Combining Conceptual and Domain-Based Couplings to Detect Database and Code Dependencies

Gethers, Malcom; Aryani, Amir; Poshyvanyk, Denys

doi:10.1109/scam.2012.27

Cited by 6 publications

(6 citation statements)

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“…Some studies have used the term "semantic" (Poshyvanyk et al 2009;Qusef et al 2011, Bavota et al 2010, 2013b, 2014aKagdi et al 2010;Gethers et al 2012), while others have used the term "conceptual" (Gethers et al 2012) to describe the same concept. Poshyvanyk et al (2009) state that conceptual coupling captures the degree to which the identifiers and comments from different classes relate to each other (Qusef et al 2011, Bavota et al 2010, 2013b, 2014aKagdi et al 2010).…”

Section: Semantic Couplingmentioning

confidence: 99%

“…Poshyvanyk et al (2009) state that conceptual coupling captures the degree to which the identifiers and comments from different classes relate to each other (Qusef et al 2011, Bavota et al 2010, 2013b, 2014aKagdi et al 2010). Gethers et al (2012) add a twist to the definition and state that conceptual coupling captures the extent to which domain concepts/features and software artefacts are related to each other. However, both definitions have things in common.…”

Section: Semantic Couplingmentioning

confidence: 99%

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An empirical study on the interplay between semantic coupling and co-change of software classes

2017

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Software systems continuously evolve to accommodate new features and interoperability relationships between artifacts point to increasingly relevant software change impacts. During maintenance, developers must ensure that related entities are updated to be consistent with these changes. Studies in the static change impact analysis domain have identified that a combination of source code and lexical information outperforms using each one when adopted independently. However, the extraction of lexical information and the measure of how loosely or closely related two software artifacts are, considering the semantic information embedded in their comments and identifiers has been carried out using somewhat complex information retrieval (IR) techniques. The interplay between software semantic and change relationship strengths has also not been extensively studied. This work aims to fill both gaps by comparing the effectiveness of measuring semantic coupling of OO software classes using (i) simple identifier based techniques and (ii) the word corpora of the entire classes in a software system. Afterwards, we empirically investigate the interplay between semantic and change coupling. The empirical results show that: (1) identifier based methods have more computational efficiency but cannot always be used interchangeably with corpora-based methods of computing semantic coupling of classes and (2) there is no correlation between semantic and change coupling. Furthermore we found that (3) there is a directional relationship between the two, as over 70% of the semantic dependencies are also linked by change coupling but not vice versa.

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Section: Semantic Couplingmentioning

confidence: 99%

Section: Semantic Couplingmentioning

confidence: 99%

An empirical study on the interplay between semantic coupling and co-change of software classes

2017

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“…Clustering techniques are commonly used to decompose a software system into small units for easier comprehension. Some studies analyze syntax features or dependencies to cluster the software [46][47][48][49][50], while others rely on the semantic information in the source code for clustering [51][52][53][54].…”

Section: Related Workmentioning

confidence: 99%

Clustering Classes in Packages for Program Comprehension

Sun

Liu

et al. 2017

Scientific Programming

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During software maintenance and evolution, one of the important tasks faced by developers is to understand a system quickly and accurately. With the increasing size and complexity of an evolving system, program comprehension becomes an increasingly difficult activity. Given a target system for comprehension, developers may first focus on the package comprehension. The packages in the system are of different sizes. For small-sized packages in the system, developers can easily comprehend them. However, for large-sized packages, they are difficult to understand. In this article, we focus on understanding these large-sized packages and propose a novel program comprehension approach for large-sized packages, which utilizes the Latent Dirichlet Allocation (LDA) model to cluster large-sized packages. Thus, these large-sized packages are separated as small-sized clusters, which are easier for developers to comprehend. Empirical studies on four real-world software projects demonstrate the effectiveness of our approach. The results show that the effectiveness of our approach is better than Latent Semantic Indexing-(LSI-) and Probabilistic Latent Semantic Analysis-(PLSA-) based clustering approaches. In addition, we find that the topic that labels each cluster is useful for program comprehension.

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“…Program clustering is one of the effective ways for program comprehension. There are two different types of program clustering, one is based on syntactic dependency analysis [39][40][41][42][43], while the other is based on the semantic information analysis [44,45].…”

Section: Related Workmentioning

confidence: 99%

Using Hierarchical Latent Dirichlet Allocation to Construct Feature Tree for Program Comprehension

Sun

Liu

Duan

et al. 2017

Scientific Programming

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Program comprehension is an important task faced by developers during software maintenance. With the increasing complexity of evolving systems, program comprehension becomes more and more difficult. In practice, programmers are accustomed to getting a general view of the features in a software system and then finding the interesting or necessary files to start the understanding process. Given a system, developers may need a general view of the system. The traditional view of a system is shown in a packageclass structure which is difficult to understand, especially for large systems. In this article, we focus on understanding the system in both feature view and file structure view. This article proposes an approach to generate a feature tree based on hierarchical Latent Dirichlet Allocation (hLDA), which includes two hierarchies, the feature hierarchy and file structure hierarchy. The feature hierarchy shows the features from abstract level to detailed level, while the file structure hierarchy shows the classes from whole to part. Empirical results show that the feature tree can produce a view for the features and files, and the clustering of classes in the package in our approach is better (in terms of recall) than the other clustering approach, that is, hierarchical clustering.

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Combining Conceptual and Domain-Based Couplings to Detect Database and Code Dependencies

Cited by 6 publications

References 50 publications

An empirical study on the interplay between semantic coupling and co-change of software classes

An empirical study on the interplay between semantic coupling and co-change of software classes

Clustering Classes in Packages for Program Comprehension

Using Hierarchical Latent Dirichlet Allocation to Construct Feature Tree for Program Comprehension

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