On the suitability of Yule process to stochastically model some properties of object-oriented systems

Concas, Giulio; Marchesi, Michele; Pinna, Stefania; Serra, Nicola

doi:10.1016/j.physa.2006.02.024

Cited by 21 publications

(24 citation statements)

References 19 publications

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“…These results highlight how the versions are not snapshots of the same Yule process with constant parameters, but seem to be the result of different Yule processes (with different parameters), one for each version. Consequently, the power-law distributions found in Concas et al [13,14] on the last version of the systems analysed seem to be the result of a Yule Process where the value of m and c change at every step -a step corresponding to the time interval between two versions of the system -or, equivalently, a Yule process varying in time. If the same Yule process (with same parameters) were in place from the beginning, each software release would be characterized always by the same parameters, irrespective of the system evolution.…”

Section: Estimating Yule Process Parametersmentioning

confidence: 83%

A modified Yule process to model the evolution of some object-oriented system properties

Turnu

Concas

Marchesi

et al. 2011

Information Sciences

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Section: Estimating Yule Process Parametersmentioning

confidence: 83%

A modified Yule process to model the evolution of some object-oriented system properties

Turnu

Concas

Marchesi

et al. 2011

Information Sciences

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“…As mentioned above, different authors have observed that networks, constructed from software systems, follow scale-free degree distributions [11,6,18,19] and exhibit small-world properties [6,20,21]. Software networks thus reveal common behavior, similar to that observed in other complex networks [4,3].…”

Section: Related Workmentioning

confidence: 84%

Community structure of complex software systems: Analysis and applications

Šubelj

Bajec

2011

Physica A: Statistical Mechanics and its Applications

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a b s t r a c tDue to notable discoveries in the fast evolving field of complex networks, recent research in software engineering has also focused on representing software systems with networks. Previous work has observed that these networks follow scale-free degree distributions and reveal small-world phenomena, while we here explore another property commonly found in different complex networks, i.e. community structure. We adopt class dependency networks, where nodes represent software classes and edges represent dependencies among them, and show that these networks reveal a significant community structure, characterized by similar properties as observed in other complex networks. However, although intuitive and anticipated by different phenomena, identified communities do not exactly correspond to software packages. We empirically confirm our observations on several networks constructed from Java and various third party libraries, and propose different applications of community detection to software engineering.

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“…The key feature of this process is that entities increase their value (here the number of enclosed cells) at each time step with a certain probability in proportion to the value itself (Concas et al, 2006). Physically we may explain the growth by the cell dynamics, which show updraughts that favour neighbouring growing cells but suppress those further away.…”

Section: Discussionmentioning

confidence: 99%

The life cycle of convective‐shower cells under post‐frontal conditions

Weusthoff

Hauf

2008

Quart J Royal Meteoro Soc

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Seventeen days with post-frontal shower precipitation are analysed by means of radar data obtained from the German Weather Service's C-band radar network. The life cycle of clusters -defined here as contiguous rain areas including one or more radar-reflectivity peaks (i.e. convection cells) -is investigated. To allow for the continuous tracking of clusters, sometimes over a time period of more than an hour, a new, specially adapted tracking algorithm has been developed. The life cycle of convective clusters comprises five different stages: genesis; growth (including merging); stagnation; decay (including splitting); and dissolving. The transition likelihoods from a cluster with n maxima to one with m maxima are determined (the case m > n corresponding to growth and m < n to decay). It is found that, predominantly, clusters grow or decay by one cell. Results relating to the temporal evolution of post-frontal showers are presented, and a conceptual growth model is proposed. Although single cells are the most frequent cluster type, the spatial structure of the post-frontal precipitation field is dominated by multi-celled clusters. Their life cycle is essentially affected by cell merging and splitting. Although the transitions of all (about one million) identified clusters have been analysed and quantified, more research is necessary in order to understand the underlying principles of cluster growth.

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On the suitability of Yule process to stochastically model some properties of object-oriented systems

Cited by 21 publications

References 19 publications

A modified Yule process to model the evolution of some object-oriented system properties

A modified Yule process to model the evolution of some object-oriented system properties

Community structure of complex software systems: Analysis and applications

The life cycle of convective‐shower cells under post‐frontal conditions

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