code quality dds

Code QualityPreface

In programming, as in everything else, to be in error is to be reborn.
— Alan J. Perlis

The non-functional properties we can discern from reading a software system's code are associated with the product's non-functional requirements: the requirements that are not directly concerned with specific functions delivered by the system, but deal with broader emergent system properties. Some common non-functional properties are the various -ilities of a system: reliability, portability, usability, interoperability, adaptability, dependability, and maintainability. Two other significant non-functional properties concern the system's efficiency: its performance related to time constraints, and its space requirements.

The skill of reading code to discern its non-functional properties is crucial for two important reasons. First of all, a failure to satisfy a non-functional requirement can be critical, even catastrophic. A system that gets some functional requirements wrong (most software products contains such errors) may well be able to operate in a degraded mode; users can be instructed to avoid using some part of the functionality. On the other hand, errors in non-functional properties are often showstoppers: an insecure web server, or an unreliable anti-lock break system (ABS) are worse than useless. In addition, non-functional requirements are sometimes difficult to verify. We can not write a test case to verify a system's reliability or the absence of security vulnerabilities. Therefore, both the critical nature of non-functional properties and the difficulty to verify them suggest that when dealing with non-functional requirements and the corresponding software properties we need to muster all help we can get. The ability to associate code with non-functional properties can be a powerful weapon in a software engineer's arsenal.

Apart from the different perspective, Code Quality follows the successful recipe of its companion book Code Reading: focus on the reading of existing code, deal exclusively with real-world examples taken out of existing open source systems, reference all examples to their source, dissect code with annotated listings, provide meaningful exercises to strengthen a programmer's critical ability and skills, identify coding idioms and traps on the text's margin, summarize each chapter's advice in the form of maxims, tie practice with theory in the ``Further Reading'' section, and use the Unified Modeling Language (UML) for all diagrams. From the above recipe the most tricky ingredient was my self-imposed rule to avoid toy examples, drawing all code samples from existing open source projects. By following the rule, I often found myself spending hours to find an appropriate example: one that would illustrate the concept I was presenting, while also being understandable and short enough to include in the book. I found this exercise both intellectually simulating and a great way to impose discipline on my writing. Often, while searching for a particular weakness, I encountered other interesting elements worthy of discussion. At other times my search for an example of a theoretical concept proved fruitless: in those cases I could then credibly reason that the concept was not important enough in practice to include in the text.

The rationale and motivation behind Code Quality are also the same as those that started Code Reading: the reading of code is likely to be one the most common activities of a computing professional, yet it is seldom taught as a subject, or formally used as a method for learning how to design and program. The popularity of open source software has provided us with a large body of code that we can all freely read and learn from. A primer and reader, based on open source software, can be a valuable tool for improving one's programming abilities. I therefore hope that the existence of the two books will spur interest to include code reading courses, activities, and exercises into the computing education curriculum so that in a few years our students will learn from existing open source systems, just as their peers studying a language learn from the great literature.

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Last modified: 2006-01-04