Assessment of Software Maintainability Evolution Using C&amp;K Metrics

Barbosa, Nelson; Hirama, Kechi

doi:10.1109/tla.2013.6684398

Cited by 6 publications

(4 citation statements)

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“…is metric is hence not defined on a single version of the software project, but it is tailored to analyze the evolution of the source code. e assumption between the usage of Chidamber and Kemerer DIT, depth of inheritance tree [5,14,26,43,44], [20,21,27,32], [23,28,29,39], [35,47] 15 13 Chidamber and Kemerer LCOM, lack of cohesion in methods [5,14,26,43,44], [20,27,28,32], [23,36,39,46,47] 14 12 Chidamber and Kemerer NOC, number of children [5,14,26,43,44], [20,27,32,39], [23,29,35,47] 13 11 Chidamber and Kemerer RFC, response for class [5,15,19,26,…”

Section: (Iii) Change (Number Of Lines Changed In the Class)mentioning

confidence: 99%

“…WMC, weighted method per class, defined in the same way as McCabe's WMC (weighted method count, described below) but applied to a class, i.e., it gives the complexity of that particular class by [20,23,28,29,35,36,39,47,50] 18 16 Chidamber and Kemerer DIT, depth of inheritance tree [5,14,26,43], S13, [20,21,27,32], [23,28,29,35,39,47] 15 13 Chidamber and Kemerer LCOM, lack of cohesion in methods [5,14,26,43], S13, [20,27,28,32], [23,36,39,46,47] 14 12…”

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confidence: 99%

“…Chidamber and Kemerer NOC, number of children [5,14,26,43], S13, [20,27,32,39] us, this metric gives us an idea on how much interlaced the classes are to each other and hence how much influence the maintenance of a single class has on other ones. RFC, response for class, is defined as the set of methods that can potentially be executed in response to a message received by an object of that class.…”

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A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Ardito

Coppola

Barbato³

et al. 2020

Scientific Programming

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Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.

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Section: (Iii) Change (Number Of Lines Changed In the Class)mentioning

confidence: 99%

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A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Ardito

Coppola

Barbato³

et al. 2020

Scientific Programming

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“…Seguindo as atividades da abordagem de reestruturação proposta, em 2 (Figura 1) com base nos valores das medidas do grupo de reestruturação, são calculados os níveis de acoplamento e de coesão do software analisado, por meio das seguintes fórmulas: = + + ã = Para obter o valor do acoplamento, foi utilizada a soma dos valores das medidas , , pois essas medidas aferem o mesmo atributo sob pontos de vista diferentes, possuem mesmo sentido de crescimento, não possuem valores inversos e apresentam correlação [Barbosa;Hirama, 2013;Lima;Resende, 2014;Silva;Costa 2015]; logo, se o valor de uma medida aumenta ou reduz, mesmo que em escalas diferentes, o valor das demais tende a seguir o mesmo comportamento. Dessa maneira é possível constatar melhoria/piora no estado do sistema pela observação das somas desses valores.…”

Section: Figura 1 -Abordagem De Reestruturação Propostaunclassified

Uma Abordagem Para Reestruturação de Sistemas de Software Orientados a Objetos

Santos¹,

Júnior²,

Costa³

2016

Anais Do XV Simpósio Brasileiro De Qualidade De Software (SBQS 2016)

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A qualidade de software é uma das principais preocupações desde as fases iniciais de desenvolvimento do software. Para isso, o software deve evoluir constantemente para atender as necessidades dos usuários e do ambiente (lei da mudança contínua). Por isso, são realizadas manutenções no software. Mas, se as manutenções forem conduzidas em discordância aos padrões de projeto empregados e às melhores práticas de programação, o software se tornará progressivamente mais acoplado e menos coeso, degradando sua qualidade, pois se torna menos modularizado. Assim, neste artigo, é proposta uma abordagem para reestruturar sistemas de software baseada na movimentação de classes entre pacotes para aprimorar a modularização e a manutenibilidade desses sistemas, melhorando sua qualidade estrutural. A movimentação de classes é determinada por uma heurística de otimização combinatória (Simulated Annealing). O resultado da avaliação indica que essa abordagem aprimora simultaneamente o acoplamento e a coesão do software, movimentando baixo percentual de classes, sem afetar estrutura de pacotes do software.

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