Java applications and found it difficult to sell the accompanying software to customers who were committed to Windows Mobile and .NET. So, they decided to write Fan to solve the problem and to keep it small enough that it could fit easily in a mobile device. 

In the process, they removed language verbosity and added features they wanted. Their vision is remarkably balanced and complete. The language, on the verge of a freezing its 1.0 features, offers: dynamic typing and/or strong typing (à la Groovy), closures and first-class functions, extensive concurrency support (thread-safe classes with immutability specified, threads with built-in message passing, and actors), and elegant handling of various namespace issues. Low-level features include default method parameters, nullable data types, built-in field accessors, unchecked-only exceptions, and simplified numerics. The numerics handle the overflow problem that is the favorite of language puzzle writers: all integers are longs and all floats are doubles. So either type uses 64-bits and effectively does not overflow. Chars are 16-bit UTF entities.

A particularly interesting aspect of Fan is the libraries. As Brian Frank told me, “Solving the JVM/CLR portability was the easy part. The hard part was what to do with the libraries and APIs.” What the brothers did was to rethink the API sets, eliminate cruft, and use a different concept of grouping. Whereas .NET and Java both use a large number of packages that include moderate numbers of classes, Fan uses few packages that contains large numbers of classes. The result is that a developer can almost always can guess correctly which package to link to for a specific need. In addition, Fan has sensible, built-in library defaults. For example, all files I/O defaults to buffered.

The good design of a language can take it only so far. To succeed, it needs good tools, good docs, and an active community. The language tools (compiler, etc.) are all open source and written in Fan. The code is clean and surprisingly readable. As to IDE support, there is currently a plugin for JetBrains IDEA and one in the very early stages for Eclipse . The Frank brothers do all their coding in regular text editors.

The documents are very good. Probably, the best I’ve seen for any new language at this point and far better than much older “new” languages, such as D. The website is well organized and elegant; and the tutorials and “cookbook” entries clean and plentiful. It’s difficult to assess language community size in general, but more so with Fan because it does not figure on Tiobe, due I suspect to the difficulty of teasing out data for a language named Fan. For this reason and for richer Google search results, there is a move afoot to change the name of the language. Nonetheless, the community is definitely small and active. The latter aspect due to the responsiveness of the Frank brothers to users’ questions, requests, and defect reports. 

Fan solves a lot of problems elegantly. If it continues growing as it has during the past year, I anticipate it will evolve into an attractive solution for some development organizations.

The biggest challenge right now is the early stage in which most IDE plugins are currently found. A second limitation, which is about to be fixed in the upcoming point release, is that libraries and binary modules are all placed by default in the same directory. The discussion on this point, found on the language's discussion boards, shows the attentive regard of the Frank brothers for their users as they kicked around various schemes, elicited comments, and posted thoughtful replies. It's one of the most spam-free, low-noise discussion groups I've been a part of in a long while. I expect good things from this language.

The Agile Rules in HP's Original Garage

According to a recent HP poster, these were the rules in Bill Hewlett and Dave Packard's famous garage:

Believe you can change the world.

Work quickly, keep the tools unlocked, work whenever.

Know when to work alone and when to work together.

Share tools, ideas. Trust your colleagues.

No Politics. No bureaucracy. (These are ridiculous in a garage).

The customer defines a job well done.

Radical ideas are not bad ideas.

Invent different ways of working.

Make a contribution every day. If it doesn’t contribute, it doesn’t leave the garage.

Believe that together we can do anything.

Invent.

Curiously, it sounds like something the agile guys might have written (had they not written the manifesto). I prefer this wording because of its greater applicability and more dynamic presentation.

Bob Martin's "Clean Code" Reviewed

I have gone through "Uncle Bob" Martin's new book, Clean Code,which is a lenthy presentation of rules that will help Java developers write better code. It's similar to Kent Beck's Implementation Patterns,except more code-fixated. Clean Code has some good points, but it contains several weaknesses that seem to have gone entirely by the reviewers on Amazon. So, here's the scoop.

First of all, it's well hidden, but the book is only partially written by Bob Martin. Many chapters are written by other consultants who work at Martin's company--many of whom I've never heard of. The one stand-out exception is Michael Feathers, whose chapter on error handling is one of the clearest in the book. I wish he had written more.

The main body consists primarily of explaining various coding rules that Martin calls heuristics and to which he assigns coded abbreviations for later reference. Alas, unlike patterns that have meaningful names as shortcuts, Martin chooses meaningless notations such as C2 and G26. So, "the function should do nothing but compacting[G30]" is a shortcut for the author, but a pain for the reader who has to cross-reference these references repeatedly to know what Martin is talking about.

Unlike Beck's book, there is no theoretical framework to Martin's prescriptions. The book is a series of examples from which he teases this rule and that. Because of this lack of framework there is a certain desultory aspect--the rules come in seemingly random order.

Some of them make you want to leap up and clap. For example, his rule that Javadoc should not contain HTML. How many times I've come to the same conclusion! I want to read comments in code easily. The small lift that HTML brings to Javadoc pages is not in anyway worth the difficulty it adds to the reading of comments in code. Bob Martin's one of the first persons I've encountered to say so unequivocally.

Other rules are good, but later contradicted. For example, Martin states that you should never leave commented-out code in place. [C5] As he points out, no one knows why it's commented out and so it remains in place forever. However, later on in an example of refactoring code per his own rules, Martin comments out large blocks of code without an explanation of how that squares with his earlier advice. (p.374)

Martin also uses questionable coding preferences. For example, all of his code uses indents of 2 columns. 2 columns? It makes every routine look like a solid chunk of code. It's clearly not a practice to be recommended.

A large portion of the book is an example of Martin refactoring someone else's code. He takes a long piece from an OSS project and proceeds to "improve" it. I found this section uncompelling. Perhaps because in Fowler's masterpiece Refactoring,each refactoring magically transforms the code. By comparison, Bob Martin's work seems journeyman-like. I didn't find the initial code interesting nor did I find Martin's cleaned-up version luminous. I was expecting a before-and-after scenario that would make me sit up and take notice. Instead, the exercise felt preachy, condescending at times, and ultimately not terribly convincing.

My last gripe addresses an inexcusable error: typos. There aren't many but they are frequent enough to be distracting. For example, Martin seemingly does not understand the difference between it's and its. (p. 272, p. 296, among others) And his code contains typos too. (p. 309). This carelessness erodes credibility. Books that preach quality should be flawless at the level of spelling and grammar.

Overall, I think some organizations can use several of Martin's heuristics as a means of boosting their in-house coding standards. But I doubt that careful coders will find much of value. Those developers will be better served by Beck's Implementation Patterns,which is based on principles and so communicates much more information in fewer words. Since my review of Beck's book, I must confess my admiration for it has deepened, and it's the volume I would recommend if you're looking to write cleaner code.

Banishing Return Status Codes

The most enduringly popular post on this blog is Perfecting OO's Small Classes and Short Methods, which presents a short series of stringent guidelines to help an imperative-trained developer master OO.

If I were to add one item to the list, it would be: Don't use return codes to indicate the status of an action. Developers trained in languages such as C have the habit of using return codes to indicate the success or the nature of failure of the work done by a function. This approach is used because of the lack of a structured exception mechanism. But when exceptions are part of the language, the use of status codes isa poor choice. Among the key reasons are: many status codes are easily ignored; developers will expect problems to be reported via the exception mechanism; exceptions are much more descriptive. And finally, exceptions enable return codes to be used for something useful--namely returning a data item.

Astute readers will note that in Java, null is frequently used as a return value to indicate a problem (as in Collections). This practice subverts the previous points, and it too should be avoided. Returning a null presents code with many problems it should not have to face. The first is the risk of a null-pointer blow-up because the return value was accessed without being checked. This leads to the code bloat of endless null value checks. A much better solution, which avoids this problem, is to return an empty item (empty string, empty collection, etc.). This too communicates that no data item fulfilled the function's mandate, but it does not risk the null-pointer problem, and it frequently requires no special code to handle the error condition.

Hence, if your OO code is characterized by heavy reliance on return codes (many of which I am certain are not checked), consider rewriting it in favor of exceptions and use return statements solely for returning non-null data items.

A Parameter-Validation Smell and a Solution

Last week, Jeff Fredrick and I did a day-long code review of Platypus. We used a pair-programming approach, with Jeff driving and I helping with the navigation. Eventually, we got into the input parser, which parses input lines into a series of tokens: text, commmands, macros, and comments. Macros can require a second parsing pass, and commands often require additional parsing of parameters.

Once you get a parser working well (that is, it passes unit and functional tests, and it handles errors robustly), you generally don't want to mess with refactoring it. Experience tells you that parsers have hideous code in them and wisdom tells you to leave it alone. However, we launched in.

A frequent cause of otiose code was my extensive parameter checking. Parameters were validated at every step as tokens passed through multiple levels of parsing logic. Likewise, the movement of the parse point was updated multiple tiems as the logic resolved itself back up the processing stack. This too had to be validated repeatedly.

Jeff came up with an elegant refactoring that I could not find in the usual sources. He created an inner class consisting of the passed variables, a few methods for validating them, and a few more methods for manipulating them.

This class was then passed to the methods in lieu of the individual parameters--thereby reducing the number of parameters to one or two. And because the class constructor verified the initialization of the fields, I need only to check whether the passed class was null, rather than validate each of the internal fields.

The effect was to reduce complexity of already complex code, enforce DRY, and place the validation of the variables inside a class that contained them--a set of small, but important improvements. And like many of the best refactorings, it seems obvious in retrospect.

So, if you find your class's methods are repeatedly validating the same parameters, try bundling them in an inner class along with their validation logic. You'll like the results.

The Handiest Java Book in Years.

One of the constant challenges I have as a Java developer is keeping up with the numerous good FOSS dev tools. I no sooner start testing one tool and adapting my project to it, when a new one comes along. Being an analyst and naturally curious, this new product (or new release) represents a constant temptation. Is it better than what I am using? How much effort is required to try it out? What does it do better? On and on.

I can put a lot of those concerns to rest now. I just received a copy of Java Power Tools from O'Reilly and it's exactly what I've been looking for. It contains deep explanations of the principal FOSS dev tools in 10 major categories. These explanations are not two- or four-page summaries, but in-depth expositions that provide crucial info on the strengths and weaknesses of the product. The author, John Smart, then provides detailed tutorial on using the product. It's clear he's spent lots of time exploring the dark corners of each tool. And he makes good use of that knowledge in his comparisons and comments on the products.

If you want to spend an hour or so coming up to speed on what a product is about before installing it (and without having to work through the usually limited docs), this book will get you there faster and enable you get an overview of a whole lot of tools quickly and with the assurance you have a clear understanding. Here are the tools that are covered, followed by the number of pages for each one in parentheses:

BUILD TOOLS: Ant (55), Maven (60)
SCM: CVS (20), Subversion (78)
CI: Continuum (24p) Cruise Control (19) LuntBuild (32) Hudson (19)
IM: Openfire (12)
UNIT TESTING: JUnit (20) TestNG (25) Cobertura (17)
OTHER TESTING: StrutsTestCase (10) DbUnit (44p) JUnitPerf (10) JMeter (20) SoapUI (22) Selenium (30( Fest (9)
PROFILING: with Sun tools (16) with Eclipse (15)
DEFECT MANAGEMENT: Bugzilla (20) Trac (35)
QUALITY: Checkstyle (20) PMD (18p) FindBugs (12) Jupiter (18) Mylyn (14p)

All told, 856 pages of crisp, well-written explanations. A must-have reference for the bookshelf.

Is the popularity of unit tests waning?

Before getting into my concerns about whether unit testing's popularity has peaked, let me state that I think unit testing is the most important benefit wrought by the agile revolution. I agree that you can write perfectly good programs without unit tests (we did put man on the moon in 1969, after all), but for most programs of any size, you're likely to be far better off using unit tests than not.

The problem is that only a small subset of developers understand that. And recent data points suggests that the number of programmers who use unit tests is not exactly growing quickly. I'll list some of the data points below that I've been developing for my column in SD Times.

1) Commercial products on the wane. Agitar was a company whose entire fate was tied to the popularity of unit testing. Despite very good products, a free service to auto-generate unit tests for your code, and some terrific exponents (especially Alberto Savoia and Jeff Frederick) to tell their story, the company closed a down a few weeks ago, essentially having come to the conclusion that it could never be sold at a price that could repay investors. So rather than ask for more funding, it closed down. If unit testing were gaining popularity robustly, Agitar surely would have come to a different conclusion.

2) Few OSS products. Except for the xUnit frameworks themselves, few FOSS tools for unit testing have been adopted. The innovative Jester project, which built a tool that looked for untested or poorly tested logic, essentially stopped development a long time ago because to quote the founder, Ivan Moore, in a comment to me "so few sites are into unit testing enough to care about perfecting their tests."

3) Major Java instructors aren't teaching it. Consider this interview with Cay Horstmann, co-author of the excellent Core Java books. (He asks, "If so many experienced developers don't write unit tests, what does that say?" In speculating on an answer, he implies that good developers don't need unit tests. Ugh!)

4) Unit testing books are few and far between. I am seeing about one new one a year. And as yet, not a single book on JUnit 4, which has been out for nearly three years(!).

5) Alternative unit-testing frameworks, such as the excellent TestNG, are essentially completely invisible. I was at a session on scripting this spring at SD West and in a class of 30 or so, two people had heard of TestNG (the teacher and I).

I could speculate on causes, but I have no clear culprit to point to. Certainly, unit testing needs to be evangelized more. And evangelized correctly. The folks who insist on 100% code coverage are making a useful tool unpalatable to serious programmers (as discussed here by Howard Lewis Ship, the inventor of Tapestry). But, I think the cause has to be something deeper than this. I would love to hear thoughts from readers in real-world situations where unit testing has been abandoned, cut back, or simply rejected--and why.

It would be a shame to have unit testing disappear and its current users viewed as aging, pining developers hankering for a technology the world has largely passed by. That would return programmers to the tried-and-true practice of glassy-eyed staring at a debugger for hours--something I have not missed at all.

Knuth Interview Posted

My interview with Donald Knuth is now posted. It's a long piece, that has some unusually interesting points, including:

- why Knuth doesn't believe in designing code for reuse
- he's most unconvinced of multithreading and multicore on the desktop
- discussion of the tools he uses to program and write (including Ubuntu)
- etc.

A very fun read (and a fun interview to do).

Perfecting OO's Small Classes and Short Methods

In The ThoughtWorks Anthology a new book from the Pragmatic Programmers, there is a fascinating essay called “Object Calisthenics” by Jeff Bay. It’s a detailed exercise for perfecting the writing of the small routines that demonstrate characterize good OO implementations. If you have developers who need to improve their ability to write OO routines, I suggest you have a look-see at this essay. I will try to summarize Bay’s approach here.

He suggests writing a 1000-line program with the constraints listed below. These constraints are intended to be excessively restrictive, so as to force developers out of the procedural groove. I guarantee if you apply this technique, their code will move markedly towards object orientation. The restrictions (which should be mercilessly enforced in this exercise) are:

1. Use only one level of indentation per method. If you need more than one level, you need to create a second method and call it from the first. This is one of the most important constraints in the exercise.

2. Don’t use the ‘else’ keyword. Test for a condition with an if-statement and exit the routine if it’s not met. This prevents if-else chaining; and every routine does just one thing. You’re getting the idea.

3. Wrap all primitives and strings. This directly addresses “primitive obsession.” If you want to use an integer, you first have to create a class (even an inner class) to identify it’s true role. So zip codes are an object not an integer, for example. This makes for far clearer and more testable code.

4. Use only one dot per line. This step prevents you from reaching deeply into other objects to get at fields or methods, and thereby conceptually breaking encapsulation.

5. Don’t abbreviate names. This constraint avoids the procedural verbosity that is created by certain forms of redundancy—if you have to type the full name of a method or variable, you’re likely to spend more time thinking about its name. And you’ll avoid having objects called Order with methods entitled shipOrder(). Instead, your code will have more calls such as Order.ship().

6. Keep entities small. This means no more than 50 lines per class and no more than 10 classes per package. The 50 lines per class constraint is crucial. Not only does it force concision and keep classes focused, but it means most classes can fit on a single screen in any editor/IDE.

7. Don’t use any classes with more than two instance variables. This is perhaps the hardest constraint. Bay’s point is that with more than two instance variables, there is almost certainly a reason to subgroup some variables into a separate class.

8. Use first-class collections. In other words, any class that contains a collection should contain no other member variables. The idea is an extension of primitive obsession. If you need a class that’s a subsumes the collection, then write it that way.

9. Don’t use setters, getters, or properties. This is a radical approach to enforcing encapsulation. It also requires implementation of dependency injection approaches and adherence to the maxim “tell, don’t ask.”

Taken together, these rules impose a restrictive encapsulation on developers and force thinking along OO lines. I assert than anyone writing a 1000-line project without violating these rules will rapidly become much better at OO. They can then, if they want, relax the restrictions somewhat. But as Bay points out, there’s no reason to do so. His team has just finished a 100,000-line project within these strictures.

Easy Does It With easyb

I just got back from the CITcon conference, which is the thrice-yearly confab of agile developers who use continuous integration (the "CIT" in the conference name). This was my second time at CITcon. It's an open-space conference that is--surprise!--free, and chock-a-block full of good information. The principal reason it's so informative is that anyone committed enough to CI to go to a conference has probably spent a lot of time thinking about how to solve problems of build and test at his/her site. And this concern and reflection on these issues is amply evident in the discussions in the hallways and the informal presentations.

All the sessions I attended were thought-provoking. But probably the most interesting was a presentation by Andy Glover, the president of Stelligent, an agile consultancy. He runs a great blog in which has been touting a tool called easyb, which enables you to script unit tests so that they describe a scenario (rather than a code feature) and then test for the expected result. I've read Andy's enthusiasm for easyb, but it wasn't until I saw him demo it that I understood what the excitement was about.

The key benefits are 1) you can show a non-programmer (like the manager who is expecting the software any day now) that you have written tests that match every one of his requirements--easyb enables you to do this by writing the test in near English language; 2) you can test at a slightly higher level than the unit test: rather than test tiny features individually, you can quite easily test a succession of conditions that are chained together.

This approach is called--a little misleadingly,--behavior-driven development; which was an immediate turn off for me. I really don't want to learn another x-driven development. I just want to do what I do better. And I think easyb might just be such a tool. So, don't worry about the name, and hop over to the easyb website for a quick look-see. You'll like what you find.

Great Reference For Ruby

Ruby aficionados have been working for the last few years under a serious handicapt: there was not good, up-to-date reference on their favorite language. Sure, the Pickaxe book provided some guidance, but it's a hybrid work--part tutorial, part reference. And the reference section was a summary, rather than an in-depth exposition.

Ever-dependable O'Reilly just released Ruby Programming Language, which is without a doubt the definitive Ruby reference. Not only is it co-authored by Yukihiro "Matz" Matusmoto, the inventor of Ruby, but it is superbly well edited, so that every page is full of useful information presented clearly. And at more than 400 pages, that's a lot of information. Couple this book with The Ruby Cookbook, which I reviewed on this blog, and you have probably the best 1-2 combination for learning and using Ruby.

Restarting the Platypus And the Lessons Learned

As many of you know, I have spent much of my free time during the last 24 months working on an open-source project called Platypus. The project's goal is to implement a command language like TeX, which enables users to embed formatting commands directly into text and generate documents of typeset quality in PDF, Microsoft Word, and HTML. The aims of Platypus are to be much easier to use than Tex and to provide many features of interest to developers, especially for printing code and listings.

After approximately 20,000 lines of Java code and comments, I have concluded that I need to restart and re-architect the project. The more I code, the more I see that I am adding top floors to a leaning tower. Eventually I'll topple it. So by restarting Platypus, I hope to straighten out architectural shortcomings and deliver a better, more expandable product more quickly.

In the process of coming to this decision, I have been able to crystallize several key lessons, a few of which I could probably have seen foreseen.

PROJECT AND DESIGN LESSONS

1) It's extremely difficult to figure out where your architecture is deficient if you have never done the kind of project you're currently undertaking. The best you can do is layout some basic architecture, abide by good dev practices, and learn as you go. Alas, as in this case, it took 20K lines of work to recognize that the architecture was irretrievably flawed and how.

2) First, do the known hard parts that can't be avoided. In the case of Platypus, I knew from the get-go I wanted a full programming language for the user to employ (the lack of which is one of the major failings of TeX). Early on, I decided that language would be JavaScript (JS). And having decided that and knowing that Java 6 had built-in support for JS, I put the issue aside for later implementation. When I revisited implementing JS, I realized that my command syntax no longer worked well with JS and that some commands would have been better implemented in JS. Had I written code and worked with embedded JS from the beginning, I could have avoided these dissonances, and I would have experienced Platypus more from the perspective of the user.

3) If you have to write a parser, write it just once. I wrote a parser for basic and compound commands, but did not anticipate intricacies of the syntax for very complex commands (think of commands to specify a table, for example). When it came time to add these commands, I found myself undoing a lot of parser work and then trying to back-fit existing syntax in to the new grammar. Parsers are a nightmare to get right, so make sure you write them just once. This means planning all your syntax ahead of time and in great detail.

4) Learn how to present your project crisply. Everyone understands writing a debugger for a hot new language is a cool project that will attract contributors. But projects where there is no immediately identifiable built-in community require crisply articulated messages. I did not do this well.

PROGRAMMING LESSONS:

a) Design Java classes around dependency injection. This will make the classes work better together and help you test them well. I am not saying use a DI framework, which is overkill in many situations, just use the DI pattern.

b) When it comes to modularity for input and output processing, plug-ins are an excellent design model. They form a very convenient way to break projects into sub-projects, and they make it easier for contributors to work on a discrete part of the project.

c) Unit testing delivers extra value when you're writing difficult code. The ability to be deep in the parser and test for some specific semantic occurrence right away is pure gold. Unit testing can also show up design errors. At one point, I was implementing a whole slew of similar commands (relating to output of special characters). I'd made each special character its own class; so each character required: copying a class, renaming it, and changing two strings inside it. Then rinse, lather, repeat. Likewise, my unit tests were just copied, tweaked, and run. This obviously is not a great use of unit tests (what are you actually testing? your ability to tweak a test template?). This was a good clue that the design needs revisiting. And in fact, it did. In the new design, one class will handle all special characters.

OBSERVATIONS ABOUT TOOLS

1) Of all the Java IDEs I've used, I like IntelliJ IDEA best. And since I review IDEs regularly for InfoWorld, I've used lots of them including the high-priced pups. IDEA provides the best user experience, bar none. However, as the number of classes in Platypus climbed towards 200, I noticed IDEA became very slow. Clicking on a file to haul it into an edit window was a tedious process, sometimes taking 30 seconds or more (even with v. 7 of the product). Because of this, I will look elsewhere at restart. I expect to use NetBeans 6 (a hugely improved IDE, by the by) at least initially. We'll see how that works out.

2) I switched from Ant to Maven while working on Platypus. Maven is a much better solution for many build steps than Ant. See my blog post on this. However, I dislike both products. I find that I still have to waste lots of time doing simple configurations. Also, I also don't like using XML for configuring builds. I generally concur with Tapestry's Howard Ship, that Ivy plus some other tool might be a better solution. I'll explore this as I go.

3) Continuous Integration (CI) is a good concept and there are truly great tools out there. But outside of providing historical data about a project, CI's value is limited on a one-developer project. This especially true when builds are already being done on a separate machine and only code that past tests is checked into the repository. (Nonetheless, the historical data is reason enough to continue using it.)

There are surely other lessons to be learned, and as they come to me, I'll post them on this blog if they seem useful.

Words of Thanks

It would be quite wrong to end this post without pausing to deeply thank Jeff Frederick, who was exceedingly generous with his time and insights while I worked on this first phase and who hastened my realization of several important aspects that I've touched on in this post. Thank you!

Internal USB Ports: What do you think they're for?

Earlier this week, I was being briefed by HP about some recently released workstations. As we were moving through the slide-deck, a small item caught my attention: one workstation claimed to have 2 USB ports on the front panel, 6 on the back, and 2 marked "internal." Why, I asked, would anyone want an internal USB port on a PC? Care to guess?

The answer is: for dongle keys. Yeah, they're still around and they use USB form factors. The internal aspect is interesting. It's designed so you can insert the dongle, lock the PC and nobody walks off with the dongle key.

I honestly would never have guessed.