High Precision Cohesion Metric

Abstract: -Metrics have been used to measure many attributes of software. For object oriented software, cohesion indicates the level of binding of the class elements. A class with high cohesion is one of the desirable properties of a good object oriented design. A highly cohesive class is less prone to faults and is easy to develop and maintain. Several object oriented cohesion metrics have been proposed in the literature. These metrics have provided a lot of valuable insight into the mechanism of cohesion and how best to capture it. However they do suffer from certain criticisms. In this paper, we propose a new cohesion metric named as High Precision Cohesion Metric (HPCM). HPCM addresses the drawbacks present in the existing cohesion metrics. We introduce two novel concepts -link strength and average attributes used in a class and apply them to arrive at the proposed metric. The metric is presented using the unified cohesion framework to avoid ambiguity. The metric is validated using theoretical approach suggested in the unified framework for cohesion metrics. Empirical validation is also carried out on this metric using data from open source Java projects. The newly proposed High Precision Cohesion Metric overcomes the shortfalls of the earlier proposed cohesion metrics

Using Clustering Techniques to Guide Refactoring of Object-Oriented Classes

Much of the cost of software development is maintenance. Well structured software tends to be cheaper to maintain than poorly structured software, because it is easier to analyze and modify. The research described in this thesis concentrates on determining how to improve the structure of object-oriented classes, the fundamental unit of organization for object-oriented programs. Some refactoring tools can mechanically restructure object-oriented classes, given the appropriate inputs regarding what attributes and methods belong in the revised classes. We address the research question of determining what belongs in those classes, i.e., determining which methods and attributes most belong together and how those methods and attributes can be organized into classes. Clustering techniques can be useful for grouping entities that belong together; however, doing so requires matching an appropriate algorithm to the domain task and choosing appropriate inputs. This thesis identifies clustering techniques suitable for determining the redistribution of existing attributes and methods among object-oriented classes, and discusses the strengths and weaknesses of these techniques. It then describes experiments using these techniques as the basis for refactoring open source Java classes and the changes in the class quality metrics that resulted. Based on these results and on others reported in the literature, it recommends particular clustering techniques for particular refactoring problems. These clustering techniques have been incorporated into an open source refactoring tool that provides low-cost assistance to programmers maintaining object-oriented classes. Such maintenance can reduce the total cost of software development

Software restructuring: understanding longitudinal architectural changes and refactoring

The complexity of software systems increases as the systems evolve. As the degradation of the system's structure accumulates, maintenance effort and defect-proneness tend to increase. In addition, developers often opt to employ sub-optimal solutions in order to achieve short-time goals, in a phenomenon that has been recently called technical debt. In this context, software restructuring serves as a way to alleviate and/or prevent structural degradation. Restructuring of software is usually performed in either higher or lower levels of granularity, where the first indicates broader changes in the system's structural architecture and the latter indicates refactorings performed to fewer and localised code elements. Although tools to assist architectural changes and refactoring are available, there is still no evidence these approaches are widely adopted by practitioners. Hence, an understanding of how developers perform architectural changes and refactoring in their daily basis and in the context of the software development processes they adopt is necessary. Current software development is iterative and incremental with short cycles of development and release. Thus, tools and processes that enable this development model, such as continuous integration and code review, are widespread among software engineering practitioners. Hence, this thesis investigates how developers perform longitudinal and incremental architectural changes and refactoring during code review through a wide range of empirical studies that consider different moments of the development lifecycle, different approaches, different automated tools and different analysis mechanisms. Finally, the observations and conclusions drawn from these empirical investigations extend the existing knowledge on how developers restructure software systems, in a way that future studies can leverage this knowledge to propose new tools and approaches that better fit developers' working routines and development processes