Djamel Eddine Khelladi

Detecting Complex Changes During Metamodel Evolution

Evolution of metamodels can be represented at the finest grain by the trace of atomic changes: add, delete, and update elements. For many applications, like automatic correction of models when the metamodel evolves, a higher grained trace must be inferred, composed of complex changes, each one aggregating several atomic changes. Complex change detection is a challenging task since multiple sequences of atomic changes may define a single user intention and complex changes may overlap over the atomic change trace. In this paper, we propose a detection engine of complex changes that simultaneously addresses these two challenges of variability and overlap. We introduce three ranking heuristics to help users to decide which overlapping complex changes are likely to be correct. We describe an evaluation of our approach that allow reaching full recall. The precision is improved by our heuristics from 63% and 71% up to 91% and 100% in some cases.

Detecting complex changes and refactorings during (Meta)model evolution

Evolution of metamodels can be represented at the finest grain by the trace of atomic changes such as add, delete, and update of elements. For many applications, like automatic correction of models when the metamodel evolves, a higher grained trace must be inferred, composed of complex changes, each one aggregating several atomic changes. Complex change detection is a challenging task since multiple sequences of atomic changes may define a single user intention and complex changes may overlap over the atomic change trace.In this paper, we propose a detection engine of complex changes that simultaneously addresses these two challenges of variability and overlap. We introduce three ranking heuristics to help users to decide which overlapping complex changes are likely to be correct. In our approach, we record the trace of atomic changes rather than computing them with the difference between the original and evolved metamodel. Thus, we have a complete and an ordered sequence of atomic changes without hidden changes. Furthermore, we consider the issue of undo operations (i.e. change canceling actions) while recording the sequence of atomic changes, and we illustrate how we cope with it. We validate our approach on 8 real case studies demonstrating its feasibility and its applicability. We observe that a full recall is always reached in all case studies and an average precision of 70.75%. The precision is improved by the heuristics up to 91% and 100% in some cases. HighlightsRecording the trace of atomic changes ensure the chronological order and the absence of hidden changes.Both issues of hidden changes and overlapping complex changes reduce the recall of the detection, if not considered.Our detection algorithm reaches 100% recall for the eight case studies.In the eight case studies, our heuristics never rank the correct overlapping complex changes with a lower priority than the incorrect ones.

Approaches to Co-Evolution of Metamodels and Models: A Survey

Modeling languages, just as all software artifacts, evolve. This poses the risk that legacy models of a company get lost, when they become incompatible with the new language version. To address this risk, a multitude of approaches for metamodel-model co-evolution were proposed in the last 10 years. However, the high number of solutions makes it difficult for practitioners to choose an appropriate approach. In this paper, we present a survey on 31 approaches to support metamodel-model co-evolution. We introduce a taxonomy of solution techniques and classify the existing approaches. To support researchers, we discuss the state of the art, in order to better identify open issues. Furthermore, we use the results to provide a decision support for practitioners, who aim to adopt solutions from research.

Metamodel and Constraints Co-evolution: A Semi Automatic Maintenance of OCL Constraints

Metamodels are core components of modeling languages to define structural aspects of a business domain. As a complement, OCL constraints are used to specify detailed aspects of the business domain, e.g. more than 750 constraints come with the UML metamodel. As the metamodel evolves, its OCL constraints may need to be co-evolved too. Our systematic analysis shows that semantically different resolutions can be applied depending not only on the metamodel changes, but also on the user intent and on the structure of the impacted constraints. In this paper, we investigate the reasons that lead to apply different resolutions. We then propose a co-evolution approach that offers alternative resolutions while allowing the user to choose the best applicable one. We evaluated our approach on the evolution of the UML case study. The results confirm the need of alternative resolutions along with user decision to cope with real co-evolution scenarios. The results show that our approach reaches 80i?ź% of semantically correct co-evolution.

AD-ROOM: a tool for automatic detection of refactorings in object-oriented models

Detecting refactorings in Object-Oriented Models (OOM) is essential to automate the repair, maintenance, and migration of OOM-related products. However, detecting refactorings is challenging since multiple sequences of atomic changes may define a single user intention and refactorings may overlap over the atomic change trace. In this paper, we present AD-ROOM an Eclipse-based tool to automatically detect refactorings during evolution of OOM. In contrast to existing tools, AD-ROOM is designed to reach 100% recall that is confirmed in our eight case studies. We allow user confirmation and we support the user with three heuristics that help to improve the precision of AD-ROOM. See the demonstration video: https://youtu.be/4OJ8zHtfnq8

On Lightweight Metamodel Extension to Support Modeling Tools Agility

Modeling in real industrial projects implies dealing with different models , metamodels and supporting tools. They continuously have to be adapted to changing requirements, involving (often costly) problems in terms of traceability, coherence or interoperability. To this intent, solutions ensuring a better adaptability and flexibility of modeling tools are needed. As metamodels are cornerstones in such tools, metamodel extension capabilities are fundamental. However , current modeling frameworks are not flexible or dynamic enough. Thus, following the ongoing OMG MOF Extension Facility (MEF) RFP, this paper proposes a generic lightweight metamodel extension mechanism developed as part of the MoNoGe collaborative project. A base list of metamodel extension operators as well as a DSL for easily using them are introduced. Two different implementations of this extension mechanism (including a model-level support when (un)applying metamodel extensions) are also described, respectively based on Eclipse/EMF and the Modelio modeling environment.

Surveying the Corpus of Model Resolution Strategies for Metamodel Evolution

Modeling languages evolve regularly. Companies need to maintain all those models that are used in running projects, which can cause these projects to fall back in their schedules. Since 10 years research addresses this issue with approaches for automating co-evolution. The dominant core of these approaches are model resolution strategies. They define 1) how models have to be changed in reaction to specific metamodel changes, 2) what degree of automation can be reached, and 3) to what extent the user can control the resolution outcome. In this paper, we survey existing co-evolution approaches and analyze model resolution strategies. We present a corpus of more than 200 resolution strategies for 116 types of metamodel changes and discuss degree of automation and choices that users have today.

Supporting the co-adaption of process properties

Process verification has become an essential activity to correct and to remove errors before process execution. Typical process verification ecosystems propose to express properties to be verified on the process. When a process is adapted, the existing properties must naturally be re-checked to ensure that no errors have been introduced. However, the properties may become outdated and must be co-adapted w.r.t. the adapted process before to be re-checked. Otherwise, the verification may raise false alarms or may not detect newly introduced errors. In this paper, we propose a co-adaptation approach for control-flow process properties. We systematically studiedcontrol-flow process changes to identify those that do impact properties, and for which we propose resolution strategies. Our preliminary evaluation shows that our resolutions strategies allow to support users in correctly co-adapting impacted properties.

A framework to formally verify conformance of a software process to a software method

The increasing complexity of development projects requires methodological frameworks to support development processes. A method comes with a set of best practices that are enforced and instantiated into processes to drive the realization steps of the development project. However, those best practices come in the form of text in guides and books, or they are in the developers mind. Thus, during an instantiation of a method, there is no guaranty to enforce its best practices into the process, which could impact negatively the criteria: cost, time, and quality. To cope with this issue, we propose a library of best practices to be checked for four popular methods: Unified Process, Extreme Programming, Scrum and Kanban. On top of this library we have built up a template-based constraint language and implemented it to express additional constraints on processes that are modeled with UML Activity Diagrams (AD). To apply formal verification, we leverage on a formalization based on fUML semantics. The evaluation showed the feasibility of our approach which covers all the aspects of the process, i.e. time, resource, control-flow, and data-flow.

Towards a User-Guided Difference-Based Detection of Atomic Changes

Detecting metamodel atomic changes during evolution is prerequisite for co-evolution of models, constraints, and transformations. They are also essential to detect complex changes over the sequence of atomic ones. However when detecting atomic changes with a difference-based technique, the applied order of the atomic changes is not recovered and some hidden changes are undetected. Thus, the quality of the detected atomic change trace is reduced which could be harmful to both co-evolution and detection of complex changes. This paper proposes to identify potential hidden changes in order to add them to the trace of atomic changes, and also to order the atomic changes with ordering heuristics.