Resolving inconsistencies in software models is a complex task because the number of repairs grows exponentially. Existing approaches thus emphasize on selected repairs only but doing so diminishes their usefulness. This paper copes with the large number of repairs by focusing on what caused an inconsistency and presenting repairs as a linearly growing repair tree. The cause is computed by examining the runtime evaluation of the inconsistency to understand where and why it failed. The individual changes that make up repairs are then modeled in a repair tree as alternatives and sequences reflecting the syntactic structure of the inconsistent design rule. The approach is automated and tool supported. Its scalability was empirically evaluated on 29 UML models and 18 OCL design rules where we show that the approach computes repair trees in milliseconds on average. We believe that the approach is applicable to arbitrary modeling and constraint languages.

When a software engineer finds an inconsistency in a model, then the first question is why? What caused it? Obviously, there must be an error. But where could it be? Or is the design rule erroneous and if yes then which part? The cause of an inconsistency identifies the part of the model or design rule where the error must be. We believe that the visualization of an inconsistency ought to visualize the cause. Understanding the cause is of vital importance before a repair can even be formulated. Indeed, any automation (e.g., code generation, refactoring) has to be considered with caution if it involves model elements that cause inconsistencies. This paper analyzes the basic structure of inconsistent design rules as well as their behavior during validation and presents an algorithm for computing its cause. The approach is fully automated, tool supported, and was evaluated on 14,111 inconsistencies across 29 design models. We found that our approach computes correct causes for inconsistencies, these causes are nearly always a subset of the model elements investigated by the design rules' validation (a naive cause computation approximation), and the computation is very fast (99.8 percent of the causes are computable in <100 ms).

Software design models are routinely adapted to domains, companies, and applications. This requires customizable consistency checkers that allow engineers to dynamically adapt model constraints. To benefit from quick design feedback, such consistency checkers should evaluate the consistency of such changeable constraints incrementally with design changes. This paper presents such a freely customizable, incremental consistency checker. We demonstrate that constraints can be defined and redefined at will. And we demonstrate that its performance is instant for many kinds of constraints without manual annotations or restrictions on the constraint language used. Our approach supports both model and meta-model constraints and was evaluated on over 20 software models and 24 types of constraints. It is fully automated and integrated into the IBM Rational Software Modeler tool.

Advances in consistency checking in model-based software development made it possible to detect errors in real-time. However, existing approaches assume that changes come in small quantities and design rules are generally small in scope. Yet activities such as model transformation, re-factoring, model merging, or repairs may cause larger model changes and hence cause performance problems during consistency checking. The goal of this work is to increase the performance of re-validating design rules. This work proposes an automated and tool supported approach that re-validates the affected parts of a design rule only. It was empirical evaluated on 19 design rules and 30 small to large design models and the evaluation shows that the approach improves the computational cost of consistency checking with the gains increasing with the size and complexity of design rules.

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