Verification and Correction of UML Models

Transcription

1 White paper Verification and Correction of UML Models Modeling is a complex process which is quite hard to do in a structured and controlled way. Many companies provide a set of guidelines for model structure, naming conventions and other modeling rules. Using meta-models to describe these guidelines makes it possible to check whether an UML model follows the guidelines or not. Providing this verification of UML models is only one step on the way to making modeling software an even more valuable and powerful tool. Moreover, by providing correction suggestions and automatic correction of these errors, we try to give the modeler as much help as possible in creating correct UML models. Fredrik Bäckström and Anders Ivarsson December, 2006

2 Verification and Correction of UML Models White paper 2 Introduction Modeling is a powerful method to visualize a system. In Model-Driven Development modeling is used to model and visualize the system before implementing it. Modeling can also be used to visualize different aspects of a system to describe a specific part of the system or to provide an overview of the whole system. A third way of using modeling is to experiment with changes to the system without having to change the system itself. Modeling is often used in software development to save time and money by planning the implementation of a system, which is a way to find errors early in the development process. The earlier an error can be discovered in the software development process, the less time it will take to correct the error [1]. The need to verify models to minimize the risk that errors are introduced in the modeling process is evident. Egyed proposes a method of doing instant consistency checking through scope-based consistency checking for a number of consistency rules to find inconsistencies among a set of UML-models [2]. Ohst et al. presents an approach for working with different versions of UML-models that visualizes two models by unifying these into one model, showing both models and highlighting the differences between them [3]. Many companies provide modeling guidelines that include information about the modeling as a process and guidelines for the produced models to ensure that the modeling process is a well-defined process that does not introduce new errors. The use of guidelines facilitates collaboration in modeling and ensures that models are easy to compare with models from other projects using the same guidelines. Guidelines for the models might include naming conventions, structural restrictions and other modeling constraints [4]. Figure 1 : Meta class representing a UML Actor element When supplying guidelines in a textual form they have to be interpreted by the engineer and it is the responsibility of each engineer to ensure that the produced models conform to the guidelines. New errors are easily introduced in the development process during this step. By converting the modeling guidelines to a meta-model expressed in a generic modeling language (such as UML) the modeling software can ensure that the models produced conform to the meta-model and thus to the guidelines. When expressing the modeling guidelines as a meta-model in UML, the language used is a subset of UML Class Diagrams. Each model element that is allowed in the model has a matching class element in the metamodel, as can be seen in Figure 1 and 2, in which rules and guidelines that applies to this specific model element are expressed. Relations in the meta-model define the allowed relations between model elements, thus defining the structural constraints on the model. Figure 2 : Actor element This paper proposes an error correction feature for UML-models. Errors in the models are found by using the meta-model to verify whether a model conforms to the modeling guidelines or not. The error correction is based on using the meta-model to find correction suggestions that alter the model in a way that makes the model conform to the modeling guidelines.

3 Verification and Correction of UML Models White paper 3 Model Verification The benefit of expressing modeling guidelines as a meta-model is the possibility to perform automatic verification of the model to ensure that the model conforms to the guidelines. Model verification can be performed as batch verification, i.e. validating the whole model and presenting all errors found during verification to the engineer using the system. Moreover, it may be used as a continuous activity that is activated each time the model is altered. The continuous model verification gives the engineer valuable feedback during the modeling process. As soon as an alteration of the model is performed it will be verified against the meta-model to see whether the model still conforms to the guidelines. Figure 3 : List of problems in the model The information about errors in the model during modeling is nonintrusive in the sense that the engineer may still continue modeling even though the model contains errors. The modeling software will indicate where errors occur and present a description of the error to the engineer as seen in Figure 3, but it will not alter what an engineer is allowed to do in any way. Allowing the engineer to continue modeling is essential, since the tool should not interrupt the workflow. In MetaModelAgent verification is done by traversing the model and by analyzing the meta-model. For each model element the verification algorithm analyzes the meta-model and checks if the model element conforms to the modeling guidelines. An error in the model is found when the model element is not allowed in its specific place, if it is not allowed in the model at all, if there are too many or too few model elements of that type according to the guidelines or if an property of the model element does not conform to the guidelines for this model property. The meta-model can specify the severity of each rule either it is a rule and the model must conform to it or it is a recommendation and the model should conform to it. In Figure 4 a meta class describing an Actor element is presented with four constraints. The first two have severity rule and must be followed, the last two are only recommendations. Figure 4 : Actor meta class with constraints The UML model verification is provided by MetaModelAgent that is an extension to IBM Rational Software Architect. MetaModelAgent is developed by Objektfabriken [5]. Model Correction Model verification is a great tool for engineers using a modeling system since it will alert them when performing changes to the model that is not allowed by the modeling guidelines. However, engineers will only receive a warning or indication that they have committed an error. Using only model verification, the engineer will still have to read and

4 Verification and Correction of UML Models White paper 4 understand the modeling guidelines and figure out how to solve the error in the model. Solving an error in the model that does not conform to the guidelines is usually not a hard task for an experienced engineer. However, it can be a time-consuming task that halts the modeling process and interrupts the workflow. During this project we have developed an extension to the model verification functionality in MetaModelAgent. This extension presents a way to provide correction suggestions to errors found in a model when validating it against the guidelines. If the model does not conform to the modeling guidelines the engineer will be presented with a list of errors in the model. For each error, the engineer will also be presented with a list of correction suggestions as in Figure 5, i.e. a list of possible changes to the model that will correct that specific error found during verification. Figure 5 : Correction suggestions In this project we have categorized changes that can be made in a model into four possible actions, namely the following: - Add element - Remove element - Change element - Move element This categorization is used as a first step in finding correction suggestions to an error in the model. The outline of the procedure can be seen in Figure 6. By examining the type of the error (e.g. there is an overflow of a specific type of elements, there is an attribute that have an incorrect value, etc.) the system determines which categories of changes might solve the problem. An error where there are too few elements of a certain type of model elements might be solved by adding an element to the model, while an error where there are too many elements of a certain type of model elements might be solved by removing an element, changing an element to another type of model elements or by moving an model element to another place in the model. The next step in finding correction suggestions for an error in the model is to analyze the model and the meta-model to find correction suggestion within the categories of changes. If the system determines that a change of an element might solve the problem, the system will analyze the model to find which elements it might change and then analyze the metamodel to find out how each of these elements could be changed to solve the problem. This will generate a list of possible correction suggestions that solve the problem. Figure 6 : Model correction procedure Using Model Correction For some of the correction suggestions provided by the system it is possible to let the system automatically commit the suggested changes to the model. This is the case in simple changes, e.g. when removing an

5 Verification and Correction of UML Models White paper 5 element or when moving an element from one place in the model to another place in the model. Atomic actions such as changing a property from true to false can also be committed as well as changes that only require the erroneous value to be substituted by a constant string. The figures below show the procedure for committing an error in the model, getting the error message and then automatically correct the error. Figure 6 : Abstract class Car with property abstract set to true. Figure 7 : Meta class for UML classes. Name must be set and the abstract property must be false. Figure 8 : Problem with class Car, it is abstract. Figure 9 : Suggested change to correct the error. Automatically change the property to false. Figure 10 : Class Car automatically corrected For complex changes it is not always possible to let the system automatically commit the suggested changes. If the model verification finds an incorrect name in a model element for example, the system may be able to give the suggestion to change the name attribute of the model element, but it may not be able to suggest what name should be chosen. This is especially the case for guidelines expressed with regular expressions. When the system cannot automatically commit the suggested change the system will ask the engineer for the correct value. The input is checked so that no incorrect input can be reentered into the model. The functionality to automatically commit correction suggestions is an improvement in the workflow of the modelling process. By allowing the engineer of the system to immediately correct errors that occur during modelling, the system improves both productivity and workflow. When an error is made by the engineer during modelling, the system will alert the engineer about the error and thus ensure that an error does not accumulate into even more errors. Allowing the engineer to immediately correct the error ensures that each error made does not break the workflow and does not make the engineer pause significantly in the

6 Verification and Correction of UML Models White paper 6 modelling process, thus improving productivity. Conclusion The result of this project clearly shows the benefit of meta-modeling, both as a piece of valuable knowledge and in the way it can improve the modeling process and workflow. The automatic correction suggestion functionality that we have developed can be compared with how modern programming environments with automatic error correction suggestions have revolutionized the programming process and how they make the process of programming much easier and more efficient. Using meta-modeling and adding a layer of abstraction enables an engineer to think more about if the specification of the system is correct and not only to be focused on implementation. Meta-modeling can ensure the correctness of models and the functionality for automatic error correction suggestion improves the modeling process.

7 Verification and Correction of UML Models White paper 7 References [1] G. Cernosek and E.Naiburg. The Value of Modeling, IBM Software Group, ftp://ftp.software.ibm.com/software/rational/web/whitepapers/valueofmodeling.pdf accessed on [2] A. Egyed. Instant Consistency Checking for the UML, Teknowledge Corporation, [3] D. Ohst et al. Differences between Versions of UML Diagrams, Universitaet Siegen, [4] MetaModelAgent - Modeling Agent for IBM Rational Rose, Objektfabriken AB, accessed on [5] Objektfabriken AB, accessed on