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1.1: Why do we Reengineer?

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    A legacy is something valuable that you have inherited. Similarly, legacy software is valuable software that you have inherited. The fact you have inherited it may mean that it is somewhat old-fashioned. It may have been developed using an outdated programming language, or an obsolete development method. Most likely it has changed hands several times, and shows signs of many modifications and adaptations.

    Perhaps your legacy software is not even that old. With rapid development tools and rapid turnover in personnel, software systems can turn into legacies more quickly than you might imagine. The fact that the software is valuable, however, means that you do not just want to throw it away.

    A piece of legacy software is critical to your business, and that is precisely the source of all the problems: in order for you to be successful at your business, you must constantly be prepared to adapt to a changing business environment. The software that you use to keep your business running must therefore also be adaptable. Fortunately a lot of software can be upgraded, or simply thrown away and replaced when it no longer serves its purpose. But a legacy system can neither be replaced nor up- graded except at a high cost. The goal of reengineering is to reduce the complexity of a legacy system sufficiently that it can continue to be used and adapted at an acceptable cost.

    The specific reasons that you might want to reengineer a software system can vary significantly. For example:

    • You might want to unbundle a monolithic system so that the individual parts can be more easily marketed separately or combined in different ways.
    • You might want to improve performance. (Experience shows that the right sequence is “first do it, then do it right, then do it fast”, so you might want to reengineer to clean up the code before thinking about performance.)
    • You might want to port the system to a new platform. Before you do that, you may need to rework the architecture to clearly separate the platform-dependent code.
    • You might want to extract the design as a first step to a new implementation.
    • You might want to exploit new technology, such as emerging standards or libraries, as a step towards cutting maintenance costs.
    • You might want to reduce human dependencies by documenting knowledge about the system and making it easier to maintain.

    Though there may be many different reasons for reengineering a system, as we shall see, however, the actual technical problems with legacy software are often very similar. It is this fact that allows us to use some very general techniques to do at least part of the job of reengineering.

    Recognizing the need to reengineer

    How do you know when you have a legacy problem?

    Common wisdom says, “If it ain’t broke, don’t fix it.” This attitude is often taken as an excuse not to touch any piece of software that is performing an important function and seems to be doing it well. The problem with this approach is that it fails to recognize that there are many ways in which something may be “broken”. From a functional point of view, something is broken only if it no longer delivers the function it is designed to perform. From a maintenance point of view, however, a piece of software is broken if it can no longer be maintained.

    So how can you tell that your software is going to break very soon? Fortunately there are many warning signs that tell you that you are headed towards trouble. The symptoms listed below usually do not occur in isolation but several at a time.

    Obsolete or no documentation. Obsolete documentation is a clear sign of a legacy system that has undergone many changes. Absence of documentation is a warning sign that problems are on the horizon, as soon as the original developers leave the project.

    Missing tests. Even more important than up-to-date documentation is the presence of thorough unit tests for all system components, and system tests that cover all significant use cases and scenarios. The absence of such tests is a sign that the system will not be able to evolve without high risk or cost.

    Original developers or users have left. Unless you have a clean, well-documented system with good test coverage, it will rapidly deteriorate into an even less clean, more poorly documented system.

    Inside knowledge about system has disappeared. This is a bad sign. The documentation is out of sync with the existing code base. No- body really knows how it works.

    Limited understanding of the entire system. Not only does nobody understand the fine print, but hardly anyone has a good overview of the whole system.

    Too long to turn things over to production. Somewhere along the line the process is not working. Perhaps it takes too long to approve changes. Perhaps automatic regression tests are missing. Or perhaps it is difficult to deploy changes. Unless you understand and deal with the difficulties it will only get worse.

    Too much time to make simple changes. This is a clear sign that Lehman and Belady’s Law of Increasing Complexity has kicked in: the system is now so complex that even simple changes are hard to implement. If it takes too long to make simple changes to your system, it will certainly be out of the question to make complex changes. If there is a backlog of simple changes waiting to get done, then you will never get to the difficult problems.

    Need for constant bug fixes. Bugs never seem to go away. Every time you fix a bug, a new one pops up next to it. This tells you that parts of your application have become so complex, that you can no longer accurately assess the impact of small changes. Furthermore, the architecture of the application no longer matches the needs, so even small changes will have unexpected consequences.

    Maintenance Dependencies. When you fix a bug in one place, another bug pops up somewhere else. This is often a sign that the architecture has deteriorated to the point where logically separate components of the system are no longer independent.

    Big build times. Long recompilation times slow down your ability to make changes. Long build times may also be telling you that the organization of your system is too complex for your compiler tools to do their job efficiently.

    Difficulties separating products. If there are many clients for your product, and you have difficulty tailoring releases for each customer, then your architecture is no longer right for the job.

    Duplicated code. Duplicated code arises naturally as a system evolves,as shortcut to implementing nearly identical code, or merging different versions of a software systems. If the duplicated code is not eliminated by refactoring the common parts into suitable abstractions, maintenance quickly becomes a nightmare as the same code has to be fixed in many places.

    Code Smells. code smells Duplicated code is an example of code that “smells bad” and should be changed. Long methods, big classes, long parameter lists, switch statements and data classes are few more examples that have been documented by Kent Beck and others [FBB+99]. Code smells are often a sign that a system has been repeatedly expanded and adapted without having been reengineered.

    What’s special about Objects?

    Although many of the techniques discussed in this book will apply to any software system, we have chosen to focus on object-oriented legacy systems. There are many reasons for this choice, but mainly we feel that this is a critical point in time at which many early adopters of object-oriented technology are discovering that the benefits they expected to achieve by switching to objects have been very difficult to realize.

    There are now significant legacy systems even in Java. It is not age that turns a piece of software into a legacy system, but the rate at which it have been developed and adapted without having been reengineered.

    The wrong conclusion to draw from these experiences is that “objects are bad, and we need something else”. Already we are seeing a rush to- wards many new trends that are expected to save the day: patterns, components, UML, XMI, and so on. Any one of these developments may be a Good Thing, but in a sense they are all missing the point.

    One of the conclusions you should draw from this book is that, well, objects are pretty good, but you must take good care of them. To understand this point, consider why legacy problems arise at all with object-oriented systems, if they are supposed to be so good for flexibility, maintainability and reuse.

    First of all, anyone who has had to work with a non-trivial, existing object-oriented code base will have noticed: it is hard to find the objects. In a very real sense, the architecture of an object-oriented application is usually hidden. What you see is a bunch of classes and an inheritance hierarchy. But that doesn’t tell you which objects exist at run-time and how they collaborate to provide the desired behavior. Understanding an object-oriented system is a process of reverse engineering, and the techniques described in this book help to tackle this problem. Furthermore, by reengineering the code, you can arrive at a system whose architecture is more transparent, and easier to understand.

    Second, anyone who has tried to extend an existing object-oriented application will have realized: reuse does not come for free. It is actually very hard to reuse any piece of code unless a fair bit of effort was put into designing it so that it could be reused. Furthermore, it is essential that investment in reuse requires management commitment to put the right organizational infrastructure in place, and should only be undertaken with clear, measurable goals in mind [GR95].

    We are still not very good at managing object-oriented software projects in such a way that reuse is properly taken into account. Typically reuse comes too late. We use object-oriented modelling techniques to develop very rich and complex object models, and hope that when we implement the software we will be able to reuse something. But by then there is little chance that these rich models will map to any kind of standard library of components except with great effort. Several of the reengineering techniques we present address how to uncover these components after the fact.

    The key insight, however, is that the “right” design and organization of your objects is not something that is or can be evident from the initial requirements alone, but rather as a consequence of understanding how these requirements evolve. The fact that the world is constantly changing should not be seen purely as a problem, but as the key to the solution.

    Any successful software system will suffer from the symptoms of legacy systems. Object-oriented legacy systems are just successful object-oriented systems whose architecture and design no longer responds to changing requirements. A culture of continuous reengineering is a prerequisite for achieving flexible and maintainable object-oriented systems.

    This page titled 1.1: Why do we Reengineer? is shared under a CC BY-SA license and was authored, remixed, and/or curated by Serge Demeyer, Stéphane Ducasse, Oscar Nierstrasz.

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