Power laws in software

Λουρίδας, Πάνος; Spinellis, Diomidis; Vlachos, Vasileios

doi:10.1145/1391984.1391986

Cited by 168 publications

(125 citation statements)

References 58 publications

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“…In fact, the overlaps in the figures indicate that different data sets possess the same CCDF, or witness the persistence of statistical distributions after release change. We chose to use the power-law and the lognormal statistical distribution functions for best fitting of the empirical data,since they have already been demonstrated to be the best candidate for fitting the empirical data for fan-in and fan-out distributions in OO software systems [23], [24], [25]. Due to finite size effects, sometimes a true power-law distribution may not appear as a straight line in a log-log plot for large values of the independent variable as well as for small values, as it should be for an ideal, infinite size sample.…”

Section: Resultsmentioning

confidence: 99%

“…It is already known in the literature [23], that fan-in is power-law distributed across all system's classes. Our studies confirm power-law distributions for fan-in of all system's classes and for the overlapping set of non-refactored classes; they also suggest that the same distribution can still hold for the set of refactored classes, both before and after refactoring, indicating a persistence of such a distribution in the set of classes.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter-based refactoring and the relationship with fan-in/fan-out coupling.

Murgia¹,

Tonelli²,

Concas³

et al. 2012

JOT

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In this paper, we analyze the effect of particular refactorings on class coupling for different aggregate releases of four object-oriented Open Source (OS) Java software systems: Azureus, Jtopen, Jedit and Tomcat, as representative of general Java OS systems. Specifically, the "add parameter" to a method and "remove parameter" from a method refactorings, as defined according to Fowler, may influence class coupling changing fan-in and fan-out of classes they are applied to. We investigate, both qualitatively and quantitatively, using a statistical approach, the global effect of the application of such refactorings, providing best fitting statistical distributions able to describe the changes in fan-in and fan-out couplings. Results show a net tendency of developers to apply such refactorings to classes with relatively high fan-in and fan-out and a persistence of the same statistical distribution for fan-in and fan-out before and after refactoring. Finally, we provide a detailed analysis of the best fitting parameters and of their changes when refactoring occurs, which may be useful for estimating the effect of refactoring on coupling before it is applied. Since refactoring requires time and effort, these estimates may help in determining costs and benefits.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Parameter-based refactoring and the relationship with fan-in/fan-out coupling.

Murgia¹,

Tonelli²,

Concas³

et al. 2012

JOT

View full text Add to dashboard Cite

show abstract

“…If they are constructed as directed graphs, the degree distributions of the inward and outward links differ, with the exponent for incoming edges being less than that of the outgoing and showing a better fit to the power law (Valverde & Solé 2003;Potanin et al 2005;Concas et al 2007;Louridas et al 2008). Solé & Valverde (2004) identify software networks as heterogeneous, scale-free and with some modular structure -a characterisation that also includes a wide range of biological and technical systems.…”

Section: Software As Typicalmentioning

confidence: 99%

“…Software networks demonstrate a wide variation of size, reflecting the range of available software from small tools to major applications, but are generally large in comparison with other networks commonly used in complexity research (Louridas et al 2008;Moore 2011;Newman n.d.) …”

Section: Software As Atypicalmentioning

confidence: 99%

The Challenge of Software Complexity

Kevin

Wermelinger

2013

Proceedings of the European Conference on Complex Systems 2012

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Given the interdisciplinary nature of complex network studies, there is a practical need for dialogue between theorists proposing graph measurements and those seeking to apply them into a domain. We consider this in the domain of software complexity by highlighting the distinctive nature of networks representing software's internal structure and also by describing the application of one such proposal, the offdiagonal complexity, against two examples of software. The results showed the promise of using complex networks to measure software complexity but also demonstrated the confounding effects of size. Based on that application we make proposals to improve the dialogue between theory and experiment. Software's importance and the need to measure complexityToday's society is heavily dependant on software. It runs our computers, our phones and the internet, while managing economies and communications. This pervasiveness means that any improvement in understanding software has a potentially enormous payback from better project management, control of costs and increased quality. Past practice of software development could be seen as a chimera of art-form and engineering with success or failure in projects seemingly dependent on anecdotal wisdoms. While a comprehensive theoretical framework seems elusive, current and future practice has become increasingly evidence-based and draws from a wide range of disciplines such as psychology, sociology, data-mining and complexity theories.That software is complex is also largely self-evident. Brooks (1987) (of "The Mythical Man-Month" fame) argues that complexity is one of the fundamental essences associated with software. As such, understanding this inherent property would make great inroads into understanding software overall.While there are several viewpoints into software such as its cognitive, computational, problem or solution complexity (Cardoso et al. 2000), this paper focuses on the structural complexity of the code, arguing that it provides the most direct understanding of the product. Software as a complex networkThe variety of coding languages, styles and paradigms makes processing and quantifying code hard to generalise. One solution is to abstract the code into a network graph, with vertices representing a chosen unit of code and edges representing an arbitrary relationship between those units. By representing the interconnections between collaborating modules, objects, classes, methods, and subroutines with a network graph, software becomes another domain capable of investigation with the interdisciplinary toolset of complex networks.The basic technique is well established (Myers 2003;Valverde & Solé 2003) and while more recent developments have for instance considered graphing the entire socio-technical system (Bird et al. 2009), obtaining a measurement that represents the complexity of source code's basic structure and that can be connected to software development practice remains desirable.

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“…Software systems and especially the structure of object-oriented ones have also been the focus of research efforts, seeking to identify properties such as scalefreeness and small-world phenomena, based on individual snapshots of the corresponding networks [3], [9]. However, given that most software systems evolve over a number of versions, interesting phenomena arise and are worth of investigating by observing how fundamental network properties vary with time.…”

Section: Introductionmentioning

confidence: 99%