Identifying Crosscutting Concerns Using Fan-in Analysis

Aspect mining is a reverse engineering process that aims at finding crosscutting concerns in existing systems. This paper proposes an aspect mining approach based on determining methods that are called from many different places, and hence have a high fan-in, which can be seen as a symptom of crosscutting functionality. The approach is semi-automatic, and consists of three steps: metric calculation, method filtering, and call site analysis. Carrying out these steps is an interactive process supported by an Eclipse plug-in called FINT. Fan-in analysis has been applied to three open source Java systems, totaling around 200,000 lines of code. The most interesting concerns identified are discussed in detail, which includes several concerns not previously discussed in the aspect-oriented literature. The results show that a significant number of crosscutting concerns can be recognized using fan-in analysis, and each of the three steps can be supported by tools.Comment: 34+4 pages; Extended version [Marin et al. 2004a

Combined Approach for Identifying Crosscutting Concerns in an Object Oriented System

A concern is an essential guideline and an important principle of software engineering development. It refers to some particular concept, the ability to identify functionalities,or some goal to encapsulate the related parts of a software system. Crosscutting concerns of a program are the concerns that affect or crosscut other concerns. Usually these concerns are very hard to identify and cannot be clearly separated from the rest of the system, as they are mixed with many core concerns from the system leading to code scattering and code tangling. Identifying crosscutting concerns will automatically improve the maintainability, reliability, understandability and the evolution of the software system. Aspect mining is a reverse engineering process that tries to find out crosscutting concerns in an object oriented software system which is already developed. Aspect mining can be done without using Aspect Oriented Software Development(AOSD) paradigm. Our goal is to locate and identify the crosscutting concerns and then to re-factor them into aspects, to obtain a system that can be easily understood, maintained and modified. In our work we have implemented a combined approach for identifying such crosscutting concerns as one approach is not efficient to identify some of the crosscutting concerns. The first technique is the fan-in analysis which is a static approach for identifying scattered codes whereas the second technique is the dynamic analysis approach where execution traces are examined and recurring execution patterns are obtained for identifying the tangled code

A Systematic Aspect-Oriented Refactoring and Testing Strategy, and its Application to JHotDraw

Aspect oriented programming aims at achieving better modularization for a system's crosscutting concerns in order to improve its key quality attributes, such as evolvability and reusability. Consequently, the adoption of aspect-oriented techniques in existing (legacy) software systems is of interest to remediate software aging. The refactoring of existing systems to employ aspect-orientation will be considerably eased by a systematic approach that will ensure a safe and consistent migration. In this paper, we propose a refactoring and testing strategy that supports such an approach and consider issues of behavior conservation and (incremental) integration of the aspect-oriented solution with the original system. The strategy is applied to the JHotDraw open source project and illustrated on a group of selected concerns. Finally, we abstract from the case study and present a number of generic refactorings which contribute to an incremental aspect-oriented refactoring process and associate particular types of crosscutting concerns to the model and features of the employed aspect language. The contributions of this paper are both in the area of supporting migration towards aspect-oriented solutions and supporting the development of aspect languages that are better suited for such migrations.Comment: 25 page

Using Modularity Metrics to assist Move Method Refactoring of Large System

For large software systems, refactoring activities can be a challenging task, since for keeping component complexity under control the overall architecture as well as many details of each component have to be considered. Product metrics are therefore often used to quantify several parameters related to the modularity of a software system. This paper devises an approach for automatically suggesting refactoring opportunities on large software systems. We show that by assessing metrics for all components, move methods refactoring an be suggested in such a way to improve modularity of several components at once, without hindering any other. However, computing metrics for large software systems, comprising thousands of classes or more, can be a time consuming task when performed on a single CPU. For this, we propose a solution that computes metrics by resorting to GPU, hence greatly shortening computation time. Thanks to our approach precise knowledge on several properties of the system can be continuously gathered while the system evolves, hence assisting developers to quickly assess several solutions for reducing modularity issues

Experiences In Migrating An Industrial Application To Aspects

Aspect-Oriented Software Development (AOSD) is a paradigm aiming to solve problems of object-oriented programming (OOP). With normal OOP it’s often unlikely to accomplish fine system modularity due to crosscutting concerns being scattered and tangled throughout the system. AOSD resolves this problem by its capability to crosscut the regular code and as a consequence transfer the crosscutting concerns to a single model called aspect. This thesis describes an experiment on industrial application wherein the effectiveness of aspect-oriented techniques is explained in migration the OOP application into aspects. The experiment goals at first to identify the crosscutting concerns in source code of the industrial application and transform these concerns to a functionally equivalent aspect-oriented version. In addition to presenting experiences gained through the experiment, the thesis aims to provide practical guidance of aspect solutions in a real application

A common framework for aspect mining based on crosscutting concern sorts

The increasing number of aspect mining techniques proposed in literature calls for a methodological way of comparing and combining them in order to assess, and improve on, their quality. This paper addresses this situation by proposing a common framework based on crosscutting concern sorts which allows for consistent assessment, comparison and combination of aspect mining techniques. The framework identifies a set of requirements that ensure homogeneity in formulating the mining goals, presenting the results and assessing their quality. We demonstrate feasibility of the approach by retrofitting an existing aspect mining technique to the framework, and by using it to design and implement two new mining techniques. We apply the three techniques to a known aspect mining benchmark and show how they can be consistently assessed and combined to increase the quality of the results. The techniques and combinations are implemented in FINT, our publicly available free aspect mining tool

Identifying aspects using fan-in analysis

The issues of code scattering and tangling, thus of achieving a better modularity for a system's concerns, are addressed by the paradigm of aspect orientation. Aspect mining is a reverse engineering process that aims at finding crosscutting concerns in existing systems. This paper describes a technique based on determining methods that are called from many different places (and hence have a high 'fan-in') to identify candidate aspects in a number of open-source Java systems. The most interesting aspects identified are discussed in detail, which includes several concerns not previously discussed in the aspect-oriented literature. The results show that a significant number of aspects can be recognized using fan-in analysis, and that the technique is suitable for a high degree of automatio

Applying and Combining Three Different Aspect Mining Techniques

Understanding a software system at source-code level requires understanding the different concerns that it addresses, which in turn requires a way to identify these concerns in the source code. Whereas some concerns are explicitly represented by program entities (like classes, methods and variables) and thus are easy to identify, crosscutting concerns are not captured by a single program entity but are scattered over many program entities and are tangled with the other concerns. Because of their crosscutting nature, such crosscutting concerns are difficult to identify, and reduce the understandability of the system as a whole. In this paper, we report on a combined experiment in which we try to identify crosscutting concerns in the JHotDraw framework automatically. We first apply three independently developed aspect mining techniques to JHotDraw and evaluate and compare their results. Based on this analysis, we present three interesting combinations of these three techniques, and show how these combinations provide a more complete coverage of the detected concerns as compared to the original techniques individually. Our results are a first step towards improving the understandability of a system that contains crosscutting concerns, and can be used as a basis for refactoring the identified crosscutting concerns into aspects.Comment: 28 page

A Hierarchical Clustering Based Approach in Aspect Mining

A Hierarchical Clustering Based Approach in Aspect Mining Clustering is a division of data into groups of similar objects. Aspect mining is a process that tries to identify crosscutting concerns in existing software systems. The goal is to refactor the existing systems to use aspect oriented programming, in order to make them easier to maintain and to evolve. The aim of this paper is to present a new hierarchical clustering based approach in aspect mining. For this purpose we propose HAC algorithm (Hierarchical Agglomerative Clustering in aspect mining). Clustering is used in order to identify crosscutting concerns. We evaluate the obtained results from the aspect mining point of view, based on two quality measures that we have previously introduced and a newly defined one. The proposed approach is compared with other similar existing approaches in aspect mining and two case studies are also reported