Michael Lawley

Transformation: The Missing Link of MDA

In this paper we explore the issue of transforming models to models, an essential part of the OMGs Model Driven Architecture (MDA) vision. Drawing from the literature and our experiences implementing a number of transformations using different technologies, we explore the strengths and weaknesses of the different technologies and identify requirements for a transformation language for performing the kind of model-to-model transformations required to realise the MDA vision.

Practical declarative model transformation with tefkat

We present Tefkat, an implementation of a language designed specifically for the transformation of MOF models using patterns and rules. The language adopts a declarative paradigm, wherein users may concern themselves solely with the relations between the models rather than needing to deal explicitly with issues such as order of rule execution and pattern searching/traversal of input models. In this paper, we demonstrate the language using a provided example and highlight a number of language features used in solving the problem, a simple object-to-relational mapping.

Incremental model transformation for the evolution of model-driven systems

Model transformations are an integral part of model-driven development. Incremental updates are a key execution scenario for transformations in model-based systems, and are especially important for the evolution of such systems. This paper presents a strategy for the incremental maintenance of declarative, rule-based transformation executions. The strategy involves recording dependencies of the transformation execution on information from source models and from the transformation definition. Changes to the source models or the transformation itself can then be directly mapped to their effects on transformation execution, allowing changes to target models to be computed efficiently. This particular approach has many benefits. It supports changes to both source models and transformation definitions, it can be applied to incomplete transformation executions, and a priori knowledge of volatility can be used to further increase the efficiency of change propagation.

Symbolic rule-based classification of lung cancer stages from free-text pathology reports

OBJECTIVE To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. DESIGN By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage. MEASUREMENTS The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines. RESULTS Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The systems performance was also comparable to support vector machine classification approaches. CONCLUSION A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.

Model Synchronisation: Definitions for Round-Trip Engineering

In a model-centric software development environment, a multitude of different models are used to describe a software system on different abstraction layers and from different perspectives. Following the MDA vision, model transformation is used to support the gradual refinement from abstract models into more concrete models. However, target models do not stay untouched but may be changed due to maintenance work or evolution of the software. Therefore, in order to preserve a coherent description of the whole system, it is necessary to propagate certain changes to a target model back to the source model. However, as transformations in general are partial and not injective, they cannot be easily reversed to propagate changes. This paper presents a formal definition of round-trip engineering and the semantics of target changes in the context of partial and non-injective transformations.

Model transformation: a declarative, reusable patterns approach

The MOF (Meta Object Facility) query, view and transformation RFP, issued by OMG will result in a key enabling technology for model-driven development of large distributed systems. We have designed a transformation language which will meet the requirements of this RFP, and several others besides. The language is declarative and patterns based. Transformation descriptions are explicitly reusable and modular. Rules that make up such descriptions may be aspect-driven, allowing for transformations to be written to address semantic concepts rather than structural features. This paper introduces the language and its rationale, and shows how it is used to solve s small but non-trivial MDA problem.

A graphical specification of model transformations with triple graph grammars

Models and model transformations are the core concepts of OMG’s MDATM approach. Within this approach, most models are derived from the MOF and have a graph-based nature. In contrast, most of the current model transformations are specified textually. To enable a graphical specification of model transformation rules, this paper proposes to use triple graph grammars as declarative specification formalism. These triple graph grammars can be specified within the FUJABA tool and we argue that these rules can be more easily specified and they become more understandable and maintainable. To show the practicability of our approach, we present how to generate Tefkat rules from triple graph grammar rules, which helps to integrate triple graph grammars with a state of a art model transformation tool and shows the expressiveness of the concept.

Model-based test driven development of the Tefkat model-transformation engine

Tefkat is an implementation of a rule- and pattern-based engine for the transformation of models defined using the Object Management Groups (OMG) Model-Driven Architecture (MDA). The process for the development of the engine included the concurrent development of a unit test suite for the engine. The test suite is constructed as a number of models, whose elements comprise the test cases, and which are passed to a test harness for processing. The paper discusses the difficulties and opportunities encountered in the process, and draws implications for the broader problem of testing in a model-driven environment, and of using models for testing.

Forensic debugging of model transformations

Software bugs occur in model-driven development, just as they do with traditional development techniques. We explore the types of bugs that occur in model transformations and identify debugging approaches that can be applied or adapted to a model-driven context. Investigation shows that the detailed source-to-target traceability available with model transformations enables effective post-hoc, or forensic, debugging. Forensic debugging techniques are introduced for automated bug localisation in model transformations. The methods discussed are grounded with examples using the Eclipse Modeling Framework (EMF) and Tefkat, a declarative model transformation engine.

Towards semantic search and inference in electronic medical records: An approach using concept-­based information retrieval

BACKGROUND This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching. AIM The concept-based approach is intended to overcome specific challenges we identified in searching medical records. METHOD Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology. RESULTS Evaluation on a real-world collection of medical records showed our concept-based approach outperformed a keyword baseline by 25% in Mean Average Precision. CONCLUSION The concept-based approach provides a framework for further development of inference based search systems for dealing with medical data.