Analyzing maintainability and reliability of object-oriented software using weighted complex network

Chong, Chun Yong; Lee, Sai Peck

doi:10.1016/j.jss.2015.08.014

Cited by 61 publications

(19 citation statements)

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“…In line (1), we first define and initialize some related variables. Lines (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) describe the process to compute the current node affected by its out-degree neighbor nodes recursively and obtain the FP of the target node.…”

Section: Figure 1 Represents a Portion Of A Weighted Function Executimentioning

confidence: 99%

“…The knowledge of complex network has been introduced into software engineering by using network model to represent the structural characteristics of a software system, and researchers have found many novel features of the structure from different points of view [6,7]. Valerde et al [8] apply complex network to construct the topology structure of software and propose a method to model the software as an undirected network for the first time.…”

Section: Introductionmentioning

confidence: 99%

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Mining the Key Nodes from Software Network Based on Fault Accumulation and Propagation

Huang

Wang

Liu

et al. 2019

Security and Communication Networks

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The increasement of software complexity directly results in the augment of software fault and costs a lot in the process of software development and maintenance. The complex network model is used to study the accumulation and accumulation of faults in complex software as a whole. Then key nodes with high fault probability and powerful fault propagation capability can be found, and the faults can be discovered as soon as possible and the severity of the damage to the system can be reduced effectively. In this paper, the algorithm MFS_AN (mining fault severity of all nodes) is proposed to mine the key nodes from software network. A weighted software network model is built by using functions as nodes, call relationships as edges, and call times as weight. Exploiting recursive method, a fault probability metric FP of a function, is defined according to the fault accumulation characteristic, and a fault propagation capability metric FPC of a function is proposed according to the fault propagation characteristic. Based on the FP and FPC, the fault severity metric FS is put forward to obtain the function nodes with larger fault severity in software network. Experimental results on two real software networks show that the algorithm MFS_AN can discover the key function nodes correctly and effectively.

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Section: Figure 1 Represents a Portion Of A Weighted Function Executimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining the Key Nodes from Software Network Based on Fault Accumulation and Propagation

Huang

Wang

Liu

et al. 2019

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The first four objective functions are the same as presented in the work of Abdeen et al However, the accuracy of these objective functions has been improved. Chong and Lee demonstrated that the different source of CRs contribute different level of coupling strength. Hence, to improve the accuracy of the coupling computation, various dimensions of CRs should be considered.…”

Section: Proposed Approachmentioning

confidence: 99%

MaDHS: Many‐objective discrete harmony search to improve existing package design

Prajapati

Chhabra

2018

Computational Intelligence

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Recently, many computational intelligence algorithms have been proposed to address software remodularization problem. Unfortunately, it has been observed that the performance of optimizers degrades with the optimization problem containing more than three objectives. In this paper, we propose a many‐objective discrete harmony search (MaDHS) to address the software remodularization problem having more than three objectives. The basic idea of MaDHS is that it uses the quality indicator Iϵ + and external archive to rank and store the nondominated solutions. Along with MaDHS, five remodularization objectives, ie, low coupling, high cohesion, low modification degree, quality of class distribution, and low package instability have also been adapted to improve the package structure of existing object‐oriented software systems. To improve the accuracy of modularization solution, the coupling and cohesion objectives are formulated in terms of various dimensions of direct coupling relationships. To test the supremacy of the proposed approach, it is evaluated over eight real‐world object‐oriented software systems. Simulation results show that the proposed approach outperforms the other existing approaches in terms of couplings, cohesion, modularization quality, modularization merit factor, rate per refactoring of achieved improvement, and external developers view.

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“…Most of the published works highlight the fact that software networks exhibit scale-free network properties with a power-law-type node degree distribution (Cai and Yin 2009;Lian Wen et al 2009;Louridas et al 2008) and a clear preferential attachment in the network growth process (Li et al 2013;Chaikalis and Chatzigeorgiou 2015), confirming the heterogeneity and hierarchical characteristics of networks of software. Other key properties in software systems like maintainability and reliability (Chong and Lee 2015), robustness (Gao et al 2014), and modularity (Zanetti and Schweitzer 2012) have been also analysed using the complex network paradigm.…”

Section: Introductionmentioning

confidence: 99%

Evolution of communities of software: using tensor decompositions to compare software ecosystems

2019

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Modern software development is often a collaborative effort involving many authors through the re-use and sharing of code through software libraries. Modern software "ecosystems" are complex socio-technical systems which can be represented as a multilayer dynamic network. Many of these libraries and software packages are open-source and developed in the open on sites such as GitHub, so there is a large amount of data available about these networks. Studying these networks could be of interest to anyone choosing or designing a programming language. In this work, we use tensor factorisation to explore the dynamics of communities of software, and then compare these dynamics between languages on a dataset of approximately 1 million software projects. We hope to be able to inform the debate on software dependencies that has been recently re-ignited by the malicious takeover of the npm package event-stream and other incidents through giving a clearer picture of the structure of software dependency networks, and by exploring how the choices of language designers-for example, in the size of standard libraries, or the standards to which packages are held before admission to a language ecosystem is granted-may have shaped their language ecosystems. We establish that adjusted mutual information is a valid metric by which to assess the number of communities in a tensor decomposition and find that there are striking differences between the communities found across different software ecosystems and that communities do experience large and interpretable changes in activity over time. The differences between the elm and R software ecosystems, which see some communities decline over time, and the more conventional software ecosystems of Python, Java and JavaScript, which do not see many declining communities, are particularly marked.

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Analyzing maintainability and reliability of object-oriented software using weighted complex network

Cited by 61 publications

References 50 publications

Mining the Key Nodes from Software Network Based on Fault Accumulation and Propagation

Mining the Key Nodes from Software Network Based on Fault Accumulation and Propagation

MaDHS: Many‐objective discrete harmony search to improve existing package design

Evolution of communities of software: using tensor decompositions to compare software ecosystems

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