thomasknierim.com

First I should explain what I mean with cup typing. When you buy a cup of coffee, you have the choice of short, tall, or grande sized cup. Sometimes you can also choose  decaf or regular. When you declare an integer variable in Java, you have the choice of  byte, short, int, and long. Sometimes (in languages like C++) you can also choose between signed and unsigned. The similarity is obvious. And it doesn’t end with integers. Floating point numbers come in two different flavours, namely as regular “float” values (32-bit) and as “double” values (64-bit). Characters come in the form of 7-bit, 8-bit and 16-bit encodings. In statically typed programming languages, multiplicity is the rule rather than the exception. While Fortran and Pascal offer a moderate choice of two different integers, Java offers four plus a BigInteger implementation (“extra grande”) for really large numbers. However, it’s C# that takes the biscuit in cup typing with 9 different integer types and 3 different real  types. Database systems are keeping up with this trend. For example, the popular MySQL RDBMS offers 5 different integer types and 3 different real types. Seeing the evolution from Fortran to C#, it almost appears as if type plurality has increased over time. We must ask two things: How did this come about and is it useful? We appreciate the fact that we can buy coffee in different cup sizes to match our appetite, but does the same advantage apply to data types?

The first question is easy to answer. Graduated types result from the fact that computer architectures have evolved in powers of two. Over several decades, the register width of the CPU of an average PC has expanded from 8 to 16 to 32 to  64 bits. Each step facilitated the use of larger types and numeric types in particular were closely matched to register width. Expressing data types in a machine-oriented way appears to be a C legacy and quite a few newer programming languages have been strongly influenced by C. - It is my contention that while curly braces and ternary operators are an acceptable C-language tradition, graduated types are definitely not. Why not? Because they counter abstraction. They hinder rather than serve the natural expression of mathematical constructs. Have you ever wondered whether you should index an array with byte- or short-sized integers? Whether you should calculate an offset using int or long values? Whether method calls comply with type widening rules? Whether an arithmetic operation might overflow? Whether a type cast may lose significant bits or not? All of this is a complete waste of time in my view. Wouldn’t it be better to let the virtual machine worry about such low-level questions, or the library if a VM is not present? Cup typing gets positively annoying when you have to write an API that is flexible enough to deal with parameters of different widths. If there’s no type hierarchy, you inevitably end up with multiple overloaded constructors and methods (one for each type) which add unnecessary bulk. The Java APIs are full of such examples and the valueOf() method is a case in point - it’s really ugly.

However, graduated types are beyond ugly; they are outright evil. They cause an enormous number of bugs and the small numeric types are the prime offenders. I wonder how many times a signed or unsigned byte has caused erratic program behaviour by silently overflowing. Such bugs can be hard to find and worse – they often don’t show until certain border conditions are reached. Casts that shorten types also belong to the usual suspects. I shall not even mention the insidious floating point operations that regularly unsettle newbie programmers with funny looking computation results. What numeric types does one really need? - Integer numbers and real numbers. One of each and not more. - If you want to be generous as a language designer, you can throw in an optimised implementation of a complex number type and a rational number type. However, in an object-oriented language with operator overloading, it’s fairly easy to express these in a library. The fixed comma type (sometimes called decimal type) is the subset of the rational type where the denominator is always a power of ten. So, that’s really all you need – a clean representation of the basic mathematical number systems.

At this point, you might object: “but the CPU register is only x bits wide,” or “how do I allocate an array of fifty thousand short values?”, or “can I still have 8-bit chars?” Unfortunately, there is no simple answer to these questions. The natural way to represent integers is to always use the machine’s native word width, but unfortunately that doesn’t solve the problem. First of all, the word width is architecture dependent. Second, it might be wasteful for large arrays that hold small numbers and on the other hand it would still be too small for applications that need big integers. The solution is of course a variable size type, i.e. an integer representation that can grow from byte size to multiple word lengths. We have variable length strings, so why shouldn’t we have variable length numbers? It seems perfectly natural. There is certainly some overhead involved, because variable length types need special encoding. The overhead will be most likely due to loading a descriptor value and/or to bit shifting operations. After all, variable length numbers don’t come for free, but they do offer tremendous advantages. They relieve the programmer from making type width decisions, as well as documenting these decisions - and worse - changing the type width later if the decision turned out to be inadequate. Furthermore, they eliminate the above mentioned bugs resulting from silent overflows and type cast errors, not to mention API proliferation due to type plurality. Thus variable length numbers are generally preferable to common fixed width types.

Of course, there are situations where you know that you will never need more than a byte. There are also situations where performance is paramount. In addition, APIs and libraries based on multiple fixed types are not going to disappear overnight. To provide backward compatibility and to offer optimisation pathways to the programmer, a language could present these as subsets of the mathematical type. For example, if a language defines the keyword “int” for variable length integer numbers, then “int(8)” could mean a traditional byte, “int(16)” could mean a short word, and so on. Now, this is a bit like reintroducing cup typing through the back door. Therefore the use of subtypes for general purpose computations should be discouraged. However, it’s always better to have a choice of fixed and variable types than having no variable types at all.

During the last few years, we have seen a trend change in CPU design. Until about 2003, CPUs  became more powerful through frequency scaling. The number of operations per second increased exponentially over a period of 20 years. Clock speeds went from 4.77 MHz in the first IBM PC (1981) to 3.3 GHz in a high-end PC of the year 2003. Thus, Moore’s law was supported chiefly by increasing clock speed. Then something “strange” happened: clock speeds plateaued. For a brief moment, it appeared that Moore’s law was nearing its end. Not because of the physical limits of miniaturisation, or because higher clock speeds were impossible, but because of thermal problems associated with such high clock frequencies. Water cooled systems were already introduced at the top-end of the market, but obviously the energy efficiency of these systems presents a serious economic problem. Then in 2004, the first PC dual core processor was introduced. Dual cores became widely available in 2005 and by now we have become used to quadcore processors, while eight-core systems are around the corner. The trend is obvious: in the forseeable future we will move from multi-core to many-core CPUs with dozens and perhaps even more cores.

Increasing the clock speed of a CPU has the effect, that program execution speed increases proportionally with clock speed. The increase is independent of software and hardware architectures. A sequential algorithm simply runs twice as fast on a machine where CPU operations take half the time. Unfortunately, this is not the case for a machine with twice as many CPUs. A sequential algorithm runs just as fast on a 3 GHz single core CPU than on a 3 GHz dual core CPU, because it uses only one of the CPU cores. Increased overall execution speed is therefore only achieved when multiple tasks run concurrently, because they can be executed simultaneously on different CPUs. This problem of parallelism is not new in computer science. Unfortunately though, today’s programming languages aren’t very well equipped to deal with parallel scaling. The computer language idiom that typifies multi-core concurrency is the thread model. Threads are lightweight processes that are typically used for self-contained subtasks. Several languages, notably Java, offer APIs and native support for their implementation. Threads are well suited to asynchronous processing, for example in communication and networking. They can also be used for simple parallelisation, where a computing problem is parallel by nature (for example serving multiple web documents at the same time). However, threads are relatively cumbersome and thus not really suitable for fine-grained parallel programming, such as traversing data structures or executing nested loops in parallel. Edward A. Lee describes the problems with threads in this excellent article.

The fundamental problem that software engineers face is described by Amdahl’s law. Amdahl’s formula expresses the speed gain of an algorithm achieved by parallelisation: speedup = N / ( (B*N) + (1-B) ), where B is the non-parallelizable (sequential) percentage of the problem and N is the number of worker threads, or processor cores. There are two notable things about Amdahl’s law: (1) the speedup is highly dependent on B, and (2) the curve flattens logarithmically with increasing N. It’s also important to know that Amdahl’s law assumes a constant problem size, which is unrealistic given that parallelisation  requires a certain amount of control overhead. Nevertheless, we can draw several conclusions from Amdahl’s law. First, the performance gain from parallel scaling is lower than that of frequency scaling. Second, it is not proportional to the number of cores. Third, it is highly dependent on software architecture, since the latter chiefly determines the size of B. From the perspective of a software engineer, the last conclusion is probably the most interesting one. It leads to the question what can be done to maximise parallelisation at the level of the algorithm exploiting data and task parallelisms. The present thread model is too coarse-grained to provide solutions at the algorithm-level. Hence, what is called for are new programming idioms that make this task easier.

Ideally, parallelisation is fully automatic, implemented by the compiler and the underlying hardware architecture. In this case, the application programmer could simply continue to formulate sequential algorithms without having to worry about parallelism. Unfortunately, automatic parallelisation has turned out to be extremely complex and difficult to realise. Despite many years of research in compiler architecture, there are no satisfactory results. Parallelisation at the algorithm level involves several abstraction steps, such as decomposition, mapping, scheduling, synchronisation, and result merging. The hardest among these tasks is very likely decomposition, which means breaking down a sequential task into parts that can be solved in parallel. The preferred method for doing this is divide and conquer. A problem is divided into identical smaller pieces, whereas the division can be expressed recursively or iteratively. The problem chunks can then be solved in parallel and the subtask results are joined into one final result. Prime examples for this strategy are merge sort and sequential search algorithms. These are easily parallelisable. Likewise many operations on arrays and collections can be parallelised fairly easily. But certain tasks are not obviously parallelisable, for example the computation of the Fibonacci series: f (n) = f (n-1) + f (n-2). Since every step in the computation depends on the previous step, the Fibonacci algorithm is sequential by nature. In theoretical informatics, the question whether algorithms of the complexity classes P and NP are in principle parallelisable is still unsolved. For practical purposes, some problems are simply non-parallelisable.

Given that automatic parallelisation is currently out of reach, the need for the facilitation of algorithm parallelisation in computer languages boils down to (1) the need for expressive constructs that allow programmers to express parallelisms intuitively, and (2) the need for freeing programmers from writing boilerplate code for the drudge work of parallel execution, such as scheduling, controlling, and synchronisation. There are already a number of computer languages that provide built-in idioms for parallel programming, such as Erlang, Parallel Haskell, MPD, Fortress, Linda, Charm++ and others. However, these are fringe languages with a small number of users. It is questionable whether the   parallel scaling trend in hardware will lead to a wide adoption of any of these languages. Perhaps mainstream languages will evolve to acquire new APIs, libraries, and idioms to support parallel programming. For example, there are Compositional C++, MPI, Unified Parallel C, Co-Array Fortran and other existing extensions that make widely used languages more suitable for parallel programming, although there aren’t any established standards yet. It also remains to be seen whether the functional programming paradigm will catch on in view of parallel programming. Java is promising, because the JDK 7 (Dolphin) will contain a  new API for fine-grained parallel processing. In this very informative article by Brian Goetz, the author of Java Concurrency in Practice, introduces the new java.util.concurrency API features of Java SE 7. It will include a fork-join framework that simplifies the expression of parallel processing, for example in loops or in recursive method invocation. It will also provide new data structures, such as ParallelArray, that make parallel iteration easier to express. To learn more about parallel computing, read the free Introduction To Parallel Computing by Blaise Barney or search the free parallel programming books at Google Books.

Perhaps you have never heard of the term web engineering. That is not surprising, because it is not commonly used. The fact that it is not commonly used is, however, surprising. Very surprising actually. The process of software creation resembles that of website creation and engineering principles are readily applied to the former. A website can be considered an information system. However, there is one peculiarity: the creation of a website is as much a technical as an artistic process.

The design of graphics and text content (presentation) goes hand in hand with the design of data structures, interactive features, and user interface (application). Despite some crucial differences, web development and software development have many features in common. Since we are familiar with software engineering, and since we understand its principles and advantages, it seems sensible to apply similar principles to web development. Thus web engineering.

Before expanding on this thought delving into the details of the engineering process, let’s try to define the term “web engineering”. Here is the definition that Prof. San Murugesan suggested in his 2005 paper Web Engineering: Introduction and Perspectives: “Web engineering uses scientific, engineering, and management principles and systematic approaches to successfully develop, deploy, and maintain high-quality web systems and applications.”

Maturation Stages

It seems that current web development practices rarely conform with this definition. Most websites are still implemented in an uncontrolled code-and-fix style without codified procedures and heuristics. The person responsible for implementation is often a webmaster who is basically a skilled craftsman. The process of crafted web development is quite different from an engineering process. Generally speaking, there is a progression that all fields of industrial production go through from craftsmanship to engineering. The following figure illustrates this maturation process (Steve McConnel, 2003):

At the craft stage, production is carried out by accomplished craftsmen, who rely on artistic skill, rule of thumb, and experience to create a product. Craftsmen tend to make extravagant use of resources and production is often time consuming. Therefore, production cost is high.

The commercial stage is marked by a stronger economic orientation. Growing demand, increased competition, and companies instead of craftsmen carrying out work are hallmarks of the commercial phase. At this stage, the production process is systematically refined and codified. Commercial suppliers provide standardised products at competitive prices.

Some of the technical challenges encountered at the commercial stage cannot be solved, because the research and development costs are too high for individual manufacturers. If the economic stakes are high enough, a corresponding science will emerge. As the science matures, it develops theories that contribute to commercial practice. This is the point at which production reaches the professional engineering stage.

On a global level, software production was in the craft stage until the 1970s and has progressed to the commercial stage since then. Though the level of professional engineering is already on the horizon, the software industry has not reached it yet. Not all software producers make full use of available methods, while other more advanced methods are still being researched.

Recent developments in methodology

Web development became a new field of production with the universal breakthrough of the Internet in the 1990s. During the subsequent decade, web development has largely remained in the craft stage. This is now changing, albeit slowly. The move from hand-coded websites to template-based websites, content management systems, and standard application packages signals the transition from craft to commercial stage. Nonetheless, web development has not yet drawn level with the state of art in software development.

Until recently, web development was like a blast from the past. Scripting a web application involved the use of arcane languages for producing non-reusable, non-object-oriented, non-componentised, non-modularised, and in the worst case non-procedural code. HTML, JavaScript, and messy CGI scripts were glued together to create so-called dynamic web pages. In other words, web development was a primitive craft. Ironically, all principles of software engineering were either forgotten or ignored. Thus, in terms of work practices, developers found themselves thrown back twenty years. However, the rapid growth of the Internet quickly made these methods appear outmoded.

During the past 15 years, the World Wide Web underwent a transformation from a linked information repository (for which it was originally designed) to a universal vehicle for worldwide communication and transactions. It has become a major delivery platform for a wide range of applications, such as e-commerce, online banking, community sites, e-government, and others. This transition has created demand for new and advanced web development technologies.

Today, we have some of these technologies. We have unravelled the more obscure aspects of HTML. We have style sheets to encapsulate presentation logic. We have OOP scripting languages for web programming. We have multi-tier architectures. We have more capable componentised browsers. We have specialised protocols and applications for a great variety of purposes.

However, we don’t yet have established methodologies to integrate all these technologies and build robust, maintainable, high-quality web applications. We don’t yet have a universally applicable set of best practices that tells us how to build, say a financial web application, from a server language, a client language, and a database backend. Consequently, there is still a good deal of black magic involved in web development.

If you build a house, you can choose from a number of construction techniques and building materials, such as brick, wood, stone, or concrete. For any of these materials, established methods exist that describe how to join them to create buildings. Likewise, there are recognised procedures to install plumbing, electricity, drainage, and so on. When you build a house, you fit ready-made components together. Normally you don’t fabricate your own bricks, pipes, or sockets.

Unfortunately, this is not so in the field of web development. Web developers do not ubiquitously rely on standard components and established methods. On occasion, they still manufacture the equivalent of bricks, pipes, and sockets for their own purposes. And they fit them together at their own discretion, rather than by following standard procedures. Unsurprisingly, this results in a more time consuming development process and in less predictable product quality.

Having recognised the need for web engineering, a number of question arises. What does web engineering have in common with software engineering? What are the differences? Which software engineering methods lend themselves best to web development? Which methods must be defined from scratch? A detailed examination of all these questions is unfortunately beyond the scope of this article. However, we can briefly outline the span of the field and name those aspects that are most crucial to the Web engineering process.

Web applications are different

Web applications are inherently different from traditional software applications, because they combine multimedia content (text, graphics, animation, audio, video) with procedural processing. Web development comprises software development as well as the discipline of publishing. This includes, for instance, authoring, proofing, editing, graphic design, layout, etc.

Web applications evolve faster and in smaller increments than conventional software. Installing, fixing, and updating a website is easier than distributing and installing a large number of applications on individual computers. Web applications can be used by anyone with Internet access. Hence, the user community may be vast and may have different cultural and educational backgrounds. Security and privacy requirements of web applications are more demanding. Web applications grow in an environment of rapid technological change. Developers must constantly cope with new standards, tools, and languages.

Multidisciplinary approach

Building a large website involves dissimilar tasks such as photo editing, graphics design, user interface design, copy writing, and programming, which in turn requires a palette of dissimilar skills. It is therefore likely that a number of specialists are involved in the creation of a website, each one working on a different aspect of it. For example, there may be a writer, a graphic designer, a Flash specialist and a programmer in the team. Hence, web development calls for a multidisciplinary approach and team work techniques. “Web development is a mixture between print publishing and software development, between marketing and computing, between internal communications and external relations, and between art and technology.” (Powell, 2000)

The website lifecycle

The concept of the website lifecycle is analogous to that of the software lifecycle. Since the field of software engineering knows several competing lifecycle models, there are likewise different approaches to website design. For example, the waterfall model can be used for relatively small web sites with mainly static content:

1. Requirements Analysis
2. Design
3. Implementation
4. Integration
5. Testing and Debugging
6. Installation
7. Maintenance

This methodology obviously fails for larger websites and web applications for the same reason it fails for larger software projects: the development process of large scale projects is incremental and requirements typically evolve with the project. But there is another argument that speaks for a more incremental/iterative methodology. Web applications are much easier to rollout and update than traditional software applications. Shorter lifecycles therefore make good practical sense. The “release early, release often” philosophy of the open source community certainly applies to web development. Frequent releases increase customer confidence, feedback, and avoid early design mistakes.

Categories of web applications

Different types of web applications can be distinguished by functionality (Murugesan, 2005):

Maintainability

Maintainability is an absolutely crucial aspect in the design of a website. Even small to medium sites can quickly become difficult to maintain. Many developers find it tempting to use static HTML for presentation logic, because it allows for quick prototyping, and because script code can be mixed seamlessly with plain HTML. What is more, presentation logic is notoriously difficult to separate from business and application logic in web applications. However, this approach is only suitable for very small sites. In most other cases, a HTML generator, a template engine, or a content management system will be more appropriate.

Dependency reduction also contributes to maintainability. Web applications have multiple levels of dependencies: internal script code dependencies, HTML and template dependencies, and style sheet dependencies. These issues need to be addressed individually. The reduction of dependencies usually comes at the price of increasing redundancy/complexity. For example, it is more complex to use individual style sheets for different templates, than to use one master style sheet for all. Internal dependencies and complexity levels need to be balanced by an experienced developer.

As in conventional software development, code reusability is paramount to maintainability. Modern object-oriented scripting languages allow for adequate encapsulation and componentisation of web application parts. The problem is that developers do not always make use of these programming techniques, because they require more upfront planning effort. In environments with high economical pressure, there is a tendency to ad-hoc coding which yields quick results, but lacks maintainability.

Scalability

Scalability is one of the friendlier facets of web development, because the web platform is -at least in theory- inherently scalable. Increasing traffic can often be handled by simply upgrading the server system. Nonetheless, developers still need to consider scalability when designing software systems. Two important issues are session management and database management. Memory resource usage is proportional to the amount of session management data. I/O and CPU load is proportional to concurrent database access, thus developers do well to anticipate peak loads and optimise links between different tiers in an n-tier system.

Ergonomics and aesthetics

The look-and-feel of a website, its layout, navigation, colour schemes, menus, etc. make up the ergonomics and aesthetics of a website. This is a more artistic aspect of web development and it should be left to a professional with relevant skills and a good understanding of usability aspects. Software ergonomics and aesthetics should not be an afterthought, but an integral aspect of the development process. These factors are strongly influenced by culture. A website that appeals to a global audience is more difficult to build than a website targeted at a specific group and culture.

Website testing

Website testing comprises all aspects of conventional software testing. In addition, it involves special fields of testing which are specific to web development:

• Page display
• Semantic clarity and cohesion
• Browser compatibility
• Navigation
• Usability
• User Interaction
• Performance
• Security
• Code standards compliance

Compatibility and interoperability

Web applications are generally intended to run on a large variety of client computers. Users might use different operating systems, different font sets, different languages, different monitors, different screen sizes, and different browsers to display web pages. In some cases, web applications do not only run on standard PCs, but also on pocket computers, PDAs, mobile phones, and other devices. While it is nearly impossible to guarantee proper page display on all of these devices, there needs to be a clearly defined scope of interoperability. This scope should spell out at least the browsers, browser versions, languages, and screen sizes that are to be supported.

Bibliography

Steve McConnell (2003). Professional Software Development
San Murugesan (2005). Web Engineering: Introduction and Perspectives
T.A. Powell (1998). Web site engineering: Beyond Web page design