Refactoring Process Models in Large Process Repositories.

With the increasing adoption of process-aware information systems (PAIS), large process model repositories have emerged. Over time respective models have to be re-aligned to the real-world business processes through customization or adaptation. This bears the risk that model redundancies are introduced and complexity is increased. If no continuous investment is made in keeping models simple, changes are becoming increasingly costly and error-prone. Though refactoring techniques are widely used in software engineering to address related problems, this does not yet constitute state-of-the art in business process management. Process designers either have to refactor process models by hand or cannot apply respective techniques at all. This paper proposes a set of behaviour-preserving techniques for refactoring large process repositories. This enables process designers to eectively deal with model complexity by making process models better understandable and easier to maintain

Keeping the Cost of Process Change Low through Refactoring

With the increasing adoption of process-aware information systems (PAIS) large process model repositories have emerged. Over time respective models have to be re-aligned to the real world business processes through customization or adaptation. This bears the risk that model redundancies are introduced and complexity is increased. If no continuous investment is made in keeping models simple, changes are becoming increasingly costly and error-prone. Although refactoring techniques are widely used in software engineering to address related problems, this does not yet constitute state-of-the art in business process management. Consequently, process designers either have to refactor process models by hand or can not apply respective techniques at all. In this paper we propose a set of techniques for refactoring large process repositories, which are behaviour-preserving. The proposed refactorings enable process designers to effectively deal with model complexity by making process models easier to change, less error-prone and better understandable

Evaluation of Kermeta for Solving Graph-based Problems

Kermeta is a meta-language for specifying the structure and behavior of graphs of interconnected objects called models. In this paper,\ud we show that Kermeta is relatively suitable for solving three graph-based\ud problems. First, Kermeta allows the specification of generic model\ud transformations such as refactorings that we apply to different metamodels\ud including Ecore, Java, and Uml. Second, we demonstrate the extensibility\ud of Kermeta to the formal language Alloy using an inter-language model\ud transformation. Kermeta uses Alloy to generate recommendations for\ud completing partially specified models. Third, we show that the Kermeta\ud compiler achieves better execution time and memory performance compared\ud to similar graph-based approaches using a common case study. The\ud three solutions proposed for those graph-based problems and their\ud evaluation with Kermeta according to the criteria of genericity,\ud extensibility, and performance are the main contribution of the paper.\ud Another contribution is the comparison of these solutions with those\ud proposed by other graph-based tools

Generic Model Refactorings

Many modeling languages share some common concepts and principles. For example, Java, MOF, and UML share some aspects of the concepts\ud of classes, methods, attributes, and inheritance. However, model\ud transformations such as refactorings specified for a given language\ud cannot be readily reused for another language because their related\ud metamodels may be structurally different. Our aim is to enable a\ud flexible reuse of model transformations across various metamodels.\ud Thus, in this paper, we present an approach allowing the specification\ud of generic model transformations, in particular refactorings, so\ud that they can be applied to different metamodels. Our approach relies\ud on two mechanisms: (1) an adaptation based mainly on the weaving\ud of aspects; (2) the notion of model typing, an extension of object\ud typing in the model-oriented context. We validated our approach by\ud performing some experiments that consisted of specifying three well\ud known refactorings (Encapsulate Field, Move Method, and Pull Up Method)\ud and applying each of them onto three different metamodels (Java,\ud MOF, and UML)

Refactoring GrPPI:Generic Refactoring for Generic Parallelism in C++

Funding: EU Horizon 2020 project, TeamPlay (https://www.teamplay-xh2020.eu), Grant Number 779882, UK EPSRC Discovery, grant number EP/P020631/1, and Madrid Regional Government, CABAHLA-CM (ConvergenciA Big dAta-Hpc: de Los sensores a las Aplicaciones) Grant Number S2018/TCS-4423.The Generic Reusable Parallel Pattern Interface (GrPPI) is a very useful abstraction over different parallel pattern libraries, allowing the programmer to write generic patterned parallel code that can easily be compiled to different backends such as FastFlow, OpenMP, Intel TBB and C++ threads. However, rewriting legacy code to use GrPPI still involves code transformations that can be highly non-trivial, especially for programmers who are not experts in parallelism. This paper describes software refactorings to semi-automatically introduce instances of GrPPI patterns into sequential C++ code, as well as safety checking static analysis mechanisms which verify that introducing patterns into the code does not introduce concurrency-related bugs such as race conditions. We demonstrate the refactorings and safety-checking mechanisms on four simple benchmark applications, showing that we are able to obtain, with little effort, GrPPI-based parallel versions that accomplish good speedups (comparable to those of manually-produced parallel versions) using different pattern backends.Publisher PDFPeer reviewe

Safe Concurrency Introduction through Slicing

Traditional refactoring is about modifying the structure of existing code without changing its behaviour, but with the aim of making code easier to understand, modify, or reuse. In this paper, we introduce three novel refactorings for retrofitting concurrency to Erlang applications, and demonstrate how the use of program slicing makes the automation of these refactorings possible

A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache