Simple C++11 – Part II – Class declarations

In the previous part, I’ve shown my guidelines for setting up compilation units. When writing simple application code with C++11, either classes or free-functions should be your main building blocks. Therefor, in this part, I will focus on what to look out for when writing class declarations.

While templates can be very useful, they do not scale well as the code base gets larger. Metaprogramming or other niche styles have their places, too, but I like to look at those as a means to create language extensions rather than principal implementation tools.

Avoid inline implementations

…especially in header files. It can be tempting to write classes solely in the header file. In fact, it has almost become a sign of quality for parts of C++ code to be header only. But this scales badly in most cases, and evolving such a code-base will result in a dramatic explosion of compile times. Always splitting classes into a declaration and definition acts as a first-level compile- firewall and dependency-breaker. Users of your class no longer need to worry about changes in the implementation of the member functions of that class. Note that those changes are often indirect: a change only affects a class that is used in the implementation of your class’ member functions. By splitting the declaration and definition, users of your class do not have to be recompiled.

But why stop at the compiler? The same argument holds for programmers. If you start to split interface and implementation on this level, you automatically provide ‘reader-firewalls’ as well. By just providing a clean header file, you are giving readers sort of a manual for your class. No need to look at the implementation at all, if the interface is well-defined.

Inline code definition is also the main reason against excessive use of templates. Yes, they grant a lot of flexibility, but you pay a hefty price which needs to be justified by an enormous reduction of complexity elsewhere. In general, templates are a bit too powerful for their own good, which is why they need extra moderation.

Always declare implicit functions

Implicitly declared functions seem comfortable, but they have a few implications that are hard to understand. First of, if an implicit function gets generated for your class, it will be generated as inline. This means that the implementation becomes a dependency to all users of your class. This can have very subtle effects such as this:

#include <vector>
class Entry;

class EntryManager {
public:
  EntryManager(EntryGenerator& generator);
  int getEntryCount() const;
  std::string getIDForEntry(int index) const;
private:
  std::vector<Entry> mData;
};

On the surface, it looks like there should be no dependency (other than the name) on MyEntry when including this header. But there is!
The destructor is not declared so it will get generated – as inline. Because deletion of a vector requires the held type to be complete, any place that needs to be able to destruct a MyEntryManager also needs to know how to destruct MyEntry, which is not intended at all. Remember there’s a total of six functions that can be implicitly generated! Because of that, there are analogous problems for copy-construction, assignment, move-construction and move-assignment.

To avoid these problems, either delete the function explicitly in the header, default it in the implementation file, or actually implement it. You rarely need to do the latter, so I advise to default all the ones you need, and delete the rest:

#include <vector>
class Entry;

class EntryManager {
public:
  EntryManager(EntryGenerator& generator);
  EntryManager(EntryManager const&)=delete;
  EntryManager& operator=(EntryManager const&)=delete;
  EntryManager(EntryManager&& rhs);
  EntryManager& operator=(EntryManager&& rhs);
  ~EntryManager();
  int getEntryCount() const;
  std::string getIDForEntry(int index) const;
private:
  std::vector<MyEntry> mData;
};

And somewhere in the implementation file:

EntryManager::EntryManager(EntryManager&& rhs) = default;
EntryManager::~EntryManager() = default;
EntryManager& EntryManager::operator=(EntryManager&& rhs) = default;

This has another nice side effect because the vector-template gets instantiated into that object file and does not “bloat” all use-sites.

Exactly one public function and one private data section per class

..starting with the public section. This is where you address the next programmer that has to read your class. And it should be the only place for him to look.

I avoid private member functions because they cannot be tested easily and can add hidden compile-time dependencies to a project. Why should a user of your class recompile if you change an implementation detail? For small and trivial implementation helpers, the unnamed-namespace in the implementation file is a much better place. If those helpers become larger or more complex, it is a better idea to implement them in a collaborating class, which can be tested and reused.

Protected member functions split your interface to two parts, one exclusively for derived classes and one for everyone (including derived classes). This is very rarely needed, and in almost all of those cases, a separate interface will scale better (although it is slightly harder to implement).

Either an interface or an implementation

So far, I have left inheritance out of the picture and only talked about concrete classes. Inheritance is actually rarely needed, composition often suffices. But if it is needed, make sure that a class is either concrete and final (implementations), or has a complete and minimal set of pure-virtual member functions (interfaces). This will result in shallow hierarchies and easily understood interfaces. Remember that inheritance is not a tool for sharing code from the classes you implement, but for the code using those classes – i.e. where the Liskov Substition Principle holds.

Now it gets really easy to implement new classes in the hierarchy: Just implement all the functions in the interface. No more questioning whether to leave the default behaviour or override. You will also automatically tend towards clearer separation of components – things that need to be polymorphic move to the interface, other  functionality merely uses it.

This pattern is useful even when polymorphy is not needed. Such small interfaces devoid of any implementation detail can act as another compiler firewall. Collaborators can work with just the interface and do not have to be recompiled when the implementation changes. Also, the interface can be implemented for mock or fake objects in testing.

Conclusion

This concludes the second part of the series. I originally intended it to be about how to write a whole class, but that would have been too much to digest for one post. I am well aware that some of these guidelines can stir quite the controversy in the C++ community. For example, declaring the implicit functions seems to be in conflict with the recently popular rule of zero. Scott Meyers had similar concerns, but does not quite touch the inline aspect.

For me personally, these guidelines have helped tremendously, especially when scaling to bigger code-bases. But as before, I am curious what others are thinking about this!

IS NULL or IS NOT NULL, that is the question

Today I’ll demonstrate a curiosity of SQL regarding the NOT IN operator in combination with a subquery and NULL values.

Let’s assume we have two database tables, users and profiles:

 users              profiles
+--------------+  +-------------+
| id  username |  | id  user_id |
| 0   'joe'    |  | 0   2       |
| 1   'kate'   |  | 1   0       |
| 2   'john'   |  | 2   NULL    |
| 3   'maria'  |  +-------------+
+--------------+

We want to find all users, which have no associated profile. The intuitive solution would be a negated membership test (“NOT IN”) on the result set of a subquery:

SELECT * FROM users WHERE id NOT IN (SELECT user_id FROM profiles);

The anticipated result is:

+---------------+
| id  username	|
| 1   'kate'    |
| 3   'maria'   |
+---------------+

However, the actual result is an empty set:

+--------------+
| id  username |
+--------------+

This is irritating, especially since the non-negated form produces a sensible result:

SELECT * FROM users WHERE id IN (SELECT user_id FROM profiles);

+--------------+
| id  username	|
| 0   'joe'    |
| 2   'john'   |
+--------------+

So why does the NOT IN operator produce this strange result?

To understand what happens we replace the result of the subquery with a set literal:

SELECT * FROM users WHERE id NOT IN (2, 0, NULL);

This statement is internally translated to:

SELECT * FROM users WHERE id<>2 AND id<>0 AND id<>NULL;

And here comes the twist: a field<>NULL clause evaluates to UNKNOWN in SQL, which is treated like FALSE in a boolean expression. The desired clause would be id IS NOT NULL, but this is not what is used by SQL. As a consequence the result set is empty.

The result for the non-negated membership test (“IN”) can be explained as well. The IN clause is internally translated to:

SELECT * FROM users WHERE id=2 OR id=0 OR id=NULL;

A field=NULL clause evaluates to UNKNOWN as well. But in this case it is of no consequence, since the clause is joined via OR.

Now that we know what’s going on, how can we fix it? There are two possibilities:

One is to use an outer join:

SELECT u.id FROM users u LEFT OUTER JOIN profiles p ON u.id=p.user_id WHERE p.id IS NULL;

The other option is to filter out all NULL values in the subquery:

SELECT id FROM users WHERE id NOT IN (SELECT user_id FROM profiles WHERE user_id IS NOT NULL);

Conclusion

Both field=NULL and field<>NULL evaluate to UNKNOWN in SQL. Unfortunately, SQL uses these clauses for IN and NOT IN set operations. The solution is to work around it.

Small is beautiful – CherryPy Microframework

We are developing web applications for our clients using various different frameworks and technologies. The choice depends on several different factors like hosting, the clients IT infrastructure and administration and of course project scope. Some of our successful web projects use the Grails or Ruby on Rails with JRuby full stack frameworks. While they provide a ton of powerful features they also intrinsically carry some complexity. This is not always needed nor wanted, some projects might fare well with much less. Sometimes the scope of a project is not entirely clear so choosing a lean alternative you can gradually extend and refine may be the right fit.

Full stack frameworks

A full stack framework usually delivers built-in solutions for persistence/database access, domain modelling, routing, html-templating and so on. If you know for sure that you will need all the provided features from the start and that they are a good fit feel free to choose a full stack framework like Rails, Grails, Play or Django. If you need less or there is no convincing solution for your needs start with a minimal system that delivers value to your clients.

Microframeworks

Microframeworks provide only the basic functionality for routing and handling requests. No templating, no databases, no session management etc. attached. We made really good experiences with microframeworks like Nancy for .NET or CherryPy for Python. You start very simple and are up an running in minutes. If most of your GUI is client-side you do not need templating and you do not have it from the start. You do not need a database, well, you do not have one. Your server side logic revolves around REST, there you go!

The key point is that you are not stuck with this minimal set of support but you can easily extend the framework with features if need be. And usually you have several options per concern to choose from. For templating in Python there are many different solutions like Mako, Jinja2Genshi and others. Need only file-based persistence, want to manage your database in SQL or need an object-relational mapper (ORM) – everything is up to you.

CherryPy

Our experience with CherryPy was very positive. It is easy to run standalone using the integrated web server. You can also run it behind any WSGI-compatible web server because a CherryPy application automatically serves as a WSGI application. This is very nice if you need the power of a native web server and the ease and flexibility of a Python microframework. CherryPy is also very flexible when it comes to request routing offering much convenience with the default routing and method annotations to expose actions on the one hand and _cp_dispatch or MethodDispatcher on the other. It feels easy to extend the solution bit by bit as needed, there is seldom something in your way. Configuration is easy and powerful and the documentation gets you up to speed most of the time.

Conclusion

Microframeworks let you choose what you need and which implementation best fits your needs. You do not carry all that complexity with you from the start and easily pull more framework support in as you go choosing the most appropriate for your current situation. Your choices may differ in the next project since every project is different.

Beyond agile: building on the roots

Software development changes. Over a decade ago the agile manifesto adduced evidence that the document heavy processes needed to change.
Before that progress was measured by producing documents, a big planning and design phase was held at the start of every project to minimize the risk of producing the wrong software. Requirements were carved into stone. Changes later in the processes were shunned.
Agile changed all that. And besides losing some valuable practices (like documentation) recent developments call for a new form and focus of software development.
What changed? Software is now used by all kinds of people. This is not new. But design, user experience (UX), user focus and so on need to be an integral part of software development.

Business people and developers and designers must work
together daily throughout the project.

Nowadays developers need to interact not only with business people but with designers as well. Especially the waterfall like processes from the UX design or product management world struggle with the iterative and highly unplanned nature of agile.

Deliver working software value continuously frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale.

Agile only thinks in iterations or sprints. Even this changed with continuous delivery and lean development. The focus is not so more on a plannable release schedule but on bringing value to the customer.

Our highest priority is to satisfy the customer and the users
through early and continuous delivery of valuable software.

With a user focussed process not only the customer but also the user needs to be satisfied. UX design has this focus.
But UX design needs a significant research (developers call this analysis), design and test phase up front. So in practice this is masked as a big sprint 0.
Unfortunately agile has no concept of planning, design or analysis. The agile philosophy concentrates on execution. This has lead to the notion that planning can be done in every iteration.

Simplicity–the art of maximizing the amount of work not done– Managing and understanding complexity–the art of finding the right places where to invest into details– is essential.

As little planning, design and analysis as possible. The famous pendulum swings the other way. In order to avoid the planning heavy processes from the past, agile ignored and many practitioners even shunned these practices. As many designers can tell us, you need a good foundation to start with. Research is needed. “But these requirements will change” I hear the cries of the past.

Welcome changing requirements, even late in development. Agile processes harness change for the customer’s competitive advantage and the user’s delight.

A dilemma. On the one hand we need an initial investment for a good foundation. On the other hand we know that many things will change during the course of the project. We are stuck.

The best architectures, requirements, and designs solutions emerge from self-organizing teams.

One key principle that the UX design world has to offer for software development is the reasoning. Everything builds upon another thing. There is an incentive to find the cause, to construct a chain of whys. Requirements do not emerge from teams. They are grounded in user needs and business goals. These form the foundation of every user centered project.

At regular intervals All through the project, the team reflects on how to become more effective, then tunes and adjusts its behavior approach accordingly.

The agile philosophy has one core principle which helps to build a bridge between the worlds of development and design: continuous feedback.
In order to build reasoning you need to test your assumptions. UX has many practices to validate assumptions: interviewing, prototyping, analytics.
Feedback is a vital to a project and needs to be included in every action, not only the technical and measurable ones.
This feedback needs to be continuous. Regular intervals won’t cut it anymore.
We need to remove the notion of phases, iterations and sprints from our thinking. We have to define practices that help to build software that meets goals and satisfies needs. These build upon another by reasoning and on demand, not when a time plan demands it. We cannot freeze features for an iteration. Work needs to be organized around streams not iterations. These are not isolated.
Lean thinking tries to foster the notion of bringing constant value to the customers and users. Value streams.
A stream has no defined time frame or regular interval.

Working software is Goals met and satisfied needs are the primary measure of progress.

To bring design into the boat we need move the focus away from requirements and features to goals and needs. Streams of development need to identify, evaluate and build these jobs to be done, the goals. Designers bring the user needs to the table. Developers add the technical constraints. Together all these three factors, business goals, user needs and technical constraints, are balanced by the team.
All this is not new. And in practice many feedback steps or actions are omitted because of time, budget and other constraints. Information and insights are lost because of communication or documentation problems.

The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.

Teams should consist of developers and designers. Both need to be present in a kind of pair. Not pair programming. Pair everything. Decisions are made at every step. All three factors need to be considered.
Right now this seems to be pretty abstract and I think we should break it down.
What are the actions during a project?

Listen

A project should start with listening (but it doesn’t need to). And listening should never stop. Listen to the customer, to the users, to the systems, to the code.

Reflect

With all that information you get from listening you constantly need to filter, to reflect, to think. What is important? What not? What is an assumption? What has changed? What needs to be together? What needs to be separated? What forms a whole? What is missing? What is common? What is different? Are we moving towards the goals or away?
Ordering and prioritizing is one of the most important tools we have to bring sense to the mess.

Imagine

How can all this information be translated into software? Into a user interface. Into parts of the software architecture. How will it be deployed? What is the environment? For the user and for the system.
Design systems, guidelines for design and development need to be constructed.

Test

Is my thinking and imaging, the design, right? Does it meet the goal? Is it fast enough? Are the technical constraints met? Does it help the user? All of this needs to be tested. Whether you use the real software, prototypes or other means: you need to remove assumptions and find proof that your translations of information into software works.

Create

This should be obvious. You need to write the software and the documentation.

Ship

Without ever reaching the user, software is worthless. It needs to be shipped. Continuously.

Build projects around motivated individuals. Give them the environment and support they need, and trust them to get the job done.

Every action happens all the time. There’s no right order of steps. This might sound chaotic.
But a project is more like the exploration of unknown terrain. You do not know beforehand what the terrain will be like. You will need to improvise. For this it is better to have practices and principles which help you recognize and react to change.
This change comes from questioning assumptions and furthering your understanding of the goals and needs. Change does not come out of nowhere. Be prepared to react to it.

Reality

One big problem from talking about development and design processes comes from the huge gap between ideal and real world. In the real world there’s not enough time, money, skills, people.

Agile processes promote sustainable development. The sponsors, developers, designers and users should be able to maintain a constant pace indefinitely.

Be pragmatic. You don’t need the newest tools. Go with universal tools: pen and paper, wiki and most importantly your head. In all this you need to think and you need to communicate. Thinking is hard and good communication is even harder. But they stand at the center of a successful project. Do not avoid meetings if they are needed for communication or decision making. Use what you have and build from there.

Continuous attention to technical excellence and good design thinking and communication enhances agility.

It is essential that you know some practices really well. Practices which help you with the actions: listening, reflecting, imagining, testing, creating, shipping.

(The quotes are taken from the principles behind the agile manifesto.)
(The idea for naming the first three actions came from Putting thought into things

The whole company under version control

by Sashkin / fotolia

A minor fact about the Softwareschneiderei that always evokes surprised reactions is that everything we do is under version control. This should be no surprise for our software development work, as version control is a best practice for about twenty years now. If you aren’t a software developer or unfamiliar with the concept of version control for whatever reasons, here’s a short explanation of its main features:

Summary of version control

Version control systems are used to track the change history of a file or a bunch of files in a way that makes it possible to restore previous versions if needed. Each noteworthy change of a file (or a bunch of files) is stored as a commit, a new savepoint that can be restored. Each commit can be provided with a change note, a short comment that describes the changes made. This results in a timeline of noteworthy changes for each file. All the committed changes are immutable, so you get revision safety of your data for almost no cost.

Usual work style for developers

In software development, each source code file has to be “in a repository”, the repository being the central database for the version control system. The repository is accessible over the network and holds the commits for the project. One of the first lessons a developer has to learn is that source code that isn’t committed to a version control system just doesn’t exist. You have to commit early and you have to commit often. In modern development, commit cycles of a few minutes are usual and necessary. Each development step results in a commit.

What we’ve done is to adopt this work style for our whole company. Every document that we process is stored under version control. If we write you a quote or an invoice, it is stored in our company data repository. If we send you a letter, it was first committed to the repository. Every business analysis spreadsheet, all lists and inventories, everything is stored in a repository.

Examples of usage scenarios

Let me show you two examples:

We have a digital list of all the invoices we sent. It’s nothing but a spreadsheet with the most important data for each invoice. Every time we write an invoice, it is another digital document with all the necessary text and an additional line in the list of invoices. Both changes, the new invoice document and the extended list are included in one commit with a comment that hints to the invoice number and the project number. These changes are now included in the ever-growing timeline of our company data.

We also have a liquidity analysis spreadsheet that needs to be updated often. Every time somebody makes a change to the spreadsheet, it’s a new commit with a comment what was updated. If the update was wrong for whatever reason, we can always backtrack to the spreadsheet content right before that faulty commit and try again. We don’t only have the spreadsheet, but the whole history of how it was filled out, by whom and when.

Advantages of version controlled files

Before we switched to a version controlled work style, we had network shares as the place to store all company data. This is probably the de-facto standard of how important files are handled in many organizations. Adding version control has some advantages:

  • While working with network shares, everybody works on the same file. Most programs show a warning that another user has write access on a file and only opens in read-only mode. But not every program does that and that’s where edit collisions occur without anybody noticing. With version control, you work on a local copy of the file. You can always change the file, but you will get a “merge conflict” when another user has altered the file in the repository after your last synchronization. These merge conflicts are usually minor inconveniences with source code, but a major pain with binary file formats like spreadsheets. So you’ll know about edit collisions and you’ll try to avoid them. How do you avoid them? By planning and communicating your work better. Version control emphasizes the collaborative work setting we all live in.
  • Version controlled data is always traceable. You can pinpoint exactly who did what at which time and why (as stated in the commit comment). There is no doubt about any number in a spreadsheet or any file in your repository. This might sound like a surveillance nightmare, but it’s more of a protection against mishaps and honest errors.
  • Version control lets you review your edits. Every time you commit your work, you’ll see a list of files that you’ve changed. If there is a file that you didn’t know you’ve changed, the version control just saved your ass. You can undo the erroneous change with a simple click. If you’d worked with network shares, this change would have gone unnoticed. With version control, you have to double-check your work.
  • There are no accidental deletions with version control. Because you have every file stored in the repository, you can always undo every delete operation. With network shares, every file lives in the constant fear of the delete key. With version control, you catch your mishap in the commit step and just restore the file.

Summary of the adoption

When we switched to version control for every company data, we just committed our network shares in the repository and started. The work style is a bit inconvenient at first, because it is additional work and needs frequent breaks for the commits, but everybody got used to it very fast. Soon, the advantages began to outweight the inconvenience and now working with our company data is free of fear because we have the safety net of version control.

You want to know more about version control? Feel free to ask!

The tables will eventually turn for every optimization

An inconvenient truth

One of the things every software developer has to learn the hard way is that performance optimizations are a bad thing most of the time. The lesson is counter-intuitive because it is in conflict with several fundamental motivations of our artist/engineer attitude:

  • We want our code to be as fast as possible or even faster. (We really like the prospect of making the impossible happen!)
  • We don’t want to waste resources if it can be avoided. (Digital resources have always been scarce. Development in abundance wasn’t taught!)
  • We want to be clever and one-up everybody with the latest trick/hack. (This ego boost driven development attitude is a major problem on its own!)

But we can’t deny that clever guys exist since forever and that their wisdom should be considered. Here’s one clever guy fourty years ago:

premature optimization is the root of all evil.

said Donald Knuth in 1974. A typical computer had RAM in the lower kilobyte range these days and clocked with around a megahertz. The Intel 8080 was introduced in this year. No sign of abundance all around.

Coming from this insight, i teach the “three rules of performance optimization” to my students:

  1. Don’t
  2. Not Yet
  3. Measure

A little disclaimer: Performance optimizations are measured in milliseconds. You are still responsible for complexity optimizations and should engage them. Complexity is measured in O(n).

I blogged about the rules some time ago, so I won’t repeat the whole meaning behind them. Today, I want to tell a story related to rule three (“Measure”) and why it’s generally a good idea to stay clear of optimizations if not absolutely necessary.

An accidental observation

by ortodoxfoto / fotolia

In one of our long-running projects, we store data in a 24/7 manner since more than ten years. The project started on Pentium IV boxes with 120 GB harddisks. Soon enough, the available disk space vanished rapidely. Our customer wanted to optimize the storage efficiency. We told him that we can always trade storage space versus computation time or the other way around (the infamous space/time tradeoff), but if we compress the data in the system’s archive to save space, it will result in slower archive access. Our customer understood the tradeoff and decided he wanted storage efficiency over access speed for the archive.

So we added a compression step when certain data types were stored in the archive. Because the data was text based (XML and other formats), the compression rate was at 90% and even higher, meaning that we could fit ten times more data on the disk than before. We certainly met the customer’s goal of store efficiency. But what about the access speed? We needed to add the decompression step for certain data types at the place where our system loads from the archive. After that, we hoped that the speed didn’t suffer too badly. We measured the access times before and after the change and couldn’t believe our eyes: The access time was nearly ten times faster than before. There was no tradeoff, we actually shrank memory consumption and computation time at once and in the same ballpark figure. We felt like heros.

Discovering false premises

Why did this happen? Our explanation is that at some specific point in time, the CPUs became too fast for a tradeoff. In the good old days, there really was a tradeoff: if you needed to compress parts of your harddisk to save space, loading these parts became slower. Because the CPU had to perform extra work on top of the loading, you had to wait longer. Loading more data directly from disk was faster than loading less data from disk and decompressing it. When the CPU became fast enough to actually decompress during the I/O delay of the harddisk, that’s when the raw amount of data that needed to be transferred from disk determined the loading speed. And since uncompressed data is larger, it now took longer to load than compressed data.

At some specific point in time, the old wisdom that compressed data is slower became a lie. Nobody told us. We weren’t heros, we just lived on false premises.

Our customer was pleased and continued to be pleased for years. Every few years, the CPUs of the target machines would become even faster, but the harddisk performance would hardly improve. The new wisdom was truer than ever: Less data is faster, regardless of what the CPU has to do with it. This was a performance optimization that could be used everywhere. You want to increase I/O speed? Compress the data.

The tables begin to turn

Fast forward to modern days. A new technology promised to improve harddisk performance by orders of magnitude: The solid state disk (SSD) delivers impressive amounts of data per second, but more important, gets rid of the initial seek time that magnetic spindle disks had (those few milliseconds to locate the data on the disk that felt like ages for modern CPUs). We started our migration to SSD in 2010 and were SSD-exclusive at the end of 2013. Our customer was a bit more hesistant, but the latest generation of target machines run on SSDs, too. So how did this affect our performance optimization?

As you can probably deduce by now, the decompression work of the CPU re-emerged in the archive access time. It’s by no means as bad as in ancient times, but the days of “no tradeoff” are gone, again. The performance optimization isn’t an optimization anymore. We still have it in the system, because it was never meant to improve performance, but storage efficiency, and it still does that perfectly. And needs to, because SSD are still expensive and don’t offer storage space in abundance. But the old wisdom is back (partially): compressed data is slower, if not by much.

What do we learn from this?

Over the course of a few years, a specific feature (transparent storage compression) in our system was a performance burden, then a performance booster and a burden again. We didn’t change the code, we just changed the hardware circumstances. The best lesson to be learnt is that no performance optimization lasts forever. Premises will change. Effects will be negated. Bottlenecks will be shifted. It’s best to know when that happens. And then it’s best to be able to revoke the optimization. Or, if all this sounds way too much trouble for such a tiny performance gain, remember the first rule of performance optimization (Don’t) and just leave it alone. You can always tell yourself that you’ve just optimized your code for the machine it will run on in ten years.

Recap of the Schneide Dev Brunch 2015-12-13

brunch64-borderedTwo weeks ago, we held another Schneide Dev Brunch, a regular brunch on the second sunday of every other (even) month, only that all attendees want to talk about software development and various other topics. So if you bring a software-related topic along with your food, everyone has something to share. The brunch was small this time, but with enough stuff to talk about. As usual, a lot of topics and chatter were exchanged. This recapitulation tries to highlight the main topics of the brunch, but cannot reiterate everything that was spoken. If you were there, you probably find this list inconclusive:

Company strategies

Our first topic was about the changes that happen in company culture once a certain threshold is overstepped. The founders lose touch with their own groundwork and then with their own employees. Compliance frameworks are installed and then enforced, even if the rules make no sense in specific cases. A new hierarchy layer, the middle management, springs into existence and is populated by people that never worked on the topic but make all the decisions. The brightest engineers are promoted to a management position and find themselves helpless and overburdened. Adopting a new technology or tool takes forever now. The whole company stalls technologically.

Sounds familiar? We discussed several cases of this dramaturgy and some ways around it. One possible remedy is to never grow big enough. Stay small, stay fast and stay agile. That’s the Schneide way.

Code analysis with jDeodorant

We devoted a lot of time on getting to know jDeodorant, an eclipse-based code smell detection tool for Java. We grabbed a real project and analyzed it with the tool. Well, this step alone took its time, because the plugin cannot be operated in an intuitive manner. It presents itself as a collection of student thesis work without overarching narrative and a clear disregard of expectation conformity. If several experienced eclipse users cannot figure out how a tool works despite having used similar tools for years, something is afoul. We got past the bad user experience by viewing several screencasts, the most noteworthy being a plain feature demonstration.

Once you figure out the handling, the tool helps you to find code smells or refactoring opportunities. In our case, most of the findings were false alarms or overly picky. But in two cases, the tool provided a clear hint on how to make the code better (both being feature envies). If the project would really benefit from the proposed refactorings is subject for discussion. The tool acts like a very assiduous colleague in a code review when every improvement gets rewarded.

We really don’t know how to rate this tool. It’s hard to learn and provides little value on first sight, but might be useful on larger legacy code bases. We’ll keep it at the back of our minds.

Naming and syntax rules

During the discussion about jDeodorant, we talked about naming schemes and other syntax rules. We remembered horrific conventions like prefixed I and E or suffixed Exception. The last one got some curious looks, because it’s still a convention in the Java SDK and some names won’t make it without, like the beloved IOException. But what about the NullPointerException? Wouldn’t NullPointer describe the problem just as good? Kevlin Henney already talked about this and other ineffective coding habits (if you have audio degradation halfway through, try another video of the talk). It’s a good eye-opener to (some of) the habits we’ve adopted without questioning them. But challenging the status quo is a good thing if done in reasonable doses and with a constructive attitude.

Unit testing

When we played around with jDeodorant and surfed the code of the project that served as our testing ground, the Infinitest widget raised some questions. So we talked about Continuous Testing, unit tests and some pitfalls if your tests aren’t blazing fast. The eclipse plugin for MoreUnit was mentioned soon. Those two plugins really make a difference in working with tests. Especially the unannounced shortcut Ctrl+J is very helpful. I’ve even blogged about the topic back in 2011.

Epilogue

As usual, the Dev Brunch contained a lot more chatter and talk than listed here. The number of attendees makes for an unique experience every time. We are looking forward to the next Dev Brunch at the Softwareschneiderei. And as always, we are open for guests and future regulars. Just drop us a notice and we’ll invite you over next time.