Clown Fish Optimized – Modified Support Vector Machine (CFO-MSVM) for Software Defect Prediction
Clown Fish Optimized – Modified Support Vector Machine (CFO-MSVM) for Software Defect Prediction |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-9 |
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| Year of Publication : 2024 | ||
| Author : Medhunhashini D. R., KS Jeen Marseline |
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| DOI : 10.14445/22315381/IJETT-V72I9P117 | ||
How to Cite?
Medhunhashini D. R., KS Jeen Marseline, "Clown Fish Optimized – Modified Support Vector Machine (CFO-MSVM) for Software Defect Prediction," International Journal of Engineering Trends and Technology, vol. 72, no. 9, pp. 203-219, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I9P117
Abstract
The growing software industry and the necessity for software development has increased rapidly. The biggest challenge is to develop software in minimal time with fewer resources and bug-free. Software defect prediction has the privilege of predicting the software bug at the earliest to avoid chaos. This paper presents a novel method combining the nature-inspired optimization technique with the Support Vector Machine, proposing a Clown Fish Optimized – Modified Support Vector Machine (CFO-MSVM) classifier for earlier effective classification of the bug. The objective function of the proposed classifier is to tune the hyperparameter of SVM following the swarm intelligence of the clown fish crowd. The Java Developers Toolkit (JDT) dataset from AEEEM repository is used as the bench marker to validate the CFO-MSVM classifier. The classifier is investigated using a grid search for the regularization parameter C, and the number of iterations is set to 100. Precision, Recall and F Score Metrics are used for evaluation. FMI and MCC statistical measurements are employed to define accuracy further. The CFO-MSVM classifier segregates the defect and non-defect modules with 87.32 % accuracy compared to the existent SVM and SVM-GA classifiers, which have 50.83% and 65.00%, respectively.
Keywords
Accuracy, Clown Fish Optimization, Software defects, Support Vector Machine, Tuning parameters
References
[1] Emad Shihab et al., “Is Lines of Code a Good Measure of Effort in Effort-Aware Models?,” Information and Software Technology, vol. 55, no. 11, pp. 1981-1993, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Harold Valdivia-Garcia, Emad Shihab, and Meiyappan Nagappan, “Characterizing and Predicting Blocking Bugs in Open Source Projects,” Journal of Systems and Software, vol. 143, pp. 44-58, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Cheng Zhou et al., “Leveraging Multi-Level Embeddings for Knowledge-Aware Bug Report Reformulation,” Journal of Systems and Software, vol. 198, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] R. Martí et al., “Providing Early Resource Allocation During Emergencies: The Mobile Triage Tag,” Journal of Network and Computer Applications, vol. 32, no. 6, pp. 1167-1182, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Bader Alkhazi et al., “Learning to Rank Developers for Bug Report Assignment,” Applied Soft Computing, vol. 95, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Anjali Goyal, and Neetu Sardana, “Performance Assessment of Bug Fixing Process in Open Source Repositories,” Procedia Computer Science, vol. 167, pp. 2070-2079, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Fiorella Zampetti et al., “Automating Orthogonal Defect Classification Using Machine Learning Algorithms,” Future Generation Computer Systems, vol. 102, pp. 932-947, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Fábio Lopes et al., “An Empirical Characterization of Software Bugs in Open-Source Cyber–Physical Systems,” Journal of Systems and Software, vol. 192, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Thazin Win Win Aung et al., “Multi-Triage: A Multi-Task Learning Framework for Bug Triage,” Journal of Systems and Software, vol. 184, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ashima Kukkar et al., “ProRE: An ACO- based Programmer Recommendation Model to Precisely Manage Software Bugs,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 1, pp. 483-498, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Chengbin Pang et al., “Generation-Based Fuzzing? Don’t Build a New Generator, Reuse!,” Computers & Security, vol. 129, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Luiz Alberto Ferreira Gomes, Ricardo da Silva Torres, and Mario Lúcio Côrtes, “Bug Report Severity Level Prediction in Open Source Software: A Survey and Research Opportunities,” Information and Software Technology, vol. 115, pp. 58-78, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Heetae Cho, Seonah Lee, and Sungwon Kang, “Classifying Issue Reports According to Feature Descriptions in a User Manual Based on a Deep Learning Model,” Information and Software Technology, vol. 142, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Shirin Akbarinasaji, Bora Caglayan, and Ayse Bener, “Predicting Bug-Fixing Time: A Replication Study Using an Open Source Software Project,” Journal of Systems and Software, vol. 136, pp. 173-186, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ashima Kukkar et al., “Bug Severity Classification in Software Using Ant Colony Optimization Based Feature Weighting Technique,” Expert Systems with Applications, vol. 230, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ashima Kukkar, Rajni Mohana, and Yugal Kumar, “Does Bug Report Summarization Help in Enhancing the Accuracy of Bug Severity Classification?,” Procedia Computer Science, vol. 167, pp. 1345-1353, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Thomas Hirsch, and Birgit Hofer, “A Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches,” Journal of Systems and Software, vol. 192, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Liangfu Ge et al., “An Improved System for Long-Term Monitoring of Full-Bridge Traffic Load Distribution on Long-Span Bridges,” Structures, vol. 54, pp. 1076-1089, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Asmita Yadav et al., “Ranking of Software Developers Based on Expertise Score for Bug Triaging,” Information and Software Technology, vol. 112, pp. 1-17, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Yguaratã Cerqueira Cavalcanti et al., “Towards Semi-Automated Assignment of Software Change Requests,” Journal of Systems and Software, vol. 115, pp. 82-101, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ashima Kukkar, and Rajni Mohana, “A Supervised Bug Report Classification with Incorporate and Textual field Knowledge,” Procedia Computer Science, vol. 132, pp. 352-361, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Xin Xia et al., “Elblocker: Predicting Blocking Bugs with Ensemble Imbalance Learning,” Information and Software Technology, vol. 61, pp. 93-106, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[23] D. Jayaraj et al., “AFSORP: Adaptive Fish Swarm Optimization-Based Routing Protocol for Mobility Enabled Wireless Sensor Network,” International Journal of Computer Networks and Applications, vol. 10, no. 1, pp. 119-129, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] R. Vadivel, and Ramkumar Jaganathan, “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications,” Incorporating the Internet of Things in Healthcare Applications and Wearable Devices, pp. 109-121, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Lingaraj Mani, Senthilkumar Arumugam, and Ramkumar Jaganathan, “Performance Enhancement of Wireless Sensor Network Using Feisty Particle Swarm Optimization Protocol,” Proceedings of the 4th International Conference on Information Management & Machine Intelligence, Jaipur, India, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] J. Ramkumar, and R. Vadivel, “Whale Optimization Routing Protocol for Minimizing Energy Consumption in Cognitive Radio Wireless Sensor Network,” International Journal of Computer Networks and Applications, vol. 8, no. 4, pp. 455-464, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[27] J. Ramkumar, and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wireless Personal Communications, vol. 120, no. 2, pp. 887-909, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] S.P. Geetha et al., “Energy Efficient Routing in Quantum Flying Ad Hoc Network (Q-Fanet) Using Mamdani Fuzzy Inference Enhanced Dijkstra’s Algorithm (MFI-EDA),” Journal of Theoretical and Applied Information Technology, vol. 102, no. 9, pp. 3708-3724, 2024.
[Google Scholar] [Publisher Link]
[29] Ramkumar Jaganathan, and Ramasamy Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) for Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” International Journal of Computing and Digital Systems, vol. 10, no. 1, pp. 1063-1074, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[30] M.P. Swapna, and J. Ramkumar, “Multiple Memory Image Instances Stratagem to Detect Fileless Malware,” Advancements in Smart Computing and Information Security, pp. 131-140, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[31] J. Ramkumar et al., “IoT-Based Kalman Filtering and Particle Swarm Optimization for Detecting Skin Lesion,” Soft Computing Applications in Modern Power and Energy Systems, pp. 17-27, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Nitish Kumar Ojha, Archana Pandita, and J. Ramkumar, “Cyber Security Challenges and Dark Side of AI: Review and Current Status,” Demystifying the Dark Side of AI in Business, pp. 117-137, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Khushbakht Ali Qamar, Emre Sülün, and Eray Tüzün, “Taxonomy of Bug Tracking Process Smells: Perceptions of Practitioners and an Empirical Analysis,” Information and Software Technology, vol. 150, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Jyoti Prakash Meher, Sourav Biswas, and Rajib Mall, “Deep Learning-Based Software Bug Classification,” Information and Software Technology, vol. 166, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Behzad Soleimani Neysiani, Seyed Morteza Babamir, and Masayoshi Aritsugi, “Efficient Feature Extraction Model for Validation Performance Improvement of Duplicate Bug Report Detection in Software Bug Triage Systems,” Information and Software Technology, vol. 126, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[36] S.K.B. Sangeetha et al., “An Enhanced Multimodal Fusion Deep Learning Neural Network for Lung Cancer Classification,” Systems and Soft Computing, vol. 6, pp. 1-10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]