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

Automated Aspect Recommendation through Clustering-Based Fan-in Analysis

Identifying code implementing a crosscutting concern (CCC) automatically can benefit the maintainability and evolvability of the application. Although many approaches have been proposed to identify potential aspects, a lot of manual work is typically required before these candidates can be converted into refactorable aspects. In this paper, we propose a new aspect mining approach, called Clustering-Based Fan-in Analysis (CBFA), to rec-ommend aspect candidates in the form of method clusters, instead of single methods. CBFA uses a new lexical based clustering approach to identify method clusters and rank the clusters using a new ranking metric called cluster fan-in. Experiments on Linux and JHotDraw show that CBFA can provide accurate recommendations while improving aspect mining coverage significantly compared to other state-of-the-art mining approaches. 1

Competitor recognition by managerial perception -- a case in the UK computer industry

This paper presents a practical alternative to the clustering technique in strategic group analysis. Using managerial perception as the input, it studies the competitor recognition in the UK computer industry. An analytical framework and the concept of competitive matrix have been developed. Two hypotheses on perceived competition have also been tested. It is argued that firms formulate their competitive strategies on the basis of perceived competition rather than on the conventionally defined industry. The competitive grouping which combines the concept of strategic groups and market segmentation, provides a useful means to conduct such analysis

Graph Theory and Networks in Biology

In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape

Identifying component modules

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity

Popularity Evolution of Professional Users on Facebook

Popularity in social media is an important objective for professional users (e.g. companies, celebrities, and public figures, etc). A simple yet prominent metric utilized to measure the popularity of a user is the number of fans or followers she succeed to attract to her page. Popularity is influenced by several factors which identifying them is an interesting research topic. This paper aims to understand this phenomenon in social media by exploring the popularity evolution for professional users in Facebook. To this end, we implemented a crawler and monitor the popularity evolution trend of 8k most popular professional users on Facebook over a period of 14 months. The collected dataset includes around 20 million popularity values and 43 million posts. We characterized different popularity evolution patterns by clustering the users temporal number of fans and study them from various perspectives including their categories and level of activities. Our observations show that being active and famous correlate positively with the popularity trend