Mining Key Classes in Java Projects by Examining a Very Small Number of Classes: A Complex Network-Based Approach

Li, Hao; Pan, Weifeng; Wang, Muchou; Chai, Chunlei; Chen, Pengyu; Wang, Jiale; Jing, Wang

doi:10.1109/access.2021.3058450

Cited by 34 publications

(25 citation statements)

References 18 publications

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“…Zhou et al [19] represented software as a dependency graph and used h-index and its derived metrics to measure the importance of classes. Li et al [20] proposed the MinClass method, which used OSE (first-order structural entropy) to calculate the importance of each class in systems.…”

Section: Related Workmentioning

confidence: 99%

“…Li et al [20] proposed a new metric, OSE and used it to measure the importance of classes. They considered four properties in CCN, including coupling strength, the difference in the weight distribution on the links between class nodes and their neighbours, the number of neighbours of class nodes, and the importance of neighbours of class nodes.…”

Section: ) Osementioning

confidence: 99%

“…In this subsection, in order to compare our COSPA method with other methods, we choose nine methods as our baseline methods: PageRank [16], [17], [24]- [26], h-index [19], aindex, MinClass [20], InDeg (In-Degree) [10], OutDeg (Out-Degree) [10], Coreness [14], Deg (Degree) [10], and ICOOK (Identifying key Class candidates in OO software using generalized K-Core decomposition) [12]. The baseline methods can be briefly described as follows:…”

Section: Baseline Approachesmentioning

confidence: 99%

“…• MinClass: MinClass [20] used OSE (first-order structural entropy) to calculate the significance of each class in systems. Meanwhile, Minclass can be used to mine key classes by examining a small number of classes.…”

Section: Baseline Approachesmentioning

confidence: 99%

“…But we discover that some of these methods proposed so far has reached an upper limit in terms of accuracy when they are used to identify key classes. Although many methods [7], [9], [12], [14], [15], [15]- [20], [22] have been proposed, very few of them combine the advantages of unsupervised learning and supervised learning together. Moreover, due to the diversity and variability of the software, we observe that such a single approach does not perform well in mining key classes under a small set of class candidates.…”

Section: Introductionmentioning

confidence: 99%

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COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation

Pan

Wang

et al. 2021

IEEE Access

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Software comprehension helps us to maintain software better. The developers often use documentation and mutual communication to understand software. However, the incompletion of documentation usually causes some problems such that we cannot acquire the detailed information of software. So researchers are now using key classes as starting points for software understanding and propose several effective methods for key class identification. Such methods are mostly implemented using supervised and unsupervised learning methods; however, they may be subject to class imbalance, insufficient amount of data, and lack of accuracy. In this paper, we propose a novel approach, called COSPA (identifying key Classes in Object-Oriented Software using Preference Aggregation), to identify key classes in software. Our approach is inspired by the social choice theory and uses Kemeny-Young method to find a preference that minimizes the total difference between different pairs of sequences returned by three key class identification methods (i.e., a-index, InDeg, and OSE). Further, we rank the classes in descending order according to that preference set, and a small number of top-ranked classes are considered as key candidates. To better evaluate the performance of our approach, we apply it to six Java software systems and compare it with nine state-ofthe-art approaches. In the experiments, we use Friedman to evaluate all methods in the whole set of research subjects; results show that, though no single method works always best in all subject systems, our method indeed performs better than others.INDEX TERMS Complex network, key classes, divergence, software comprehension.

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Section: Related Workmentioning

confidence: 99%

Section: ) Osementioning

confidence: 99%

Section: Baseline Approachesmentioning

confidence: 99%

Section: Baseline Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation

Pan

Wang

et al. 2021

IEEE Access

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Analyzing the Structural Complexity of Software Systems Using Complex Network Theory

Du¹

2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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Software systems have nearly been used in all walks of life, playing an increasingly important role. Thus, how to understand and measure complex software systems has become an ever-important step to ensure a high-quality software system. The traditional analysis of software system structure focuses on a single module. However, the traditional software structural metrics mainly focus on analyzing the local structure of software systems and fail to characterize the properties of software as a whole. Complex network theory provides us with a new way to understand the internal structure of software systems, and many researchers have introduced the theory of complex networks into the examination of software systems by building software networks from the source code of software systems. In this paper, we combine software structure analysis and complex network theory together and propose a SCANT (Software Complexity Analysis using complex Network Theory) approach to probe the internal complexity of software systems.

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Leveraging sustainable development of agriculture with sustainable water management: The empirical investigation of “Five Water Cohabitation” of Zhejiang Province in China

Tian

Han

Shen

et al. 2022

Environ Monit Assess

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Mining Key Classes in Java Projects by Examining a Very Small Number of Classes: A Complex Network-Based Approach

Cited by 34 publications

References 18 publications

COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation

COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation

Analyzing the Structural Complexity of Software Systems Using Complex Network Theory

Leveraging sustainable development of agriculture with sustainable water management: The empirical investigation of “Five Water Cohabitation” of Zhejiang Province in China

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