Conference Paper2017Cited by 15

BFSp: A feature selection method for bug severity classification

Sadia Sharmin, Farzana Aktar, Amin Ahsan Ali, Muhammad Asif Hossain Khan, Mohammad Shoyaib

2017 IEEE region 10 humanitarian technology conference (R10-HTC)

IEEE, pp. 750–754

DOI: 10.1109/r10-htc.2017.8289066URL

Abstract

Bugs are inevitable in every software system. Resolving these bugs requires proper classification based on their severity. This is because the severe bugs need to be assigned to the developers immediately for fixing so that the vulnerability of software can be reduced. For this reason, several automated bug severity classification methods are already proposed to date that use the terms extracted from bug reports. However, most of the time they lack to identify the desired set of features that enhance the classification results. Besides, the existing works mainly focuses on classifying the bug severity within the different versions of a software. However, identifying the bugs in the newly released software is also crucial. To resolve these issues, we propose a method namely Bug Feature Selection method that mainly uses Pareto Optimality in order to find the most informative features. To evaluate the effectiveness of the proposed approach, we use the bug reports of three open source projects namely Eclipse, Mozilla and GCC. The experimental results show that our technique achieves promising performance compared to other state-of-the-art algorithms.

Keywords

  • Mozilla
  • GCC
  • Pareto optimality
  • Feature selection
  • bug reports
  • eclipses
  • software defects
  • software vulnerabilities

CCDS Authors

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

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Cited By

  1. 1.Ashima Kukkar, Rajni Mohana, Anand Nayyar, Jeamin Kim, Byeong-Gwon Kang, Naveen Chilamkurti. (2019). A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting. Sensors, 19(13), 2964[10.3390/s19132964]
  2. 2.Yegor Bugayenko, Ayomide Bakare, Arina Cheverda, Mirko Farina, Artem Kruglov, Yaroslav Plaksin, Witold Pedrycz, Giancarlo Succi. (2023). Prioritizing tasks in software development: A systematic literature review. PLoS ONE, 18(4), e0283838[10.1371/journal.pone.0283838]
  3. 3.Aladdin Baarah, Ahmad Aloqaily, Zaher Salah, Mannam Zamzeer, Mohammad Sallam. (2019). Machine Learning Approaches for Predicting the Severity Level of Software Bug Reports in Closed Source Projects. International Journal of Advanced Computer Science and Applications, 10(8)[10.14569/ijacsa.2019.0100836]
  4. 4.Ashima Kukkar, Rajni Mohana, Yugal Kumar. (2020). Does bug report summarization help in enhancing the accuracy of bug severity classification?. Procedia Computer Science, 167, 1345–1353[10.1016/j.procs.2020.03.345]
  5. 5.Dipok Chandra Das, Md. Rayhanur Rahman. (2018). Security and Performance Bug Reports Identification with Class-Imbalance Sampling and Feature Selection. , 316–321[10.1109/iciev.2018.8641045]
  6. 6.Puloma Roy, Sadia Sharmin, Amin Ahsan Ali, Mohammad Shoyaib. (2020). Discretization and Feature Selection Based on Bias Corrected Mutual Information Considering High-Order Dependencies. Lecture notes in computer science, 830–842[10.1007/978-3-030-47426-3_64]
  7. 7.Ashima Kukkar, Yugal Kumar, Ashutosh Sharma, Jasminder Kaur Sandhu. (2023). Bug severity classification in software using ant colony optimization based feature weighting technique. Expert Systems with Applications, 230, 120573[10.1016/j.eswa.2023.120573]
  8. 8.Abdalrahman Aburakhia, Mohammad Alshayeb. (2024). A Machine Learning Approach for Classifying the Default Bug Severity Level. Arabian Journal for Science and Engineering, 49(9), 13131–13148[10.1007/s13369-024-09081-8]
  9. 9.Md. Hasan Tarek, Md. Mumtahin Habib Ullah Mazumder, Sadia Sharmin, Md. Shariful Islam, Mohammad Shoyaib, Muhammad Mahbub Alam. (2022). RHC: Cluster based Feature Reduction for Network Intrusion Detections. , 15, 378–384[10.1109/ccnc49033.2022.9700680]
  10. 10.Sayeda Shamma Alia, Md. Nazmul Haque, Sadia Sharmin, Shah Mostafa Khaled, Mohammad Shoyaib. (2018). Bug Severity Classification Based on Class-Membership Information. , 97, 520–525[10.1109/iciev.2018.8641032]
  11. 11.Patrick Kwaku Kudjo, Selasie Aformaley Brown, Solomon Mensah. (2023). Improving software vulnerability classification performance using normalized difference measures. International Journal of Systems Assurance Engineering and Management, 14(3), 1010–1027[10.1007/s13198-023-01911-6]
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  14. 14.Kamthorn Sarawan, Jantima Polpinij, Gamgarn Somprasertsri, Bancha Luaphol. (2025). Analyzing Hybrid Feature Representations for Improved Multiclass Bug Severity Classification. Engineering Technology & Applied Science Research, 15(4), 24561–24569[10.48084/etasr.11090]
  15. 15.Ying Li, Yuhao Lin, Yongchao Zhong, Qiuling Yue, Jinglu Hu, Wenjie Wang, Huiyang Shi, Yuqing Zhang. (2023). Multimodal Software Defect Severity Prediction Based on Sentiment Probability. Lecture notes in computer science, 250–265[10.1007/978-981-99-7032-2_15]
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