Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking
Software specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approaches are inaccurate when dealing with large-s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/1/28 |
| _version_ | 1827369965156564992 |
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| author | Yiming Fan Meng Wang |
| author_facet | Yiming Fan Meng Wang |
| author_sort | Yiming Fan |
| collection | DOAJ |
| description | Software specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approaches are inaccurate when dealing with large-scale systems. In order to improve the accuracy of mined specifications, we propose a specification mining approach based on the ordering points to identify the clustering structure clustering algorithm and model checking. In the approach, the neural network model is first used to produce the feature values of states in the traces of the program. Then, according to the feature values, finite-state automatons are generated based on the ordering points to identify the clustering structure clustering algorithm. Further, the finite-state automaton with the highest F-measure is selected. To improve the quality of the finite-state automatons, we refine it based on model checking. The proposed approach was implemented in a tool named MCLSM and experiments, including 13 target classes, were conducted to evaluate its effectiveness. The experimental results show that the average F-measure of finite-state automatons generated by our method reaches 92.19%, which is higher than most related tools. |
| first_indexed | 2024-03-08T10:00:23Z |
| format | Article |
| id | doaj.art-f8805aafe90e44ed890d3dee932fef77 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-08T10:00:23Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-f8805aafe90e44ed890d3dee932fef772024-01-29T13:41:29ZengMDPI AGAlgorithms1999-48932024-01-011712810.3390/a17010028Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model CheckingYiming Fan0Meng Wang1Cyberspace Security and Computer College, Hebei University, Baoding 071000, ChinaCyberspace Security and Computer College, Hebei University, Baoding 071000, ChinaSoftware specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approaches are inaccurate when dealing with large-scale systems. In order to improve the accuracy of mined specifications, we propose a specification mining approach based on the ordering points to identify the clustering structure clustering algorithm and model checking. In the approach, the neural network model is first used to produce the feature values of states in the traces of the program. Then, according to the feature values, finite-state automatons are generated based on the ordering points to identify the clustering structure clustering algorithm. Further, the finite-state automaton with the highest F-measure is selected. To improve the quality of the finite-state automatons, we refine it based on model checking. The proposed approach was implemented in a tool named MCLSM and experiments, including 13 target classes, were conducted to evaluate its effectiveness. The experimental results show that the average F-measure of finite-state automatons generated by our method reaches 92.19%, which is higher than most related tools.https://www.mdpi.com/1999-4893/17/1/28softwarespecification miningmodel checkingOPTICS clustering algorithmFSAformalization |
| spellingShingle | Yiming Fan Meng Wang Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking Algorithms software specification mining model checking OPTICS clustering algorithm FSA formalization |
| title | Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking |
| title_full | Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking |
| title_fullStr | Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking |
| title_full_unstemmed | Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking |
| title_short | Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking |
| title_sort | specification mining based on the ordering points to identify the clustering structure clustering algorithm and model checking |
| topic | software specification mining model checking OPTICS clustering algorithm FSA formalization |
| url | https://www.mdpi.com/1999-4893/17/1/28 |
| work_keys_str_mv | AT yimingfan specificationminingbasedontheorderingpointstoidentifytheclusteringstructureclusteringalgorithmandmodelchecking AT mengwang specificationminingbasedontheorderingpointstoidentifytheclusteringstructureclusteringalgorithmandmodelchecking |