Watching Eric Evans explaining how to incorporate agile methods in domain driven design gives you an understanding of some of the differences and commonalities between domain driven development and agile methods.

Agile methods grew out of the fact that up-front design which involved months of analysis was getting out of synch with the world which required faster development cycles and the need to adapt to a world changing at high speed (*). However, the responsiveness of agile methods and their focus on the next iteration limited development the scope at which development is carried out and eliminated the design phase out of the development process. The result were applications for which development slowed, if not even stopped, once the application reached a certain complexity.

Eric argues that in order to fix this problem we need to bring domain driven design back into the picture, but different from the top-down, process-heavy manner in which it was conducted before. The main reason modelling an application iteratively is that the development of an application is a learning process, a process of discovering and annotating a particular domain.

Some agile techniques such as the ability of reacting swiftly to changes and to be able to perform significant changes late in the game are presented, but they seem to me to be liabilities rather than assets. Performing a significant change late in the game is a very expensive operation since it is essentially a re-organization of the concepts encapsulated in the model followed by the confusion generated by the dissemination of the new model; such things should be avoided at all costs. Rapid changes in the model are also bad since they can create confusion.

One agile practice which I think benefits domain driven design is the break-down of the design process in iterations during which new domain concepts are inserted into the model. It is very important that these iterations also make sure that the model stays in synch with the domain in order to avoid late, massive and expensive domain re-factorings. In order to do this Eric outlines a series of diagnosis measures used for detecting when the model is straying away from the model.
While these measures are necessary what is missing in this process is a way for the domain expert to validate the model. The knowledge transfers have no feed-back loop from the domain experts. The developer seems the sole owner of the model, ideally the domain expert could become more engaged in the definition of the model rather than a passive disseminator of knowledge.

Modelling brand new domains or domains in a state of change will probably always be error-prone because the development of an application in a such a domain is a discovery process for all the parties involved (from users to business analysts to developers) whose end-result is defining the domain. In such an environment probably the only way to define and disseminate domain knowledge is to set-up sessions with domain experts. However, knowledge transfers in mature domains should be handled differently in order to leverage the existing knowledge. It is a good idea to get into the domain by first reading up on it and then engaging in knowledge-transfers with domain experts.

Eric Evans covers a lot of ground in his talk from the methodology of transferring domain knowledge to diagnosing misalignments between the model and the domain, solutions to correcting such misalignments and strategies for coordinating teams working on the same project. However, I wish he would have talked a bit about the structure and skill-sets of the teams involved in domain-driven development, of the ways to disseminate domain knowledge into a team and of how to engage the domain experts in reviewing and providing feed back on the domain being developed.
All in all a very good presentation which I encourage people to watch.

* The rejection of the analysis phase by agilists could partially be explained by the fact that the agile movement started at a point where appeared brand new domains (such as e-commerce, e-marketing, content management) which were in a continous state of transformation and for which there was no prior knowledge. Analysis in this case was viewed, to a certain extent correctly, as a process which requires significant investements (in order to overcome the lack of knowledge) without a clear return (the results could be obsolete in a few months due to unforeseen changes).
At the same time the revolution in communications created the opportunity to add on stakeholders previously considered un-related to the domain. Encapsulating requirements in an application from unfoereseen stakeholders meant that the domains became more prone to change and harder to predict.
These factors contributed to the misconception that analysis hinders rather than helps.

In this post I will go briefly over some important issues regarding regression testing and they can be used for managing complexity that I have come across in my experience.

A batch of regression tests covering an appropriate part of an application is a very good strategy to deal with growing complexity. Furthermore, the more complex the application the higher the need for regression tests because as systems become more complex the components that make them up tend to become unwieldy (*). In a heterogeneous team with a high variation in skill sets the regression tests are probably best written and carried out by the most experienced members of the team (it goes without saying that the implementation of some functional aspect and the test covering it should be done by 2 separate persons). If the tests are written by the most experienced team members this renders them more expensive, as a result care should be taken regarding the scope of these tests. I generally consider that regression tests should cover higher-value use-cases at a higher scope and that this scope has implications for the testing infrastructure.

The tests themselves should be isolated from testing infrastructure concerns and should focus mostly on the domain that is being covered. A pretty good discussion of these items can be found here. However, the tests should also be run in an environment as close to the production environment as possible, having seen an instance where tests covering an application deployed on a legacy database were passing without even inserting a single record in the DB only to have the application have problems later in PROD because of legacy datastructures.

Manual regression tests are expensive and this high cost has some bad side effects apart from having a higher price-tag: manual regression tests tend to be executed late in the release process. Also, by the fact that they take longer (being manual) they tend to cut deeper into the safety buffer dedicated to fixing bugs found during the release process. In order to solve this problem the regression tests should be in a format which allows them to be carried out early in the development process in order to detect problems early, ideally as close as possible to the development. This would mandate that the regression tests have a high degree of automation and a certain level of embedment into the development process.

Ideally, automated regression tests should be written in a format that ensures that the tests can be manipulated with ease by both developers (in order to be able to run them at an early stage in the development process) and by domain experts/business analysts (in order to prove their validity). A solution which may satisfy these 2 different audiences could be regression tests written in the programming language of the application by experienced team members and which are read-able by domain experts (**). For complex domains the testing infrastructure may need to be adapted in order to satisfy the second point as well.

* Components become out of synch with the required functionality as the complexity that they need to cover grows. Most of the time this misalignment is due the fact that the development pace is too fast and the information for correctly re-designing components for new functionality is unavailable. As components become unwieldy the need to refactoring appears and with it the need for regression tests which are a key component in the refactoring process.

** In practice, however, it is pretty costly to create a test environment that satisfies these 2 different audiences. This is why automated regression tests are typically written in the language used for development with senior team members acting as domain experts for writing tests of validating tests written by team members.

This Infoq presentation on DDD reminded me of some opinions that I wrote a while ago about DDD and about how it can be effectively applied in a typical development environment.

From my experience there are a few obstacles to using DDD in a typical development environment and one of them is the vagueness which accompanies the definition of different artifacts (entities, value objects, etc…), vagueness which sometimes may generate conflicts (*). The same vagueness works against the size of the development team and the distance between developers (**). DDD is probably best applied in settings which minimize both the potential conflicts and additional overhead of creating and distributing new concepts: small teams of experienced developers in close geographical proximity.
DDD projects tend to exhibit a high degree of closeness between the code and the domain which is a pretty good thing. Code which binds to the domain closely tends to “document” the domain and drive down the time for making knowledge transfers.

Given that DDD is best applied at the high-end spectrum of the IT workforce it follows that it can act as a big differentiator in the productivity of development teams as it further increases the productivity of experienced developers away from the productivity of less experienced developers. However, the gains in productivity may be counter-balanced by the reduced size of the available developer pool and the higher costs and dependencies associated with it.

I would conclude with the fact that the current way of promoting DDD probably works against it. Typically DDD is portrayed as an art-form or craft available to the lucky few which can appreciate and exploit its finesse. This is a pretty good way of driving the typical IT project manager away, IT managers really disliking to rely on artisans rather than on professionals.
For DDD to be applied at a larger scale it needs to be accepted by the business and for this it needs a more “professional” image. Rather than focusing on the “beauty” of various DDD patterns the  DDD community should probably focus a lot more on how to better formulate and deliver DDD best practices to a large audience. This, or it risks to remain at the fringes of software development.

* These conflicts are usually generated when developers cannot agree on a particular design. Given that DDD pushes design right into the core of the software development process these conflicts are bound to appear more often. In order to minimize these conflicts a certain level of experience in software modeling, of communication skills and, to a certain degree, of negotiation skills is needed. All this, in return, requires experienced developers. It may also require to have a domain expert handy (which is typical of DDD projects where the domain expert is a key person in the development process) in order to proof-read the design.

** Size and geographical proximity of teams are determined by the costs of effective communication (costs which usually increase with size and distance). To these communication costs we need to add the costs of debating vague concepts, of defining them and disseminating the knowledge required for manipulating them. These additional communication costs come at the expense of the usual communication costs and require them to be reduced.

From my experience I see that replication of data usually helps deal with performance problems, but that sometimes this replication involves replication of functionality, sometimes you end-up replicating some data on a different system (let’s call this the client system) and then you find yourself needing to code on the client system some piece of business logic which resides on the system from where data comes originally from (let’s call this system the master system).

More often than not this is a pretty scenario, because every modification to the business logic done on the master system has to be re-coded on the client system. Sometimes you find that you need to coordinate the releases of these 2 separate systems in order make the whole picture functionally consistent. One way to avoid re-coding the business logic on the client system is to expose the business logic on the master system thru some remoting mechanism (EJBs, Hessian, etc…) and then have the client system pass the data that it replicated locally to the master system in a synchronous call. While this solves the fact having the same business logic reside in 2 places it makes the client system dependent on the master system (*).

One solution to the above conundrum that is to replicate the data, along with the results of carrying out that piece of business logic on that data, to the client system. With this arrangement the client system does not need to have the master system up and running and it also does not need to re-code the same business logic inside. If you manage to use this (this approach doesn’t work in all cases) you will find yourself exporting business logic asynchronously by exporting both data and the results of carrying out that business logic on the data.

If I don’t write another post till January I wish you a Happy New Year!!!!!

* There is a physical dependency (the client systems needs the master system up and running for it to service calls) and possibly a library depedency which may couple the release cycles of these 2 systems (if the client system communicates with the master system thru objects rather than thry wire protocols you will need to synchronize the release cycles of these 2 systems in order to make sure that the client system doesn’t run in PROD with obsolete libraries).

Typically knowledge is considered to be an asset, some going as far as saying that knowledge is power. Well, when you design interactions between systems is better to avoid systems know to much about each other. Having deep knowledge about a system with which you are integrating is both a moral hazard (it gives you the opportunity to hack your way out of problems) and creates high transaction/interaction costs (typically the costs of transferring this deep knowledge to someone else).
The same reasoning goes for interaction between modules in a single system, please check out this article

P.S. I was shocked to see the date on that article: 1971. The fact that we are still struggling with these problems shows a pretty big problem with computer science. Most people equate computer science with exotic algorithms, it would be about time to add to this equation software modelling. It is pathetic when useful concepts for writing software developed 30 years ago are still unknown to the typical developer.
Software development has a pretty big educational problem (most developers educate themselves on their own and learn on the job), it would not be a bad idea to address it.

Relational databases are going thru a rough patch right now, some pundits going as far as writing them off. Their main problem is the fact that they are constrained to run within the same physical box (*) and scaling out is pretty hard. Once the datasets reach a certain size you will probably need to look beyond the typical relational database at other ways to store your data.

One type of alternative datastore that is getting a lot of attention is the distributed key-value store which maps values to keys and then assign keys onto multiple storage nods according to a hash function (**). In such a setting you get an object thru a key, you work with it and then you save it back in the datastore. The fact that this configuration scales out easily (***) makes this datastore very appealing, and if you are working with large datasets you will probably have no choice but to use something like it.

The transition from relational databases to key-value stores will probably include taking relations out of your application, re-modelling your application in order to group data together and streaming data to a reporting database. Relational databases gave the user both data storage as well as relations between entities which came as both a blessing and a curse (while relations made reporting easier they also gave un-checked access to data which allowed all sorts of corners to be cut). Well, key-value stores will take relations out: data access is more local, you typically have access only to what is immediate to a particular entity and you cannot perform queries spanning your entire data model. This type of data access could turn out to be actually a good thing, because these very close relationships will force an application to have a cleaner design since you will not be able to rely on monster SQL statements for compensating for design deficiencies.

In an application which needs to handle massive amounts of data relations between data will be delegated to activities which require them (reporting is a pretty good example) and data will be streamed from the key-value stores to the relational databases where these activities take place.
All in all, a pretty good division of tasks: applications with a (hopefully) cleaner model relying on key-value stores streaming data to relational databases for reporting.

All the above doesn’t mean that relational databases will disappear, the vast majority of applications do not require to process the massive amounts of data which render your typical database unpractical. Relational databases will be around for quite a while.

* Obviously, there are database clusters (such as Oracle RACs), but they are pretty costly.

** Examples of key-value stores are Google’s Big Table, Amazon’s Dynamo and IBM’s eXtreme scale.

*** Actually, there are constraints attached to it. In order to get the best performance you need to model your application so that data access occurs within the same node in order to avoid a distributed transaction spanning across multiple nodes. Please read this article by Billy Newport from IBM for a better understanding.

In a previous post I was saying that some technological issues are simply proxies for organizational issues and that whether one techonology will thrive or fail within an organization is not determined exclusively on the technology itself but it could be influenced by the organization itself. I ended the post saying:
For example, you may find out that DSLs will not work in particular setting not because DSLs are bad, but because the organization within which these DSLs are implemented and used simply doesn’t warrant their success.

I will get into an example where using DSLs is probably not a good fit because of organizational issues rather than inner strengths and weaknesses. Let’s say that you have some distributed teams working on the same application and that these distributed teams are led by a team of architects which need to create an environment in which these distributed teams work together. I find it pretty hard to see how this environment could be constructed using DSLs, particularly iteratively-developed DSLs, because changes in the DSL would have to be propagated across multiple teams in dispersed locations which would need to be re-trained for every iteration. Compare this approach with creating an API that would be released every so often and for which re-training costs are very small.

I have the impression that the main cost of DSLs is the distance between the place where the DSL is produced and the place where the DSL is consumed and that DSLs tend to be either specific to the location where they are produced or widely known and used within a community (in the second case the distance between the DSL producer and the DSL consumer is compensated by “mind-share”). I would say that the main difference between a DSL and an API is that DSLs are more tightly bound to the domain (by definition) and that part of the costs of using a DSL is the cost of transferring the knowledge about that domain between various stake-holders. This would mean that domain experts can adapt to a DSL targetting their domain faster than a typical developer that is new to the domain and that domain experts would be more productive. The difference in productivity between various members of the work-force means that the work-force composition should partly drive the decision to choose a DSL or APIs on a particular project. Regarding the distance between DSL production and DSL consumption this paper discussing how distance affects knowledge transfers is probably a good read.

Other issues wih DSLs such as developer lock-in (a developer working with a DSL can become out of synch with the rest of the IT landscape), the lack of language design skills on the IT job market, the problem of capturing domain knowledge correctly in large or rapidly changing domains are not explored in this post.

I end this post hurriedly…

Later Edit: I was watching this interview with Charles Simonyi and I would say I remained with these impressions:
If the DSL comes from an already existing language, such as domain experts notations, then there are chances that the DSL will be a good language (this addresses the issue of lack of language design skills on the IT job market). The problem of creating a new language is done away with by re-using a currently used language (the domain experts notations) and creating a new way to implement this existing language.
Regarding domains which are changing rapidly Charles Simonyi says that by separating the notation from the actual domain schema it becomes pretty easy to change the notation (the DSL) without affecting the underlying domain schema. Creating a DSL becomes an iterative process.
End-user programming is to remain a pipe-dream because of the difference in skills between what is needed for programming and the difference in skills needed for working within the domain. DSLs seem to engage the domain expert more into the process of software creation rather than turn domain experts into programmers. Domain experts contributions to software creation thru DSLs may be simply to communicate the domain better to a programmer. Language work benches would create a new division of tasks according to skills.

LLE: I watched this presentation by Intentional Software on domain work-benches and I remained with these impressions:
In an environment where domain workbenches are used it looks like the programmer will be involved in creating projections to an operational environment. The developer will focus on the systems while the  domain experts will focus on the business logic. This division of tasks addresses the issues arising from developers’ lack of enthusiasm about the domain.
It looks like domain work-benches should be used in large and rapidly changing domains where the transfer of knowledge from domain experts to developers is very costly due to the size of the domain and the speed at which it moves (this answers one of my questions above).

I think that the argument that I was making above that DSLs will be location-specific still applies even with a domain workbench in the picture because knowledge transfers with high costs will not appear in the creation of the DSL itself (the DSL being just one notation for an underlying domain schema, one notation among many, it can be created and used locally) but rather in the creation of the domain schema itself (which may end up being a collaborative effort carried out among people in various locations).

As I was saying above I have the impression that in the near future domain workbenches and DSLs will be used where the costs of knowledge transfers from domain experts to developers is large. If the costs of creating languages start to drop and these domain work-benches become ubiquitous then it is possible that a complete separation between domain logic (stored in domain code and expressed thru various notations) and its implementation in a system (which appears to be essentially a projection of the domain code into an operational environment) will appear. It would be interesting to work with Intentional Software’s domain workbench too.

We will have to wait and see…

L3E: This is another presentation on the Domain Workbench.

Code reuse is one software development goal consistently sought for. Its main benefits are that the code which gets reused becomes an investment on which you can build later. However, code reuse has its limits when the codebase is shared by a large number of participants, limits due to communication costs and to diverging interests.

Code reuse granularity goes against the number of the participants because implementing code reuse means deciding on what is common (and prone to be re-used) and what is specific as well as on the mechanisms used for implementing the relationships between what is common and what is specific (via sub-classing, various design patterns, etc…). This process of decision starts to blur once the number of participants increases and the corresponding communication costs start to rise. Too many people arguing about what is common to all of them will have quite a few problems figuring out what they are agreeing on.

The interests of pool of participants also are also more prone to diverge as their number increases, further reducing what is common to these participants. The end result is that once the number of participants increases both the commonalities between them shrink (as their specific interests diverge) and finding and implementing these commonalities becomes more expensive (because of the higher communication costs which occur between a larger number of participants). In these situations the code which gets reused starts to boil down to the main architecture classes within which the participants to the projects are coding their specific classes.

Keeping the number of participants low (agile techniques suggest keeping the number of participants below 7) is a way to deal with a codebase and a way to encourage code reuse efficiently.
However, sometimes you may end-up with a high number of participants. In this case code reuse should be brought down to re-using the main architecture classes in order to avoid confusion born out of mis-communication.
Another technique to deal with a large number of participants is to partition the code-base in order to minimize the number of people which are sharing code, the less people are sharing code the less the communication costs and the more things they will have in common.
Big, unpartitioned codebases come with big problems from what I have seen so far.

I have avoided reading and writing about SOA lately because SOA has attained buzzword status and the high number of articles written about it makes it pretty difficult to follow. I also see so much repetition of the same terms and concepts that pretty much every article I have read in the last 4 months feels stale once I go over the first 2 lines.

Against this background I have read 2 interesting articles on SOA: one article by Martin Fowler about using open-source techniques in order to implement collaboration between a consumer service and a supplier service and one article about abandoning the mindset which focuses on IT and rather on focusing on the relationships between various services thru contracts and policies (contracts and policies are organizational issues and not technology issues).

I found Martin Fowler’s article refreshing, even though I have to say that I disagree with it. From what I understand from the article. I think that having developers commit code to a service that you need to connect to has high interaction costs (basically the developers need to understand the partner service, need to be coordinated with the rest of developers, etc…), interaction costs which drive down the number of services with which you can partner effectively. Martin Fowler takes the issue of partnering with a service even further by having developers from the service consumer become full rights committers to the codebase of the partner service. When your developers are committers to a different service you have to wonder on what service are these developers working. The service consumers and the service providers are becoming very tightly coupled, not thru the interfaces they use for communicating (REST, WS-*, wire protocols, etc…) but thru the developers that these 2 services are sharing. I would say that Martin Fowler open-source like approach to service interactions does not scale to a larger number of services. I could go a bit into the differences between open-source projects and your typical enterprise application (I think that the most important one is that on an open source project features are chosen by boiling down competing features to the lowest common denominator chosen by the OS project participants while your typical enterprise application chooses its features in order to boost its revenues), differences which will pretty much tell why OS-like projects are very rare in the enterprise computing world but I don’t really have the time.

I have to say that when comparing the above OS approach to SOA to the typical vendor approach to  SOA you see big differences: a typical vendor assumes that services are out there in a registry and that a business analyst can create new processes and new value by drag-and-dropping various services into an IDE storing references to firm-wide services. This vision does not have much in common with the real world for two important reasons: 1) there are interactions costs associated with consuming each service, interaction costs which are not expressed in a service IDE and 2) in order to manipulate services at a firm-wide scope you need to master knowledge at the same scope. Possessing knowledge at this scope is nearly impossible, I personally have seen rather the reverse: business analysts getting so specialized that their scope becomes more and more focused. As far as I see it in order to achieve the SOA-nirvana championed by your typical IT vendor you need significant disruptions in your work-force.
And this brings me to the MWD article which echoes some thoughts that I wrote a while back when I was saying that interactions between IT services will be driven by the interations between the BUs implementing these services. SOA is not about empowering some business analyst to draw some funny diagrams on a board and re-engineer information flows at the firm level, but rather about how you create relationships and interactions, how you create incentives for adhering to rule and penalties for straying away from them, how you delegate managing an interaction to the parties involved in it, how you identify and consolidate common tasks into shared services and how you allocate tasks which are particular to a small set of parties, how you deal with who loses in these shifts, etc…

As you can see, all these concerns don’t have much to do with IT, being first and foremost management concerns. The only thing that IT can do is help these management issue get resolved, but not resolve them. IT brings some capabilities which can be used: service registries (which consolidate the view of and access to IT assets), ESBs (which allow for controlling data flows), policy standards (which allow the automation of defining and enforcing policies), etc… These capabilities should not be mistaken for their usage or their ends.
Ultimately, all the IT issues on SOA will be pushed to the back and management will take over. Ideally this field will become so specialized and its audience so narrow that we will get rid of another tiresome buzzword…

Later Edit: As you can guess this post has been written in about half a dozen different sessions, each sessions adding a few more lines to what was written before. I don’t have much time to write anymore (and I guess it shows), but I felt compelled to comment on those 2 articles because I thought they stand out from the rest of articles written on SOA.