A most frustrating problem often encountered by people, with experience in relational databases when they are introduced to OWL ontologies, is that OWL ontology reasoners seem to ignore constraints. In this post I give examples of this problem, explain why they happen and I provide ways to deal with each example.
A typical example encountered in relational databases is that of modeling orders with orderlines, which can be modeled via
Orderlines tables where the
Orderlines table has a foreign key constraint to the
Orders table. A related OWL ontology is given in Figure 1. It creates as expected
Orderline classes with a
hasOrder object property. That individuals of
Orderline are necessarily associated with one order is enforced by
Orderline being a subclass of
exactly 1 owl:Thing
Two frustrating and most surprising errors given the Order ontology are: (1) if an
Orderline individual is created for which no associated
Order individual exists, the reasoner will not give an inconsistency, and (2) if an
Orderline individual is created for which two or more
Order individuals exist, the reasoner will also not give an inconsistency.
Missing Association Problem
Say we create an individual
orderline123 of type
Orderline, which is not associated with an individual of type Order, in this case the reasoner will not give an inconsistency. The reason for this is due to the open world assumption. Informally it means that the only inferences that the reasoner can make from an ontology is based on explicit information stated in the ontology or what can derived from explicit stated information.
When you state
orderline123 is an
Orderline, there is no explicit information in the ontology that states that
orderline123 is not associated with an individual of
Order via the
hasOrder property. To make explicit that
orderline123 is not in such a relation, you have to define
orderline123 as in Figure 2.
hasOrder max 0 owl:Thing states that it is known that
orderline123 is not associated with an individual via the
Too Many Associated Individuals Problem
Assume we now change our definition of our
orderline123 individual to be associated via
hasOrder to two individuals of
Order as shown in Figure 3. Again, most frustratingly the reasoner does not find that the ontology is inconsistent. The reason for this is that OWL does not make the unique name assumption. This means that individuals with different names can be assumed by the reasoner to represent a single individual. To force the reasoner to see
order2 as necessarily different, you can state
order1 is different from
order2 by adding
order1 (or similarly for
Constraint Checking versus Deriving Inferences
The source of the problems described here is due to the difference between the
purposes of a relational database and an OWL reasoner. The main purpose of a
relational database is to enable view and edit access of the data in such a way that the integrity of the data is maintained. A relational database will ensure that the data adheres to the constraints of its schema, but it cannot make any claims beyond what is stated by the data it contains. The main purpose of an OWL reasoner is to derive inferences from statements and facts. As an example, from the statement
Class: Dog SubclassOf: Animal and the fact
Individual: pluto Type: Dog it can be derived that
pluto is an
Animal, even though the ontology nowhere states explicitly that
pluto is an
Many newcomers to OWL ontologies get tripped up by the difference in purpose of relational databases and OWL ontologies. In this post I explained these pitfalls and how to deal with them.
If you have an ontology modeling problem, you are welcome leaving a comment detailing the problem.
A question that is asked regularly in testing circles is: “When should you stop testing?”. With an infinite budget it may be reasonably easy to answer this question. When the budget is severely constrained, this question becomes more difficult to answer. An even more difficult situation to address is when the project starts out with a given budget, but during the project lifetime the budget gets significantly reduced (i.e. due to economic downturn). This forces us be able to provide the highest level of quality for the least amount of money.
How Testing Fails
A mistake that is often made is that the testing effort is distributed equally across the system – both critical and less critical portions of the system are tested equally. This results in critical parts of the system not being tested sufficiently and less critical parts being tested to the point of diminishing returns.
A further mistaken mindset of developers is that there is no such thing as a useless test, which may entice developers to add tests for the sake of adding tests. In actual fact every test (unit-, integration- or systems test) has to earn its place in the codebase. If there no good motivation for a test, the test must be deleted from the codebase. Why is that? It adds to volume of code that developers have to master to be productive members of the team. It adds to the volume of code that has to be maintained. As such tests adds to the overall cost of maintenance of a system. Remember: The most cost effective code to maintain is the code that has never been written.
Risk Based Testing
Risk based testing is a testing approach that is helpful in addressing these concerns. The main advantage of risk based testing is to enable prioritization of the testing effort. The heart of risk based testing is to provide a set of criteria for evaluating risk. Some criteria for determining risk can be found in . The criteria may differ depending on the project. Ideally the set of criteria needs to be negotiated with the project owners. Another important aspect is to decide on the level at which risk will be determined. For example, risk may be determined at business process, component or use case levels.
Some general criteria are provided below:
- How frequently is this use case used?
- What will be the cost of getting this use case wrong?
- What is the complexity of this use case?
- How often will the use case be changed?
A value from 1 to 5 is assigned for each of these criteria and the product determined. This product provides a way for ranking the risk of each use case. A higher risk value will indicate a use case with a higher risk.
Ideally determining risk should not be left to developers. Developers need to be guided by the project owner, the architect and the business analyst on their project.
Advantages/Disadvantages of Risk Based Testing
Advantages of this approach are:
- The highest risk items can be developed and tested first which reduces the overall risk on the project.
- If testing has to be watered down, a guideline exists for deciding what to test and what not to test.
- At any given time an indication can be given of the risk of the project by considering the highest risk use cases that has not been tested.
A possible disadvantage of risk based testing is that not everything will be tested, but as stated earlier, if a testcase cannot be motivated, it may be more cost effective to not have the test as part of the codebase.
Risk based testing provides a means via which one can estimate how well, or how poorly, a system has been tested.
Some developers who are passionate about testing are sometimes also fanatical advocates of Test Driven Development (TDD), to the point that they believe TDD is the only correct way to test applications. Strict adherence to TDD requires a test to be created first before any code has been written. As such proponents of TDD tend to see TDD as a design mechanism rather than as a test mechanism. Irrespective of whether TDD is used as a design and/or test mechanism, it has a number of pitfalls.
In this post I will:
- give a brief overview of the TDD cycle,
- explain why TDD as software design mechanism is inadequate,
- explain why TDD as software test mechanism is inadequate,
- explain the conditions under which TDD can be applied successfully to projects.
In TDD the steps to add new code are as follows :
- Add a (failing) test that serves as a specification for the new functionality to be added.
- Run all the tests and confirm that the newly added test fails.
- Implement the new functionality.
- Run all tests to confirm that they all succeed.
- Refactor the code to remove any duplication.
The aim of TDD is to provide a simple way to grow the design of complex software one decision at a time.
TDD as a Design Mechanism
From a design perspective TDD emphasizes mirco level design rather than macro level design . The weaknesses of this approach are:
- TDD forces a developer to necessarily focus on a single interface. This often neglects the interaction between interfaces and leads to poor abstraction. Bertrand Meyer gives a balanced review of the challenges regarding TDD and Agile in this regard .
- Quality attributes (like performance, scalability, integrability, security, etc.) are easily overlooked with TDD. In the context of Agile, where an upfront architecture effort is typically frowned upon, TDD is particularly dangerous due to poor consideration of quality attributes.
TDD as a Testing Mechanism
From a testing perspective TDD has the following drawbacks:
- Interfaces/classes may be polluted in order to make them testable. A typical example is that a private method is made public in order to test it. This obfuscates the intended use of the class which will cause developers to more easily digress from the intended use of the class. In the long term this creates an unmaintainable system.
- Often when TDD is used on projects, unit testing is used to exclusion, with limited or no regard for the broad spectrum of testing, which should at least include integration testing and systems testing.
- TDD has no appreciation for the prioritization of the testing effort: Equal amounts of effort are expended to test all code irrespective of the associated risk profile. Typically TDD expects all further development to be blocked until all tests pass. This ignores the reality that some functionality has greater business value than others.
Guidelines for using TDD Effectively on Projects
TDD can be used with no side effect if the following process is adhered to:
- The architecture has to be complete, which should include details as to how testability of the system at all levels (unit-, integration- and systems testing) will be achieved.
- The risk profile of the sub system (module/use case) has been established, which informs the testing effort that must be expended on the sub system.
- The design for the sub system (module/use case) should be complete in adherence to the architecture and risk profile. Since testability is considered as part of the architecture, and the design is informed by the architecture, the design should be by definition testable.
- Only at this point is the developer now free to follow a TDD approach in implementing the code.
The very positive thing that TDD emphasizes is the need for testing. However, to naively embrace TDD, is to do it at the peril of your project.
- Kent Beck, Test-Driven Development by Example, The Addison-Wesley Signature Series, Addison-Wesley, 2003.
- Cédric Beust and Hani Suleiman, Next Generation Java Testing : TestNG and Advanced Concepts, Addison-Wesley, Upper Saddle River, NJ, 2008.
- Bertrand Meyer, Agile!: The Good, the Hype and the Ugly, Springer, 2014.
I recently treid to compile the DBPedia Extraction Framework. What was not immediately clear to me is whether I have to have Scala installed. It turns out that having Scala installed natively is not necessary, seeing as the
scala-maven-plugin is sufficient.
The steps to compile DBPedia Extraction Framework from the command line are:
- Ensure you have the JDK 1.8.x installed.
- Ensure Maven 3.x is installed.
- mvn package
Steps to compile DBPedia Extraction Framework from the Scala IDE (which can be downloaded from Scala-ide.org) are:
- Ensure you have the JDK 1.8.x installed.
- Ensure you have the Scala IDE installed.
- Import existing Maven project into Scala IDE.
mvn clean installfrom within the IDE.