Guest Post : Object-Oriented Design Patterns and Oracle Policy Automation #2
In a previous post by our guest writer Dr Jason Sender, he investigated improvements in Oracle Policy Automation rules by applying some of the principles of refactoring, and then he began to discuss Object-Oriented Design Patterns and Oracle Policy Automation, and their application in real-world contexts.
As before, this third article draws on the work and publications of Martin Fowler, which we discussed in the previous post, those of Joshua Kerievsky from his highly regarded book “Refactoring to Patterns”, and the ground-breaking work on design patterns called “Design Patterns: Elements of Reusable Object-Oriented Software”, which had four authors known collectively as the Gang of Four (GoF).
Before studying some further examples of patterns and their application to Oracle Policy Automation, it is probably wise to step back and take a broad view of the context. Computer science is often defined as dealing in abstraction, and software engineering as managing complexity, and the connection is that only by considering different parts of programs and systems as abstract concepts are we better able to manage complexity.
Design Patterns and Oracle Policy Automation in Context
To put it in terms more related to our daily jobs, Oracle Policy Automation is often integrated with other systems that the Oracle Policy Automation developer does not need to understand, and can think of in abstract terms. A good example would be the Siebel CRM or Oracle Service Cloud database that Oracle Policy Automation may interact with, but about which the Oracle Policy Automation developer may not need to know anything – beyond understanding the available attributes for mapping and having a brief overview of the context. Abstraction is about ignoring irrelevant details, and this is often accomplished by what is a theme running through many design patterns, which is to: “encapsulate the concept that varies” (GoF, p. 54).
We often obtain abstraction in Oracle Policy Automation by using indirection (interposing an intermediate attribute) to encapsulate the attribute that varies. This allows us to “Program to an interface, not an implementation“, as the GoF (p. 18) term it, the rationale for which is that the implementation can be changed if other parts of the program only depend on the interface.
If you come, like some of us here on the OPA Hub Website, from a CRM background, you will be familiar with the concept whereby access to a CRM Object is provided through an interface, and the interface does not change even if the Object undergoes modifications (such as when using the GetMetadata Operation of the Oracle Policy Automation Connector Framework).
Although design patterns and refactoring techniques should serve the goals of reduced duplication, reduced complexity, and increased clarity, these goals can be in conflict, not just with each other, but with certain Oracle Policy Automation-specific goals. Take for example one of the stand-out benefits of Oracle Policy Automation: Policy Isomorphism. This means having the same form (i.e., you can copy and paste legislation or other source material directly into Microsoft Word, and base your rules on this and compare them side-by-side) and this is in tension with the concept of intermediate attributes (adding more attributes to increase clarity).
Design Patterns and Oracle Policy Automation : Strategy and Template Patterns
With that in mind we return to another example of how Object-Oriented Design patterns can be applied to Oracle Policy Automation. The following extended example will be given to demonstrate how useful the Strategy and Template Method design patterns (which we adapt from the GoF book) could be in reducing the number of tables and increasing the flexibility of calculations. We show this extended example to demonstrate the size of the reduction in rules from applying these design patterns to Oracle Policy Automation. We start with an information collection screen and associated Boolean rules to derive values from the drop down list items:
We then look at the top level goals for determining the total profit of the company, which is our main goal:
These are then derived from three very similar tables of calculations, which are listed in succession below:
Design Patterns with OPA : Implementing the Design Pattern
We can now alter this to implement our design pattern. We first create a main rule to determine the total profit:
This is the Template Method pattern since it delegates a part of the algorithm – the tax factor, a newly created attribute. Then, we employ the Strategy pattern to effectively split up the tax factor into one of three algorithms (in effect, we are treating the tax factor as an algorithm and then applying Strategy to it). We do this by parameterising, based on type of company, using a feature called Apply Sheet that avoids multiply proven attributes by letting the parameter determine which Excel Worksheet applies:
Then each of the subsequent tabs has a small table. As an example, here is the mining sheet. The others have identical structure and adjusted values for the tax factor.
For the Strategy and Template Method patterns, applying these design patterns has transformed our example rulebase into something much more easily extensible.
Improved Maintenance and Clarity
If we were to create another sector (e.g., oil), it would be very easy to add on another sheet in the Excel Workbook and add it to the Apply Sheet. In fact, we could easily add another 20 sectors, whereas there would be a lot of time-consuming ‘find and replace’ work to do in the original, and we would have ended up with dozens of pages of rules. Moreover, the original algorithm had a lot of code duplication, as the same Boolean attributes were repeated in row after row in table after table.
Furthermore, if we had needed to add or remove conditions from the tables it would have taken extensive work in order to verify that each and every table was updated correctly. In the reformed algorithm, the conditions were written only once and these are easily changeable. And, we were able to eliminate three (possibly confusing) and unnecessary sub-totals relating to each of the company types. The unvarying part of the algorithm (the total revenue – the total cost) is now written once, rather than 15 times, and so it is easily changeable.
Finally, our new algorithm mentions only the tax factor. This means that if all tax rates were harmonised, a single tax rate declared, or a new formula implemented that did not depend on the company type, since we have encapsulated the part of the algorithm that varies, we could just delete the Excel table and introduce a new table for the tax factor that did not mention the type of company. This would not have been so easy to do with the original algorithm.
Once again, even from a very simple set of examples, it should be clear that Oracle Policy Automation rules will benefit from the targeted application of principles from programming – in this case, the Strategy and Template Method design patterns. For more information about the ideas discussed in this article, Dr Sender can be reached using his LinkedIn profile, at the end of this article.
Design Patterns and Oracle Policy Automation : Going Further
The OPA Hub and Dr Sender are currently working towards the launch of advanced training based on his work. If you are interested, please take a moment to answer the 1 question survey below (if you have not already registered for the OPA Hub you can do that here before you answer). Thank you!