Scale-free geometry in OO programs

Potanin, Alex; Noble, James; Frean, Marcus; Biddle, Robert

doi:10.1145/1060710.1060716

Cited by 176 publications

(126 citation statements)

References 8 publications

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“…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%

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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Section: Software As Typicalmentioning

confidence: 99%

The Challenge of Software Complexity

Kevin

Wermelinger

2013

Proceedings of the European Conference on Complex Systems 2012

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“…Both automata exhibit some typical graph properties that we find in real-world software systems. First, software is not made of "Lego blocks" [20]. The topology of both automata varies greatly.…”

Section: Structural Analysismentioning

confidence: 99%

Partition Refinement of Component Interaction Automata: Why Structure Matters More Than Size

Lumpe

Vasa

2010

Electron. Proc. Theor. Comput. Sci.

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Automata-based modeling languages, like Component Interaction Automata, offer an attractive means to capture and analyze the behavioral aspects of interacting components. At the center of these modeling languages we find finite state machines that allow for a fine-grained description how and when specific service requests may interact with other components or the environment. Unfortunately, automata-based approaches suffer from exponential state explosion, a major obstacle to the successful application of these formalisms in modeling real-world scenarios. In order to cope with the complexity of individual specifications we can apply partition refinement, an abstraction technique to alleviate the state explosion problem. But this technique too exhibits exponential time and space complexity and, worse, does not offer any guarantees for success. To better understand as to why partition refinement succeeds in some cases while it fails in others, we conducted an empirical study on the performance of a partition refinement algorithm for Component Interaction Automata specifications. As a result we have identified suitable predictors for the expected effectiveness of partition refinement. It is the structure, not the size, of a specification that weighs heavier on the outcome of partition refinement. In particular, Component Interaction Automata specifications for real-world systems are capable of producing scale-free networks containing structural artifacts that can assist the partition refinement algorithm not only converge earlier, but also yield a significant state space reduction on occasion.

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“…Prior analysis of static class collaboration networks [40,44] suggest an anti-correlation, characterizing some functions as source or sinks. We found no significant correlation between in and outdegree of functions in the disassembled executables (Fig.…”

Section: Correlationsmentioning

confidence: 99%

“…Focusing on the properties of directed graphs, Potanin et al [44] examined the binary heap during execution and took a snapshot of 60 graphs from 35 programs written in Java, Self, C++ and Lisp. They concluded that the distributions of incoming and outgoing object references followed a power law with α in ≈ 2.5 and α out ≈ 3.…”

Section: (F)mentioning

confidence: 99%

On callgraphs and generative mechanisms

Bilar

2007

J Comput Virol

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This paper examines the structural features of callgraphs. The sample consisted of 120 malicious and 280 non-malicious executables. Pareto models were fitted to indegree, outdegree and basic block count distribution, and a statistically significant difference shown for the derived power law exponent. A two-step optimization process involving human designers and code compilers is proposed to account for these structural features of executables.

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Scale-free geometry in OO programs

Abstract: Though conventional OO design suggests programs should be built from many small objects, like Lego bricks, they are instead built from objects that are scale-free, like fractals, and unlike Lego bricks.

Cited by 176 publications

References 8 publications

The Challenge of Software Complexity

The Challenge of Software Complexity

Partition Refinement of Component Interaction Automata: Why Structure Matters More Than Size

On callgraphs and generative mechanisms

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