Abstractness, specificity, and complexity in software design

09/05/2017 ∙ by Stefan Wagner, et al. ∙ 0

Abstraction is one of the fundamental concepts of software design. Consequently, the determination of an appropriate abstraction level for the multitude of artefacts that form a software system is an integral part of software engineering. However, the very nature of abstraction in software design and particularly its interrelation with equally important concepts like complexity, specificity or genericity are not fully understood today. As a step towards a better understanding of the trade-offs involved, this paper proposes a distinction of abstraction into two types that have different effects on the specificity and the complexity of artefacts. We discuss the roles of the two types of abstraction in software design and explain the interrelations between abstractness, specificity, and complexity. Furthermore, we illustrate the benefit of the proposed distinction with multiple examples and describe consequences of our findings for software design activities.



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1 Introduction

Abstraction is an important tool in any engineering discipline but in software engineering it is essential. Everything we build during the development of software systems contains abstractions. Textual requirements, a high-level architecture view, low-level design and even actual code are abstractions from the real world and from the machine the software will be executed on. In all steps of software development, we are confronted with decisions about what abstractions to use. In particular, we often have to decide on what level of abstractness different artefacts should reside. The motives for abstraction are manifold but most commonly improved reuseability or reduced complexity are the goal.

For example, the design of a function that retrieves specific data from a database raises a number of questions: Will similar functionality be needed in other parts of the software? Hence, should the function abstract from a specific type of data? If it does, will it require additional parameters to support different types of data? It is well known that it is not always advisable to make everything generic and parameterisable (“over-engineering” or “gold-plating”) as the costs may outweigh the benefits due to increased complexity. On the other hand we do not only abstract to allow reuse but to facilitate understandability by reducing complexity. Some well-chosen abstractions help any reader of an artefact – even its author – to comprehend the design and implementation more quickly [5, 4, 22]. Still, it is not always best to have the highest level of abstraction possible. It is a common saying that we cannot understand something if it is too abstract. These questions illustrate that choosing the right abstraction level is non-trivial and involves complex trade-offs.

We use “artefact” as the basic unit of our consideration of abstraction in software design. An artefact can be anything that is created for developing software. However, for our discussion of abstractness only human-readable documents are of interest. Hence, an artefact can be a function, class, procedure, module, component, etc. All of these have a level of abstractness that needs to be decided on during design.

1.1 Problem

Despite the uttermost importance of abstraction in software development, the implications and trade-offs involved are not totally understood. What does it mean when I replace one or more artefacts by a more abstract one? What implications does it have on the complexity? Are there other properties of the artefacts involved? To our knowledge, there is no established basis to answer these questions today although they influence nearly every task in software development.

1.2 Contribution

As a first step to answer these questions, we propose three characteristics of artefacts that capture the necessary properties involved: abstractness, specificity, and complexity. These characteristics are of a relative nature and can only be analysed w.r.t. other artefacts. Abstractness is the degree of information loss an artefact has, specificity denotes the number of contexts it can be used in, and complexity is divided into detail complexity and dynamic complexity. The former is related to the number of elements, the latter to cause-effect relationships. This division allows to describe the effects of abstractions more precisely. Based on these characteristics, we analysed a set of examples that lead to several consequences for using abstractions.

1.3 Outline

In Sec. 2 we describe our basic view on software development and reuse including the decisive characteristics. Based on these characteristics, we describe two types of abstractions in Sec. 3. We illustrate the characteristics and abstraction types w.r.t. several existing languages in Sec. 4. We derive consequences of our findings for different areas of software development in Sec. 5. Related work is compared in Sec. 6 and final conclusions are given in Sec. 7.

2 Abstractness, Specificity, and

This section sets the scene for the following discussion about the effects of abstraction in software design. We first discuss the aims of abstraction and what kind of artefacts we consider. Then we describe the two levels involved in abstraction and what role the fixed and variable parts play. Finally, the three decisive characteristics of artefacts with respect to abstraction are defined: abstractness, specificity and complexity.

2.1 Aims of Abstraction

Abstraction is often said to have the aim of reducing complexity. However, we see complexity reduction as only one aspect and consider the following two reasons to be fundamental for abstraction. Firstly, abstraction is needed in order to be able to comprehend the necessary artefacts for software design. Reality and the software that interacts with reality is too complex to be understood as a whole. Hence, we need to divide it into smaller chunks and throw explicit information away in order to understand certain aspects.

Secondly, as is often pointed out (cf. [13, 16, 21]), reuse is inexorably tied to abstraction. We need to raise the level of abstraction of an artefact in order to be able to use it in different contexts. An artefact that is concretely shaped for a specific context cannot be considered very abstract. Abstracting it to a more general (or generic) artefact enables the reuse of the artefact. This is obviously also connected to the first part. Only an artefact that can be comprehended with reasonable effort will be reused.

Both aims of abstraction are related to communication. One could see the combination of all abstractions in an artefact as a language that is used to communicate between different developers or designers. The names of the abstractions that are introduced constitute the words of the language and the composition rules of the abstractions constitute the grammar. We use different language elements and combine them in certain ways, hence, we communicate. This communication between humans – as opposed to the communication with the computer – becomes more and more important. With advances in computational power and compiler technology the details of how the program is realised on the computer are less significant. Moreover, considering the sheer size of many of todays software systems and the time they are maintained, communication with humans via the programs is vital.

2.2 Artefacts in Software Design

When we discuss in this paper artefacts in software design, we mean all work products that are needed for the specification and realisation of the software. Hence, the range goes from algebraic specifications, to executable models, from conceptual models to compilable procedures and classes. We consider all levels of software design because the design decisions and effects are all of a similar nature w.r.t. abstractness, specificity and complexity. Because we consider communication between humans as the main issue, the artefacts are all human-readable.

There is one important difference when considering executability. In case we want to execute the artefact on a computer, there must be a mechanism in place that is able to generate the information that has been abstracted away. Otherwise, the computer will not know how to execute the artefact. For non-executable artefacts such as conceptual models, this is not necessary because the human reader is expected to be able to complete the information by himself. For example, a C compiler knows how to generate machine code for details like register allocation that is not made explicit in the source code (“abstracted away”). However, there is no program that is capable of executing a UML use case diagram as the information that is missing to generate an executable program cannot be reconstructed in an automated manner.

2.3 Two Levels of Abstraction

In his seminal paper on software reuse [13], Krueger presented a simple but powerful model that illustrates the nature of abstraction in software design. He starts from the assumption that abstraction is a concept that cannot be discussed for a single artefact but needs to be discussed w.r.t. two artefacts that are part of an abstraction relation. Here one of the artefacts is called the abstraction specification and the other is called abstraction realization. The relationship is also shown in Fig. 1. In the later discussion about the effects of abstractions we usually mean the abstraction specification.

Figure 1: Specification Realisation [13]

An abstraction specification always consists of a variable and a fixed part. The fixed part is what is set by the abstraction, i.e. the information that has been abstracted but is still visible. The variable part is the part that can be set when realising the abstraction. Hence, we can have a set of abstraction realisations that “instantiate” the abstraction specification. In the realisations, there is also a hidden part that is added. It is also fixed but not directly visible from the abstraction specification.

As an example, consider this concept of two levels of abstraction for (domain-specific) programming languages as illustrated in Fig. 2. The language, its elements, syntax and control-structure, is the set of abstraction specifications. It defines fixed parts that are always the same in the language such as the paradigm used or the hardware mapping. The variable parts are defined in the way the language elements can be combined by the programmer. All this information is derived from the problem domain the language aims at. A programming language used in embedded systems that are closely tight to the hardware such as C has usually quite different fixed parts than a hardware-independent language such as Java. The mapping of the objects in Java to the memory is hidden to the programmer. In C, the programmer has explicit means to manipulate the memory. Hence, depending on the language and its problem domain, we have different separations into the fixed and variable part.

Figure 2: Variable and fixed parts in programming languages

The fixed and hidden parts are all encoded into the compiler of the language. This way, they can be easily added during the compilation of a program. The program fixes the variable parts of the language – the abstraction specification – and feeds this information into the compiler. Together this information can be put into the solution domain. Hence, an executable for a computer system is created.

2.4 Characteristics

Three important characteristics of an artefact are needed to discuss the effects and trade-offs of abstractions: abstractness, specificity, and complexity. We discuss each of these in the following in more detail. Note that the characteristics are not intrinsic properties of the artefacts but they can only be analysed in the context with other artefacts.

2.4.1 Abstractness

In computer science, abstraction is always connected to information loss [9]. We remove explicit detail and thereby build models. Hence, abstractness, i.e., the degree of being abstract, is determined by the amount of visible, variable information contained in an artefact (cf. Sec. 2.3).

This view fits also to word abstractness used in linguistics as discussed by Kammann and Streeter [11]. The abstractness of a word is the number of subordinate words it embraces. In this definition, the more abstract word has less information as well. A simple example would be that “furniture” is more abstract than “chair”. The chair has more explicit information such as that it has four legs and that it has space for a single person to sit on. However, Kammann and Streeter discussed that the containment relation is not always easy to define. This is also the case in software design.

2.4.2 Specificity

Specificity is a characteristic that every programmer is familiar with. It is an often occurring question how specific or – in contrast – generic a certain solution should be realised. In essence, this means that the specificity of an artefact is defined by the number of contexts it can be used in. This is again related to the observation that there are variable and fixed parts in artefacts described in Sec. 2.3. The larger this variable part, the more generic is the artefact. The larger the fixed part, the more specific is the artefact.

For example, an abstract GUI builder language probably has the element

window that represents the standard window in the user interface. The specificity is then determined by the variability in the windows. A large variable part (e.g. a high degree of parameterisation) allows a use in many contexts. Hence, the specificity is low. A small variable part causes high specificity.

2.4.3 Complexity

The complexity of an artefact needs to be considered because it is one of the major aims of abstraction to reduce complexity. However, we found that there does not exist a single agreed definition of complexity and even philosophy has not agreed on a unified view. Following Backlund [2], complexity is “a measure of the effort […] that is required to understand and cope with the system.” This allows to measure the complexity of the system. However, it is too coarse-grained to describe the effects of abstraction in software design in detail.

In our context, the categorisation of complexity provided by Senge [19] is most useful. He distinguishes detail complexity from dynamic complexity. Detail complexity is what is often measured by complexity metrics, the number of parts of an artefact. Hence, the difficulty lies in overlooking the amount of details. Dynamic complexity, on the other hand, describes subtle cause and effect relationships, i.e. when it is not clear what effects certain inputs or changes will have. In a software library, the dynamic complexity is how hard it is to understand how to use the interfaces and what effects changing parameters have.

3 Types of Abstraction

Having defined these characteristics, we need to analyse how abstraction in software design influence these characteristics. Abstraction is a central activity in software development. We abstract from detailed concepts to make them more comprehensible and to handle them all in an uniform way. This actually includes two different types of abstraction: (1) simplifying abstraction and (2) generalising abstraction. Fig. 3 illustrates these two types with related artefacts – classes for example – on two levels of abstractness.

Figure 3: The two types of abstraction

3.1 Generalising Abstraction

We start with the abstraction that is the transition from the lowest level in Fig. 3 to the middle level. The type or generalising abstraction is a common activity when designing or writing software. We identify several artefacts that have many similarities and only differ in some aspects. In Fig. 3 the differing information between the artefacts and is only and . We then generalise and to that has a parameter which can process , , and anything of type . Here we see that must be some kind of super-type of the types of and . That is why we also call it type abstraction. However, by generalising, the type can be more than a simple union. Using this generalising step we also become more abstract. We loose the explicit information and and we have to input it when using . The major design goal for generalising abstraction is reuse. We want to use the common aspects of -like concepts several times. However, we see that we did not necessarily reduce complexity by the abstraction.

As an example consider that and are both GUI dialog windows that have the aim to choose an element from a tree. For the sake of the example, the only differing information is the title of the dialog window. In other words, and are strings that contain the window title. In our example this means that and . The generalising abstraction to would be a dialog with a parametric title. As both differing informations have the type String, we can simply introduce the parameter of that type. The dialog window will then use the value of as the title of the window. By this, we made the language element more generic because we are now able to use any kind of string as the window title.

3.2 Simplifying Abstraction

The transition from the middle layer to the top layer in Fig. 3 is the word or simplifying abstraction. This is the type of abstraction that is used when we want to reduce dynamic complexity. In Fig. 3 we remove the information about completely by abstracting from to . We do not talk explicitly about anymore. This makes the usage of simpler – less complex – than the usage of . In software design this means that we have some fixed value for that is included in the artefact. This fixed value is strongly dependent on the context. It needs to be a superordinate word of all the words it abstracts from. In any case, we will be more specific. We were able to put anything into as long as it is of type . We are probably not able to find an abstract word that embraces all of these possibilities and still makes sense in a realistic application.

In the dialog window example from above, we try to remove the parameter that takes the window title as input. This reduces the complexity of the artefact because the artefact user does not have to care about the window title anymore. However, this leaves the artefact designer with the task of finding a window title that is generic enough. In our case we could use the title “Choose file”. This would at least work for the cases where we would have used and . Yet, we still reduce the possibilities of . Now, the dialog can only be used when a file must be chosen. could in principle ask for anything. Therefore, the element becomes more specific when setting this information to a fixed value in the library.

One could argue, that in Fig. 3, we could move the abstractness directly from the lowest to the highest level, i.e., go directly from and to . Although this is what we often see in practice, in theory there is more to it. Implicitly, we always make a generalising abstraction before we use simplifying abstraction. We need to identify the parameters and their types that distinguish the less abstract artefacts before we unify them to a more abstract artefact. Hence, these two abstraction are needed and sufficient to describe the effects of abstraction in design.

3.3 Discussion

Tab. 1 summarises which consequences generalising and simplifying abstractions have for the specificity and both types of complexity. Both kinds of abstraction obviously increase the abstractness. However, the main aim of generalising abstraction is to make the artefacts more generic. Hence, the specificity in relation to the artefacts on the lower level decreases but at the cost of higher dynamic complexity. To comprehend the consequences for the detail complexity, one needs to understand the relationships and effects of the parameters in the more abstract artefact. Whether the detail complexity is really reduced depends on the number of parameters that need to be introduced and the number of elements that are abstracted from. If abstracting away two elements requires the introduction of two parameters, the detail complexity stays the same. If, however, the number of parameters is smaller than the number elements abstracted away, the detail complex decreases.

Type Specificity Detail com. Dynamic com.
Generalising - 0/- +
Simplifying + - +/-
Table 1: The influences of abstraction on specificity and complexity

Simplifying abstraction reduces the detail complexity but with the drawback of higher specificity. By removing the parameters from the artefact, we obviously reduce the number of units and thereby reduce detail complexity. However, it might be that we increase dynamic complexity because while using the artefact we cannot control it by the parameters. Hence, the cause-effect structure might not be easy to understand. These basic rules are often not considered during software design. If we want to reduce complexity without increasing specificity, we can only reduce the detail complexity, often with the drawback of increasing dynamic complexity. A reduction of dynamic complexity will always increase specificity.

4 Examples

To foster the understanding of the concepts presented in this paper, this section presents a number of examples from different domains and exemplifies how our considerations apply to them.

4.1 Natural Languages

Natural languages like software systems undergo a continuous evolution. A central part of this evolution is the extension of languages through the establishment of abstractions. As in software development the goal of this abstractions is usually improved reuse and reduced complexity.

A recent example is the word “Blog”. Before the concept of a Blog was known, people simply referred to the specific website they were talking about. As these website shared a number of characteristics like being updated frequently or being similar to an online journal the concept of “Blog” emerged. This abstraction allowed people to communicate about a whole class of similar websites. However, at that time the concept was not lexicalised and one had to use lengthy circumlocutions to refer to it. Obviously, the main motivation for introducing a single term describing the concept was reuse. It spared people from using lengthy descriptions again and again.

A Google search for define:blog yields about two dozen definitions of the term “Blog” that agree to a great degree on what a Blog is. Nevertheless, some definitions contain information others lack, e. g. that a Blog usually reflects the personal opinions of the author or that Blog entries are typically small. According to our analogy these details can be viewed as the parameters of the concept “Blog”. This shows that the generalising abstraction does indeed increase the complexity. Before the concept “Blog” was introduced one could simply refer to website or website . Using the abstracted concept “Blog” he needs not only to point that something is a Blog but also specify the parameters of this Blog.

As natural languages also aim at simplification the explicit specification of these parameters is usually omitted. Simplifying abstraction is used to abstract from the parameterised concept “Blog” to a parameterless concept with the same name. This example shows that abstraction also means loosing information: The parameterless concept “Blog” omits a number of details one needs to know to properly use it. As users of natural languages are intelligent human beings (as opposed to software compilers) they are usually capable of reconstructing this lost information from the context and their common knowledge. In certain situations, however, this information loss can pose severe difficulties for human communication, too.

4.2 Libraries

The development of libraries is a showpiece of the abstraction mechanisms described in this paper as their central goal is to abstract from complex underlying functionality and present it to their users in an easy-to-use manner. Examples for libraries can be found at several complexity levels and for various domains. Very well-known instances are Java Swing for building graphical user interfaces, Log4X to support logging functionality and Java Collections/C++ STL that provide commonly used data types.

A good example for the discussed types of abstractions is the GUI library Java Swing that allows developers to implement the elements and concepts typically found in user interfaces, e. g. windows, button, menus or listeners. In comparison to implementing a window in Java without such a library it doubtlessly raises the abstraction level and reduces complexity. In fact, this and similar libraries are so widely used that most developers do not know most of the details involved in drawing a window on the screen, making it aware to mouse events, keyboard events, etc. The library achieves this by presenting a well-chosen simplifying abstraction of the underlying details and thereby enables even casual users to quickly implement a program that opens a simple window.

However, the library can appear strikingly complex if one wants to fine-tune certain specifics of its behaviour. Based on our considerations about abstraction mechanisms, we claim that this can only partially be blamed on bad library design but is mainly rooted in the fundamentals of the generalising abstraction. The process of e. g. drawing a window is itself a complex task with countless variation points, like e. g. window size, colour, shape, resizeability, etc. As Swing is designed to enable the user to control a good part of these variation points it has to make them explicit as parameters that do increase its complexity (e. g. the JFrame class has well above 50 accessible parameters).

Although Swing is quite good at hiding this complexity by providing further simplifying abstractions (e. g. one needs to set only one parameter on class JFrame to show an empty window with a title) its usability suffers from the mere number of language elements it contains. Following our considerations in Sec. 3 we believe that this cannot be improved without limiting its genericity and thereby consequently narrowing the options a user has to fine-tune the user interface he implements.

4.3 Domain-Specific Languages

Domain-specific languages (DSLs) are built with the purpose to abstract from the solution language and enable their users to interact with concepts close to the problem domain. Prominent examples are SQL for database access, BNF for syntax specification, ANT for building software, LaTeX for type setting and the DOT language used by the graph layout tool GraphViz111http://www.graphviz.org/ to specify graphs.

The DOT language provides a good example for the discussed types of abstractions. Together with the GraphViz tool it allows the creation of directed graphs with highly sophisticated layouts without knowing the least bit about graph layout algorithms. Due to its use of simplifying abstraction the most simple graph can be specified by the single line: A -> B. Although the language allows some control of graph attributes like colours, fonts, line width, etc. it offers only limited options to influence the way graphs are laid out. While this may be perceived as limitation in specific situations, the achieved reduction of complexity enables users to efficiently solve their graph drawing problems in most situations.

One notable property that the DOT language shares with many other DSLs is its lack of mechanisms for defining own abstractions. In contrast to libraries that support new abstractions by using the mechanisms of their host language, DSLs often lack such mechanisms as this would require the language designers to explicitly create them.

4.4 Programming Languages

When writing programs with general purpose programming languages like C or Java, developers continuously build or extend languages by introducing new abstractions [18]. Even the definition of a constant like VALUE_ADDED_TAX_RATE = 0.16 is in fact an abstraction as it abstracts from a concrete number and introduces a new language element.

For more complex concepts the abstractions are typically realised by defining first-class program elements. An example is the search for a specific character in a string. This can be captured in a function to foster reuse. In its most concrete form such a function searches for a specific character in a specific string using a specific search algorithm. As this function is not reusable the developer has to abstract from the specifics to implement a function that can be used in multiple contexts. Therefore, he has to create a function with a number of parameters. These parameters include the character to search for and the string to be searched. It abstracts from a specific character and a specific string to all characters and all strings. The sort algorithm is parameterised to distinguish between case-sensitive and case-insensitive search.

As even this seemingly simple function is equipped with up to three parameters, this example shows how generalising abstraction increases detail complexity. Later the developer may find this function too complex and unhandy to use. He could then introduce a simplifying abstraction by introducing a new method that always performs a case-insensitive search. By doing so he reduces detail complexity and genericity. However, dynamic complexity can be increased because the user of the function needs to be aware of these options.

Looking at the whole language defined by the program, the question how this last step affected the abstractness, genericity and detail complexity of this language, is unfortunately not straight-forward to answer. An important factor is the fate of the original search function. If it was removed the language’s abstractness was increased while its genericity and complexity were reduced. If the new function was included in addition to the old function the the situation is more complex. The genericity of the languages did not change as the language still provides access to the old function. The complexity of the language, however, is a matter of discussion: On one hand one could say the language became more complex as it grew by one element. On the other hand one could claim that the language became less complex as it offers a simpler entry point now (the new search function). We believe that this cannot be answered in general and depends on the context as well as on the language design goals.

4.5 Modelling Languages

There is obviously no clear distinction between modelling languages, domain-specific languages and programming languages. However, modelling languages are typically considered to have a higher level of abstraction as programming languages and they often come with a graphical notation to support comprehension. Well known examples are the Unified Modelling Language (UML), Matlab Simulink and Stateflow, or Entity-Relationship diagrams (ER).

Recently, the model-driven architecture (MDA)222http://www.omg.org/mda/ approach has received considerable attention in research and practice. Its aim is to use simple UML models that are transformed several times down to running code. The promise is that the productivity in building the simple – abstract – models is much higher. This approach is a perfect example for the relationships described in Sec. 3. In current MDA tools the models that are mainly used are UML class diagrams. These diagrams cannot express a lot of detail about behaviour. The emphasis is on the data and structure similar to an entity-relationship diagram. In order to generate a runnable program from this model a great deal of additional information needs to be added. Hence, the fixed (and hidden) part is extensive. It depends largely on the tool and the way the transformations are described. This large amount of fixed information implies that the context in which the model can be used, i.e. the type of software that is generated from it, needs to be rather specific. Today, several MDA tools are able to generate simple, web-based database applications. However, if we needed to generate a different application, the abstraction provided would be useless.

Another example widely used in the embedded systems domain is the Simulink and Stateflow toolkit provided by the Matlab tool. Simulink is a graphical dataflow language that provides blocks for various continuous and discrete functions. They can be combined to calculate the results needed. Stateflow is a statecharts dialect that can be used in combination with Simulink blocks in order to model the more state-oriented parts of the application. There are some code generators available for these modelling tools that typically generate C code.

The Simulink/Stateflow toolkit is a prominent example for successful abstraction. The toolkit offers the abstractions typically needed for embedded systems design, parts of differential equations as well as state transitions. This frees the designer from a lot of the ugly details that are involved in embedded systems, especially the concrete hardware details and the interfaces to the platform software. However, for this to be possible, the toolkit is rather specific. It is useful for the embedded domain but to use it in web development is rather impossible because the data structure parts are not strongly supported. The abstractions chosen simply are not able to help in data design. We have again a certain amount of fixed information in the code generators. This information determines the contexts in which the language can be used and hence its specificity.

5 Consequences

We identified two main types of abstraction in software design and showed how they affect the three characteristics abstractness, specificity, and complexity of the designed artefacts. These definitions were substantiated by several examples and a survey. Based on this, we are able to describe several consequences and insights that follow from it.

Generalising Abstraction Increases Dynamic Complexity

A goal for language development is often to increase the abstractness in order to reduce complexity but also to decrease or maintain the specificity, e.g., [3]. We showed that generalising abstraction is able to increase abstractness by increasing the genericity. However, this is only possible by increasing the dynamic complexity which is in fact contrary to the initial goal of complexity reduction. The consequence is that we cannot increase genericity and reduce dynamic complexity at the same time. We are only able to reduce detail complexity. Hence, we need to decide on the trade-off between detail and dynamic complexity with respect to the expected reuse and understandability issues.

Simplifying Abstraction Increases Specificity

Abstraction is often proposed as the key to reduce complexity in software development. We showed that this is only true for simplifying abstraction. We loose information about some parts of the artefact. Hence, it is less complex and thereby becomes more comprehensible. However, we also saw that simplifying abstraction always implies a loss of genericity, i.e., an increase in specificity. Therefore, when we increase abstractness and reduce complexity, we must be aware that we need to increase the specificity. This applies, for example, to the model-driven architecture approach (MDA). This approach uses abstract models that – if not excessively parameterised – can only generate software for a specific, predefined domain.

To Manage Complexity, Design Specific and Generic

In general, it makes sense to have abstract, specific artefacts that are useful for new developers and common, often occurring situations. It is helpful for expert developers to have also more generic elements so that they can express their seldom occurring problems. This avoids detail complexity to a certain extent and hence supports reuse as well as understandability of the design.

6 Related Work

There is a surprisingly small amount of discussions of abstraction and its relationships with complexity and specificity (or generality) in the literature on software design. One exception is Kramer’s contribution in  [12]. He also states that there are two types of abstractions, a simplifying and generalising one. However, the article focuses on the abstraction skills of computer sciences students and, hence, does not discuss the consequences of this definition for software design.

Obviously, the abstractions introduced and used in common programming languages have been discussed, e.g. [20]. An area in software design where abstractions are used explicitly is design patterns [7]. However, also the pattern community fails to look at the effects on other properties of the design artefacts.

Krueger describes in [13] ideas that we used as a basis for several aspects of this paper. He states that abstraction is an essential part of reuse and that this has been noted by several other authors. It helps developers in selecting and specialising artefacts. He also remarks that a “generalized reusable artifact is in fact an abstraction with a variable part.” Finally, he uses the concept of cognitive distance that is similar to what we call complexity. In contrast to this work, we contribute the clear distinction between the two types of abstraction and their effect on the abstractness, specificity, and complexity of a design.

Because we consider also the elements of a (domain-specific) language as artefacts, the general literature on the design of programming language is also relevant [1, 6, 8, 14]. It defines several properties or characteristics of languages. The readability is a desired property of a language. This is related to abstractness and complexity. A language that is simple and uses abstract concepts is more readable than a language with many elements and technical details. The complexity of a language is also recognised as an important principle in language design by requiring simplicity. However, a definition of simplicity and its tradeoffs is not given in the literature. Related to our work is also the principle of programming efficiency or expressiveness of programming languages. It describes the easiness to express complex processes and structures. We can describe this issue by the abstractness and the specificity. Very abstract language elements (using simplifying abstraction) allow to express those complex processes and structures concisely. The genericity of such an element then allows to alter certain parts only. Hence, this also contributes to programming efficiency.

Our work is also related to the work on domain-specific languages, e.g., [15]. It is explained there that the design of a domain-specific language must pay off in terms of more efficient development and maintenance. However, the essential tradeoff between specificity and complexity is not explicitly stated. It is also noted that a domain-specific language can be an application library or simply embedded into a so-called general purpose language by abstract data types. This supports our view that all kinds of software design are similar to this respect.

Prenninger and Pretschner discuss abstractions for model-based testing in [17]. Although this is not general software design the essential ideas apply. They also see abstraction as losing information that can either be automatically inserted or not. The main goal for abstraction is given as simplification because generalising abstraction is not discussed in that paper. However, it is also stressed that abstraction (especially the automatically resolvable one) tends to be highly domain-specific. We are able to show why this is the case using our abstraction types.

Finally, the paper of Bernholdt, Nieplocha, and Sadayappan [3]

is an example that neglects the tradeoffs discussed in this paper. They classify languages for high-performance computing in the dimensions “abstraction” and “generality”. Based on this it is stated that the ultimate goal of further language developments should be to rank high in both dimensions, i.e., that new languages should be more abstract and more general at the same time. We show that this is only comes at the cost of higher dynamic complexity although their first goal was complexity reduction.

7 Conclusions

Abstraction is an essential activity in software design. We use it as a means to improve understandability by reducing complexity and to support reuse of the artefacts developed. However, the effects of the abstraction and hence the abstractness of artefacts is rarely discussed and not well understood. The extremes – very concrete or very abstract – are clearly not the aim in software design. There must be a reasonable choice somewhere in between. For this choice to be made, we need to know the effects and trade-offs involved.

For this, we introduce three characteristics of design artefacts: abstractness, specificity, and complexity. We show that there are two basic types of abstractions that have different influences on these characteristics. Generalising abstraction decreases the specificity and detail complexity of artefacts but also increases the dynamic complexity, simplifying abstraction decreases again the detail complexity but at the cost of higher specificity. The simplification can even increase the dynamic complexity. Obviously, both abstractions increase the abstractness. Based on these insights and several examples, we are able to formulate several consequences on software design.

We are fully aware that so far this is a rather theoretical work. It is quite probable that we will never be able to show these relationships empirically because there is a certain amount of subjectivity involved. For the detail complexity, we might be able to count the details and compare complexity but cause-effect relationships are not that easy to measure. Moreover, the specificity of an artefact is probably not measurable. How should we count the number of contexts where it can be used? Nevertheless, we see several relationships that hold in this fuzzy environment that in our view are useful to understand and to consider in software design in order to build good abstractions.

For future work, we plan to concretise the ideas presented here by building an abstractness-complexity calculus that allows us to express the interrelations in a more formal way. In general we aim at applying the developed concepts to different areas of software engineering. We see strong relations to the areas of aspect-oriented programming as well as product-line engineering. In these areas, our approach might help to explain the concepts and especially the shortcomings.


We are grateful to Manfred Broy, Martin Feilkas, Elmar Jürgens and Daniel Ratiu for fruitful discussions on the topic.


  • [1] D. Appleby. Programming Languages. Paradigm and Practice. McGraw-Hill, 1991.
  • [2] A. Backlund. The concept of complexity in organisations and information systems. Kybernetes, 31(1):30–43, 2002.
  • [3] D. E. Bernholdt, J. Nieplocha, and P. Sadayappan. Raising the level of programming abstraction in scalable programming models. In Proc. HPCA Workshop on Productivity and Performance in High-End Computing (P-PHEC ’04). IEEE Computer Society, 2004.
  • [4] T. J. Biggerstaff, B. G. Mitbander, and D. Webster. The concept assignment problem in program understanding. In Proc. 15th International Conference on Software Engineering (ICSE ’93), pages 482–498. IEEE Computer Society Press, 1993.
  • [5] F. Deissenboeck and M. Pizka. Concise and consistent naming. Software Quality Journal, 14(3):261–282, 2006.
  • [6] D. P. Friedman, M. Wand, and C. T. Haynes. Essentials of Programming Languages. MIT-Press and McGraw-Hill, 1992.
  • [7] E. Gamma, R. Helm, and R. E. Johnson. Design Patterns. Elements of Reusable Object-Oriented Software. Addison-Wesley, 1995.
  • [8] C. Ghezzi and M. Jazayeri. Programming Language Concepts. John Wiley & Sons, 1982.
  • [9] J. Guttag. Abstract data types, then and now. In M. Broy and E. Denert, editors, Software Pioneers – Contributions to Software Engineering, pages 442–452. Springer, 2002.
  • [10] C. A. R. Hoare. Notes on data structuring. In O.-J. Dahl, E. W. Dijkstra, and C. A. R. Hoare, editors, Structured Programming. Academic Press, 1972.
  • [11] R. Kammann and L. Steeter. Two meanings of word abstractness. Journal of Verbal Learning and Verbal Behavior, 10:303–306, 1971.
  • [12] J. Kramer. Is abstraction the key to computing? Communications of the ACM, 50(4):36–42, 2007.
  • [13] C. W. Krueger. Software reuse. ACM Computing Surveys, 24(2):131–183, 1992.
  • [14] K. C. Louden. Programming Languages: Principles and Practice. Thomson Course Technology, 2nd edition, 2002.
  • [15] M. Mernik, J. Heering, and A. M. Sloane. When and how to develop domain-specific languages. ACM Computing Surveys, 37(4):316–344, 2005.
  • [16] D. L. Parnas, P. C. Clements, and D. M. Weiss. Enhancing reusability with information hiding. In T. J. Biggerstaff and A. J. Perlis, editors, Software Reusability, volume I: Concepts and Models, pages 141–15. ACM Press, 1989.
  • [17] W. Prenninger and A. Pretschner. Abstractions for model-based testing. Electronic Notes in Theoretical Computer Science, 116:59–71, 2004.
  • [18] D. Ratiu and F. Deissenboeck. Programs are knowledge bases. In Proc. 14th IEEE International Conference on Program Comprehension (ICPC ’06), pages 79–83. IEEE Computer Society, 2006.
  • [19] P. Senge. The Fifth Discipline. Transworld, 1990.
  • [20] M. Shaw. Abstraction techniques in modern programming languages. IEEE Software, 1:10–26, 1984.
  • [21] T. A. Standish. An essay on software reuse. IEEE Transactions on Software Engineering, SE-10(5):494–497, 1984.
  • [22] A. von Mayrhauser and A. M. Vans. Program comprehension during software maintenance and evolution. Computer, 28(8):44–55, 1995.