COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation
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 researcher...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9514899/ |
| _version_ | 1819137988793204736 |
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| author | Xin Du Tian Wang Weifeng Pan Muchou Wang Bo Jiang Yiming Xiang Chunlai Chai Jiale Wang Chengxiang Yuan |
| author_facet | Xin Du Tian Wang Weifeng Pan Muchou Wang Bo Jiang Yiming Xiang Chunlai Chai Jiale Wang Chengxiang Yuan |
| author_sort | Xin Du |
| collection | DOAJ |
| description | 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 <underline>C</underline>lasses in <underline>O</underline>bject-Oriented <underline>S</underline>oftware using <underline>P</underline>reference <underline>A</underline>ggregation), 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-of-the-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. |
| first_indexed | 2024-12-22T10:59:37Z |
| format | Article |
| id | doaj.art-85a8d8644a6e47cbaaf1e86185daa97b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T10:59:37Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-85a8d8644a6e47cbaaf1e86185daa97b2022-12-21T18:28:33ZengIEEEIEEE Access2169-35362021-01-01911476711478010.1109/ACCESS.2021.31054759514899COSPA: Identifying Key Classes in Object-Oriented Software Using Preference AggregationXin Du0https://orcid.org/0000-0003-0356-623XTian Wang1Weifeng Pan2https://orcid.org/0000-0001-6355-1385Muchou Wang3Bo Jiang4Yiming Xiang5Chunlai Chai6Jiale Wang7Chengxiang Yuan8School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaSchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaSchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaWenzhou University Library, Wenzhou University, Wenzhou, ChinaSchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaSchool of Management and E-Business, Zhejiang Gongshang University, Hangzhou, ChinaSchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaSchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaSchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaSoftware 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 <underline>C</underline>lasses in <underline>O</underline>bject-Oriented <underline>S</underline>oftware using <underline>P</underline>reference <underline>A</underline>ggregation), 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-of-the-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.https://ieeexplore.ieee.org/document/9514899/Complex networkkey classesdivergencesoftware comprehension |
| spellingShingle | Xin Du Tian Wang Weifeng Pan Muchou Wang Bo Jiang Yiming Xiang Chunlai Chai Jiale Wang Chengxiang Yuan COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation IEEE Access Complex network key classes divergence software comprehension |
| title | COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation |
| title_full | COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation |
| title_fullStr | COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation |
| title_full_unstemmed | COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation |
| title_short | COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation |
| title_sort | cospa identifying key classes in object oriented software using preference aggregation |
| topic | Complex network key classes divergence software comprehension |
| url | https://ieeexplore.ieee.org/document/9514899/ |
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