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

On the Value of Quality Attributes for Refactoring Model Transformations Using a Multi-Objective Algorithm

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152454/1/QMOOD_for_ATL__Copy_.pd

Evolution of Model Transformations by Model Refactoring: Long Version

Model-to-model transformations between visual languages are often defined by typed, attributed graph transformation systems. Here, the source and target languages of the model transformation are given by type graphs (or meta models), and the relation between source and target model elements is captured by graph transformation rules. On the other hand, refactoring is a technique to improve the structure of a model in order to make it easier to comprehend, more maintainable and amenable to change. Refactoring can be defined by graph transformation rules, too. In the context of model transformation, problems arise when models of the source language of a model transformation become subject to refactoring. It may well be the case that after the refactoring, the model transformation rules are no longer applicable because the refactoring induced structural changes in the models. In this paper, we consider a graph-transformation-based evolution of model transformations which adapts the model transformation rules to the refactored models. In the main result, we show that under suitable assumptions, the evolution leads to an adapted model transformation which is compatible with refactoring of the source and target models. In a small case study, we apply our techniques to a well-known model transformation from statecharts to Petri nets

Refactoring of Model Transformations

Model-to-model transformations between visual languages are often defined by typed, attributed graph transformation systems. Here, the source and target languages of the model transformation are given by type graphs (or meta models), and the relation between source and target model elements is captured by graph transformation rules. On the other hand, refactoring is a technique to improve the structure of a model in order to make it easier to comprehend, more maintainable and amenable to change. Refactoring can be defined by graph transformation rules, too. In the context of model transformation, problems arise when models of the source language of a model transformation become subject to refactoring. It may well be the case that after the refactoring, the model transformation rules are no longer applicable because the refactoring induced structural changes in the models. In this paper, we consider a graph-transformation-based evolution of model transformations which adapts the model transformation rules to the refactored models. In the main result, we show that under suitable assumptions, the evolution leads to an adapted model transformation which is compatible with refactoring of the source and target models. In a small case study, we apply our techniques to a well-known model transformation from statecharts to Petri nets