Abstractions that are... too abstract? - oop

Question

In the Vaughn Vernon's Domain-Driven Design Distilled book we can read that we should try to avoid creating technical abstractions that are perhaps too abstract and try to be more explicit by sticking to the concepts of the Ubiquitous Language.
Where I work we've built several tracking applications and in almost every of them there is the problem of having multiple specializations of the same thing, most likely with common behaviors, but different data and validation rules.
For instance, imagine an incident logging application where various kind of incidents are reported over the phone (e.g. car accident, fire, robbery). The information gathering process is similar to every incidents, but the captured data may vary widely as well as the validation rules that constrains this data.
So far, we have always solved these kind of problems with very technical abstractions (this is an oversimplified model, but you should get the idea):
As you can see, the DataValidationRules, DataFields and DataEntries abstractions have very little to do with the business of incident logging. Actually, they are part of a very generic solution to the problem of representing multiple entity specializations with different data in any domain.
I'd like to move away from this kind of very abstract model, but at the same time I do not see what would be the correct approach in making the business concepts explicit. I understand that the answer would be different in every domain, but in essence, should I be looking into having a single class per specialization? E.g. CarAccidentIndicent, FireIncident and RobberyIncident?
With a very limited number of specializations it seems like it could be manageable, but what if I have hundreds of them?
What about the UI? That means I'd have to move away from a generic way of generating the UI as well.
After thinking a little more about it I think I may have found a better and simpler way to express my concerns when it comes to DDD, OO and modeling many specializations.
On the one hand I want to come up with a model that is faithful to the Ubiquitous Language (UL) and model domain concepts explicitly. On the other hand I'm trying to respect the "favor composition over inheritance" mantra I'm so used to apply.
It seems that boths are conflicting because in order to enable composition I'll have to introduce abstractions that are most likely not part of the UL (e.g. Entity--Field composition) and when it comes to explicit modeling I do not see any other way than inheritance with one class per specialization.
Am I wrong in trying to avoid inheritance to represent hundreds of specialized entities that mainly differ in terms of data structure, not behaviors?
Then again, assuming they did differ a lot in terms of behaviors as well I'd have the same dilemma.
Just to be more explicit on the design choices:
In one scenario, composition would be achievable dynamically without requiring multiple classes per specialized compositions:
class Incident {
Set<Detail> details;
IncidentType type;
}
interface Detail {
public DetailType type();
}
class SomeDetail implements Detail {
...
}
class SomeOtherDetail implements Detail {
...
}
In the other scenario compositions are static and do require one class per specialized composition:
class CarAccidentIncident extends Incident {
SomeDetail someDetail;
SomeOtherDetail someOtherDetail;
}
class SomeDetail {}
class SomeOtherDetail {}
Obviously, the second approach is more explicit and offers a natural home for specific behaviors and rules. In the first approach we would have to introduce some abstract and technical concepts like Operation and DetailValidation which may not align well with the UL.
With a small number of different specializations I'd probably choose the latter without a second though, but because there are many of them it seems like I'm leaning more towards dynamic composition (even thought being dynamic is not required). Should I?

Answer 1

When to use DDD?
The thing is, DDD is not necessarily the right fit for all systems. It's particularly well suited to large systems with complex business rules.
If the business rules that need expressing to capture the essence of a FireIncident are simple enough to be encoded in a DataValidationRules record and a set of DataFields, then that suggests that perhaps those rules do not require the complexity of a DDD implementation.
The Domain of Data Validation
However, if you acknowledge that, you can shift your perspective towards intending to actually build a pure data validation engine. The domain of data validation should include entities such as data validation rules, and data fields, and would contemplate such questions related to the lifecycles of rules and fields - e.g. 'what happens if a validation rule changes - do all existing records that have previously been validated need revalidation?'
If the lifecycle of a data validation rule itself is complex enough to warrant it, then by all means, use DDD to implement that domain, although you may still choose to use CRUD if you find there are no complex rules or processes in the domain of data validation.
Who are your Domain Experts?
The further consequence of that is that your domain experts are no longer your end users (the people who know about car accidents and fire incidents) they are now the people (most likely specialists) who craft the validation rules and fields. If using DDD, you need to be asking them what types of rules they need and how they need the rules to work, and implementing using the Ubiquitous Language that they use to talk about the art and process of crafting validation rules.
Those people, in turn, would be 'programming' a next level system (you might say they are using a 4GL language tailored to the domain of incident logging) using your data validation engine. The thing is, their domain experts would be the people who know about car accidents. But the specialists wouldn't strictly be using DDD to craft the rules of a car accident, because they would not be expressing their model in software, but in the constrained language of your data capture and validation engine.
Additions following Update
Have been pondering this since your update and had a few more thoughts/questions:
Data Validation vs Entity Lifecycle/Behavior
Most of your concern is around representing data validation rules on create/update. Something that would help to understand is - what behavior/rules are represented by your entities other than data validation? i.e. in an incident management system, you might track an incident through a set of states such as Reported, WaitingForDispatch, ResponseEnRoute, ResponseOnSite, Resolved, Debriefed? In an insurance system you might track Reported, Verified, AwaitingFunding, Closed, etc.
The reason I ask, is that in the absence of such lifecycle behavior - if the main purpose of your system is pure data validation, then I return to my original thought of wondering if DDD is really the right approach for this system, as DDD brings greatest value when there is complex behavior to be modelled.
If you do have such lifecycles or other complex behavior - then one possibility is to consider the approach from the perspective of different bounded contexts - i.e. have one bounded context for data validation - which uses the approach you've described with more technical abstractions - as it is an efficient way to represent the validations - but another context from the perspective of lifecycle management, in which you could focus more on business abstractions - if all incidents follow similar set of lifecycles, then that context would have a much smaller number of specific entities.
Keeping entities sync'd between contexts is a whole 'nother topic, but not too troublesome if you adopt a service bus or event type technology and publish events when things change.
Updates to Validation Rules?
How do your business experts express requests for changes to validation rules? And how do you implement them? I'm guessing from what you've said, they probably express them in domain terms such as 'FireIncident'. But the implementation is interesting - do you have to craft data modification statements in SQL which get applied as part of a deployment?
Inheritance vs Composition
It seems that boths are conflicting because in order to enable composition I'll have to introduce abstractions that are most likely not part of the UL (e.g. Entity--Field composition)
I do not think this is true - composition does not have to require introducing technical abstractions. With either composition or inheritance, the goal is to distil insights into the domain to discover common patterns.
e.g. look for common behaviours or data validation sets and find the business language term that describes this commonality. e.g. You might find RobberyIncident and FireIncident both apply to Buildings.
If using inheritence you might create a BuildingIncident and RobberyIncident and FireIncident would extend BuildingIncident.
If using composition, you might create a valueobject to represent a Building and both RobberyIncident and FireIncident would contain a Building property. However RobberyIncident would also contain a Robbery property and FireIncident would also contain a Fire property. CarAccidentIncident and CarRobberyIncident would both contain a Car property, but CarRobberyIncident would also contain a Robbery property of the same type as the Robbery property on RobberyIncident - assuming they are truly common behaviours.
You may still have hundreds of classes representing specialised incident types, but they are simply composed of a set of value object properties representing the set of common patterns they are composed of - and those value objects can and should be in terms of ubiquitious language concepts.

Answer 2

My take on this is that not all information is pertinent to the domain.
I think that in many instances we try to apply techniques in an "all-or-nothing" approach whereas we may need to be focusing on the "right tool for the job". In the answer provided by Chris he asks the question "When to use DDD?" and mentions "The thing is, DDD is not necessarily the right fit for all systems." I would argue that DDD may not be appropriate for some parts of a system.
Would DDD be useful to create, say, a word processing application? I don't really think so. Although some good old proper OO would go a long way.
DDD is absolutely great for business behaviour focused bits of a system. However, there are going to be bits that can be modeled in a more technical/generic way that feed into more interesting business functionality. I'm sure that those incidents end up in some business process. An example may be a Claim. The business is very interested in tracking a claim and the claim amount, but where that claim came from isn't all too interesting. For all intents and purposes the "initiating documentation" may be filled in using pen and paper and scanned in to be linked to said claim. One could even start a new claim on the system using a plain text input.
I have been involved in a number of systems where a lot of peripheral data was sucked into the system but actually it wasn't really contributing much (law of diminishing returns and such).
I once worked on a loan system. The original 20 year-old system was re-written in C#. The main moving bits:
Client
Loan Amount
Payment schedule
Financial transactions (interest, payments, etc.)
All-in-all it is really a simple system. Well, 800+ tables later and stacks of developers/BAs and the system is somewhat of a monster. One could even capture stock and title deeds as guarantee. Now, my take would be to scan in some of this information and link it to the loan. However, somehow some business folks decide that they absolutely "must have" this information in the system. It isn't core though, I would say.
On the other end, another system I worked on calculated premiums. It was modeled quite business-like and was quite a maintenance nightmare. It was then re-written very generically by simply defining calculations that work on given inputs. There were some lookup tables for values and so on but no business processing as such.
Sometimes we may need to abstract moving bits into something that makes sense as an input or output and then use that in our domain. I think the UL should be used by ourselves and domain experts but it doesn't mean that we are not going to end up using technical concepts that are not part of the UL, and I think that that is okay. I'm sure a domain expert wouldn't care much for a SqlDbConnection even though we are going to using one of those in our code :) --- similarly we could model some structures outside of the domain proper.
In response to your update and question: I would not create a concrete class unless it really does feature in the UL in a big way. On a side note, I still favour composition over inheritance. I typically implement interfaces where necessary and go with abstract classes when inheriting, just to place some default behaviour when it helps.
The UL, as with any design, represents a model with nuances. We can apply DDD without using domain events. When we do use domain events we may even go with event sourcing. Event sourcing has very little to do with the UL in much the same way that the terms "Aggregate", "Entity", or "Value Object" would. The UL is going to be specific to the domain / domain experts and when we, as domain modelers, talk to each other we can describe various models in terms of DDD tactical patterns in order to bring across some of the specific UL concepts.
We have to listen to how a domain expert describes the problem space. As soon as we hear "When", as stated in so many other places, we know that we are probably dealing with an event. In much the same way we can listen to how a domain expert talks about the aggregates. For instance (totally bogus example):
"When a customer is registered we need to inform the supervisor of the CSR that initiated the request"
More loosely related to your example:
"When an incident takes place we need to capture some specific details regarding the incident. Depending on the type we need to capture different bits and validate that we have sufficient data to process our claim
Between these two we can see a distinct difference in how they are referring to interacting with the problem space. When a domain expert thinks of something in very broad terms I think it is prudent that we do the same.
On the other hand, should the conversation be more along the lines of this:
"When a car accident is registered we need to assign an assessor an wait for an assessment report that has to answer..."
Now we have something much more specific. These are, necessarily, mutually exclusive in that if they only ever talk about specifics then we go with "specific". If they first mention in broad terms and then specifics, we can also work in broad terms.
This is where our modeling is tricky to get right. It is the same nuance as we have in the Address as an aggregate vs value object "debate". It all depends on the context.
These things are going to be tricky and dependent on the domain in order to get right. As Eric Evans did mention: it may take a couple of models to get something that fits just right. This is necessarily so based on one's experience with the domain.