Assessing modularity using co-change clusters

Silva, Luciana Lourdes; Valente, Marco; Maia, Marcelo de Almeida

doi:10.1145/2584469.2577086

Cited by 6 publications

(22 citation statements)

References 16 publications

(23 reference statements)

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“…Artifacts that have changed together appear closer to each other in the layout computed by them. Silva et al [22] considered the co-change graph as a sparse graph and retrieved the co-change clusters of system classes using Chameleon clustering [14]. This direction was further pursued by de Oliviera et al [7] who provided empirical evidence that static dependencies across the modules do not truly capture the future maintenance cost.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, co-change data can be easily mined from software repositories without analyzing the code-base. This motivated Gall, Hajek and Jazayeri [11], and others [7,22] to define coupling and cohesion in terms of co-change relations. Geipel and Schweitzer [12] and the subsequent work [1,6,19] shows that the nature of the interplay between static dependencies and co-changes is not completely understood although reasonably strong correlation exists between the two.…”

Section: Introductionmentioning

confidence: 99%

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A design structure matrix approach for measuring co-change-modularity of software products

Benkoczi

Gaur

Hossain

et al. 2018

Proceedings of the 15th International Conference on Mining Software Repositories

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Several authors have quantified the modularity of software systems in terms of coupling and cohesion metrics. Most of these approaches focus on functional and procedural dependencies in the system. Although highly relevant at the design phase, these static dependencies alone do not account for how a software product evolves over time. Instead, this is also dictated by logical and hidden dependencies between system files. To a large extent, the co-change (co-commit) relation captures these different types of dependencies. In this paper, we define two measures of co-change-modularity of a software product based on a weighted design structure matrix (DSM). The first metric, called the weighted propagation cost, uses matrix exponential to measure how changes to one system file potentially affect the whole product. The second metric, called the weighted clustering cost, uses the output of the first metric to measure the partitionability of the system based on the co-change relation. In addition, we provide a visual representation of how the co-change structure of a system evolves over time. We discuss the theoretical foundation of our work and highlight its advantages over existing methodologies. We apply our approach to GNU Octave and show the findings to be consistent with the available literature on the evolution of Octave. Our analysis is extensible and applicable to a range of scenarios including open source systems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A design structure matrix approach for measuring co-change-modularity of software products

Benkoczi

Gaur

Hossain

et al. 2018

Proceedings of the 15th International Conference on Mining Software Repositories

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“…First, we do not use all changesets when building the graph; and thus we only consider commits explicitly related to at least one issue present in the Issues Tracking System (ISS) of the target systems-since this decision tends to produce clusters that are more semantically related [71]. With regard to the number of issues associated with commits, we found that only 4% of the commits are associated with more than one issue and only 0.7% are associated with more than 2 issues.…”

Section: Extracting Co-change Clustersmentioning

confidence: 99%

“…For the first threshold, we experiment with the value [16] and the value 100-because we grouped entities per issue instead of commit, and this scenario leads to a greater number of associations. For the second threshold, we experiment with the values 1 and 2, also following the recommendations of [16,71]. Finally, for the third threshold, we experiment with the value 0 (according to Silva et al [71]) and with the value 0.5 (a slightly more conservative scenario than the value 0.4 recommended in [16]).…”

Section: Selection Of the Threshold Combinationmentioning

confidence: 99%

“…object-oriented classes or interfaces) as input for building software clusters, and thus they coined the term co-change clusters to label the outcomes of their resulting perspective on software decomposition. Later, Silva et al [71] empirically evaluate the use of coarse-grained co-change clusters as a software representation and found a mismatch between the cluster based decomposition and the modular structure of the target systems (in terms of Java packages) of four open-source systems.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the use of co-change clusters in software comprehension tasks

Oliveira¹

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A minha esposa, Paula, e filhos, Beatriz, Bruno, Gabriela e Vítor, não só pela paciência e compreensão nas intermináveis horas de dedicação e ausência que foram necessárias durante a execução desse trabalho. Mas principalmente pelo incentivo e apoio da minha esposa, sempre me motivando a tentar ir além. E também especialmente aos meus filhos pelo incentivo de poder ser uma inspiração para eles.Ao meu orientador, professor Rodrigo Bonifácio, que demonstrou grande espírito colaborativo, sempre disposto a ajudar mesmo quando os prazos eram exíguos. Além disso, por sua grande inteligência e intuição que foram fonte de inspiração para mim. Ao meu co-orientador, professor Guilherme Ramos, por suas contribuições decisivas, que enriqueceram o resultado final. Ao Márcio Ribeiro, que se dispôs a dedicar seu valioso tempo, capacidade e experiência, às importantes contribuições ao nosso trabalho.Aos demais professores do curso de Mestrado Profissional em Computação Aplicada, que tive a honra de conhecer e deles receber os mais valiosos ensinamentos que foram cruciais não só para o avanço desse trabalho, mas também que serão úteis em outros momentos da minha vida acadêmica e profissional. E aos colegas de mestrado, que, como ninguém, sabem como é difícil conciliar família, trabalho e estudo, e além disso buscar a excelência em tudo o que fazem. À Secretaria de Orçamento Federal (SOF), do Ministério do Planejamento, Orçamento e Gestão, especialmente ao Coordenador-Geral de Tecnologia da Informação, Robson Rung, por me apoiar desde o início, pela confiança em minha capacidade de realizar essa tarefa, e por me dar condições de dedicar tempo a esse trabalho. E também a todos da equipe da SOF que sempre foram solidários e que são motivo de orgulho para mim por poder fazer parte desse grupo de extraordinários profissionais de tão renomada instituição. Espero poder retribuir aplicando o que aprendi e, principalmente, procurando ser um profissional melhor para a instituição e para o Brasil.v Palavras-chave: Desenvolvimento de Software Orientado a features, engenharia reversa, mineração de repositórios de software vi Abstract Feature-oriented software development (FOSD) is a paradigm that can be used, among others, to structure a software system around the feature concept that can represents small functionalities and non-functional requirements. Besides their role in software structure, FOSD enables the activation and deactivation of individual features in a given configuration of the software. This advantage can be useful in scenarios where the variability of the software is required. On the other hand, the adoption of FOSD can be done for an existing software system, thus, becomes necessary to apply some reverse engineering technique to extract features from a legacy code base, and also the mapping between these features and their implementations. This dissertation presents a new approach to aid in the reverse engineering activity, that relates historical data from issue tracking systems and source-code changes. The approach relies ...

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An Empirical Study of the Relation Between Strong Change Coupling and Defects Using History and Social Metrics in the Apache Aries Project

Wiese

Kuroda

Ré

et al. 2015

Open Source Systems: Adoption and Impact

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Part 1: Open Source Software EngineeringInternational audienceChange coupling is an implicit relationship observed when artifacts change together during software evolution. The literature leverages change coupling analysis for several purposes. For example, researchers discovered that change coupling is associated with software defects and reveals relationships between software artifacts that cannot be found by scanning code or documentation. In this paper, we empirically investigate the strongest change couplings from the Apache Aries project to characterize and identify their impact in software development. We used historical and social metrics collected from commits and issue reports to build classification models to identify strong change couplings. Historical metrics were used because change coupling is a phenomenon associated with recurrent co-changes found in the software history. In turn, social metrics were used because developers often interact with each other in issue trackers to accomplish the tasks. Our classification models showed high accuracy, with 70−99 % F-measure and 88−99 % AUC. Using the same set of metrics, we also predicted the number of future defects for the artifacts involved in strong change couplings. More specifically, we were able to predict 45.7 % of defects where these strong change couplings reoccurred in the post-release. These findings suggest that developers and projects managers should detect and monitor strong change couplings, because they can be associated with defects and tend to happen again in the subsequent release

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Assessing modularity using co-change clusters

Cited by 6 publications

References 16 publications

A design structure matrix approach for measuring co-change-modularity of software products

A design structure matrix approach for measuring co-change-modularity of software products

Exploring the use of co-change clusters in software comprehension tasks

An Empirical Study of the Relation Between Strong Change Coupling and Defects Using History and Social Metrics in the Apache Aries Project

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