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...process, even when the required modifications are conceptually very simple. We argue that this problem can be ameliorated by adopting an approach in which logical data models of legacy application are used by software developers to understand, maintain, and transform the software. In addition, we outline the goals and status of the Mastery project at IBM Research, which aims to build a suite of tools for automatically extracting logical models from legacy applications, focusing initially on logical data models.
THE PROBLEM
For the past few years, our group at IBM Research has been investigating tools and techniques for analyzing and transforming legacy business applications, focusing on mainframe-based applications written in COBOL. (1) Such applications are often decades old and implement core business functionality. Yet they are difficult to update in a timely manner in response to new business requirements due to a number of factors that include the following:
[] Volume of code in a typical application
[] Logical structure of code has deteriorated as updates have accumulated over time
[] Functional redundancy
[] Structure of code reflects the dated technology on which it was built
[] Scarce technical skills
Size
Legacy application portfolios, that is, complete collections of programs and related components, can be very large. For example, one IBM customer had a portfolio consisting of 700 interdependent applications, 3000 online data sets, 27,000 batch jobs, and 31,000 compilation units. The sheer volume of information contained in an application of this size makes it impossible for an individual to understand the relationships between all parts of the application.
Deterioration
The logical structure of code and data tends to deteriorate over time as a result of a continuous stream of modifications and enhancements. For large legacy applications, persistent data is the principal coupling mechanism between components of an application portfolio. Yet, as an application evolves to meet new business requirements, the structure and coherence of the data models underlying the code decays faster than the structure and coherence of the basic control and process flow through the application. Perhaps this is because it is relatively easy to add new functionality to an existing application by creating modules that manipulate new data items stored separately from the original application data. The alternative of refactoring the basic process flow through the application to accommodate new requirements typically requires much more intrusive changes.
Redundancy
Over time, applications frequently accumulate a great deal of redundant code (multiple code fragments that perform the same logical function) and redundant data (data structures that represent the same information, perhaps with slight differences, and are scattered throughout the code). Reasons for this redundancy include incomplete integration of information systems following business mergers, performance-driven enhancements to the code, and quick "hacks" when adding new functionality under tight schedules.
Technology
The code structure of legacy applications often reflects the limitations of the programming languages used and the middleware on which it was originally designed to run. In many cases, the code structure dictated by the constraints of legacy languages and middleware renders such systems more difficult to understand and evolve than they would be if they had been implemented on modern platforms.
Skills
As new languages and software systems become popular, it becomes more difficult to find people with skills in legacy languages and systems.
Interest in the use of automated and semiautomated tools to analyze and transform legacy code is increasing. Such tools include program-understanding tools, tools for identifying and extracting semantically related code statements (through techniques such as program slicing (2)), tools for migrating from one library or middleware base to another, tools for integrating legacy code with modern middleware, and so on.
In the remainder of the paper, we first explain the value of logical data models and describe a number of applications of logical models to program-understanding and transformation tasks. Then we describe the Mastery project, which is concerned with developing algorithms and tools for extracting and manipulating logical data and the source code from which they are derived. We conclude with a brief review of...
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