BFSp: A feature selection method for bug severity classification

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

doi:10.1109/r10-htc.2017.8289066

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.2017.8289066 URL

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

Feature selectionComputer scienceSelection (genetic algorithm)Artificial intelligencePattern recognition (psychology)Feature (linguistics)Machine learning

References

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2.M.F. Porter. (1980). An algorithm for suffix stripping. Data Technologies and Applications, 14(3), 130–137[10.1108/eb046814]
3.R. Battiti. (1994). Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 5(4), 537–550[10.1109/72.298224]
4.Cyril Goutte, Eric Gaussier. (2005). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. Lecture notes in computer science, 345–359[10.1007/978-3-540-31865-1_25]
5.Gavin Brown, Adam Pocock, Mingjie Zhao, Mikel Luján. (2012). Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. Research Explorer (The University of Manchester), 13(1), 27–66[link]
6.Mohamed Bennasar, Yulia Hicks, Rossitza Setchi. (2015). Feature selection using Joint Mutual Information Maximisation. Expert Systems with Applications, 42(22), 8520–8532[10.1016/j.eswa.2015.07.007]
7.David D. Lewis. (1992). Feature selection and feature extraction for text categorization. , 212[10.3115/1075527.1075574]
8.L. Erlikh. (2000). Leveraging legacy system dollars for e-business. IT Professional, 2(3), 17–23[10.1109/6294.846201]
9.Stefano Panzeri, Alessandro Treves. (1996). Analytical estimates of limited sampling biases in different information measures. Network Computation in Neural Systems, 7(1), 87–107[10.1080/0954898x.1996.11978656]
10.Ahmed Lamkanfi, Serge Demeyer, Emanuel Giger, Bart Goethals. (2010). Predicting the severity of a reported bug. , 1–10[10.1109/msr.2010.5463284]
11.R. Moddemeijer. (1989). On estimation of entropy and mutual information of continuous distributions. Signal Processing, 16(3), 233–248[10.1016/0165-1684(89)90132-1]
12.Ahmed Lamkanfi, Serge Demeyer, Quinten David Soetens, Tim Verdonck. (2011). Comparing Mining Algorithms for Predicting the Severity of a Reported Bug. , 249–258[10.1109/csmr.2011.31]
13.Yuan Tian, David Lo, Chengnian Sun. (2012). Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction. , 215–224[10.1109/wcre.2012.31]
14.Tao Zhang, Jiachi Chen, Geunseok Yang, Byungjeong Lee, Xiapu Luo. (2016). Towards more accurate severity prediction and fixer recommendation of software bugs. Journal of Systems and Software, 117, 166–184[10.1016/j.jss.2016.02.034]
15.Geunseok Yang, Tao Zhang, Byungjeong Lee. (2014). Towards Semi-automatic Bug Triage and Severity Prediction Based on Topic Model and Multi-feature of Bug Reports. , 97–106[10.1109/compsac.2014.16]
16.Xin Xia, David Lo, Xinyu Wang, Bo Zhou. (2015). Dual analysis for recommending developers to resolve bugs. Journal of Software Evolution and Process, 27(3), 195–220[10.1002/smr.1706]
17.K. K. Chaturvedi, V. B. Singh. (2012). Determining Bug severity using machine learning techniques. , 1–6[10.1109/conseg.2012.6349519]
18.Gitika Sharma, Sumit Sharma, Shruti Gujral. (2015). A Novel Way of Assessing Software Bug Severity Using Dictionary of Critical Terms. Procedia Computer Science, 70, 632–639[10.1016/j.procs.2015.10.059]
19.Tofigh Naghibi, Sarah Hoffmann, Beat Pfister. (2014). A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 1529–1541[10.1109/tpami.2014.2372791]
20.Nivir Kanti-Singha Roy, Bruno Rossi. (2014). Towards an Improvement of Bug Severity Classification. , 269–276[10.1109/seaa.2014.51]
21.Shruti Gujral, Gitika Sharma, Sumit Sharma, Diksha. (2015). Classifying bug severity using dictionary based approach. [10.1109/ablaze.2015.7154933]
22.Sadia Sharmin, Amin Ahsan Ali, Muhammad Asif Hossain Khan, Mohammad Shoyaib. (2017). Feature Selection and Discretization based on Mutual Information. , 20, 1–6[10.1109/icivpr.2017.7890885]
23.Ahmed Fawzi Otoom, Doaa Al-Shdaifat, Maen Hammad, Emad E. Abdallah. (2016). Severity prediction of software bugs. , 92–95[10.1109/iacs.2016.7476092]
24.Naresh Kumar Nagwani, Shrish Verma. (2014). A Comparative Study of Bug Classification Algorithms. International Journal of Software Engineering and Knowledge Engineering, 24(01), 111–138[10.1142/s0218194014500053]
25.S. A. Fattah, Chia-Chun Lin, Sun-Yuan Kung. (2011). A mutual information based approach for evaluating the quality of clustering. , 22, 601–604[10.1109/icassp.2011.5946475]

Cited By

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.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.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.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.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.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.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.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.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.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.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]
12.Md. Hasan Tarek, Md. Eusha Kadir, Sadia Sharmin, Abu Ashfaqur Sajib, Amin Ahsan Ali, Mohammad Shoyaib. (2021). Feature Subset Selection based on Redundancy Maximized Clusters. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 521–526[10.1109/icmla52953.2021.00087]
13.Tariq Saeed Mian, Abdullah Alsaeedi. (2024). Software Bug Severity Prediction Using Convolutional Neural Network and BiLSTM Models. Lecture notes on data engineering and communications technologies, 1–12[10.1007/978-3-031-59707-7_1]
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.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]

← All publications

Abstract

References

1.Hanchuan Peng, Fuhui Long, C. Ding. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238[10.1109/tpami.2005.159]

2.M.F. Porter. (1980). An algorithm for suffix stripping. Data Technologies and Applications, 14(3), 130–137[10.1108/eb046814]

3.R. Battiti. (1994). Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 5(4), 537–550[10.1109/72.298224]

4.Cyril Goutte, Eric Gaussier. (2005). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. Lecture notes in computer science, 345–359[10.1007/978-3-540-31865-1_25]

5.Gavin Brown, Adam Pocock, Mingjie Zhao, Mikel Luján. (2012). Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. Research Explorer (The University of Manchester), 13(1), 27–66[link]

6.Mohamed Bennasar, Yulia Hicks, Rossitza Setchi. (2015). Feature selection using Joint Mutual Information Maximisation. Expert Systems with Applications, 42(22), 8520–8532[10.1016/j.eswa.2015.07.007]

7.David D. Lewis. (1992). Feature selection and feature extraction for text categorization. , 212[10.3115/1075527.1075574]

8.L. Erlikh. (2000). Leveraging legacy system dollars for e-business. IT Professional, 2(3), 17–23[10.1109/6294.846201]

9.Stefano Panzeri, Alessandro Treves. (1996). Analytical estimates of limited sampling biases in different information measures. Network Computation in Neural Systems, 7(1), 87–107[10.1080/0954898x.1996.11978656]

10.Ahmed Lamkanfi, Serge Demeyer, Emanuel Giger, Bart Goethals. (2010). Predicting the severity of a reported bug. , 1–10[10.1109/msr.2010.5463284]

11.R. Moddemeijer. (1989). On estimation of entropy and mutual information of continuous distributions. Signal Processing, 16(3), 233–248[10.1016/0165-1684(89)90132-1]

12.Ahmed Lamkanfi, Serge Demeyer, Quinten David Soetens, Tim Verdonck. (2011). Comparing Mining Algorithms for Predicting the Severity of a Reported Bug. , 249–258[10.1109/csmr.2011.31]

13.Yuan Tian, David Lo, Chengnian Sun. (2012). Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction. , 215–224[10.1109/wcre.2012.31]

14.Tao Zhang, Jiachi Chen, Geunseok Yang, Byungjeong Lee, Xiapu Luo. (2016). Towards more accurate severity prediction and fixer recommendation of software bugs. Journal of Systems and Software, 117, 166–184[10.1016/j.jss.2016.02.034]

15.Geunseok Yang, Tao Zhang, Byungjeong Lee. (2014). Towards Semi-automatic Bug Triage and Severity Prediction Based on Topic Model and Multi-feature of Bug Reports. , 97–106[10.1109/compsac.2014.16]

16.Xin Xia, David Lo, Xinyu Wang, Bo Zhou. (2015). Dual analysis for recommending developers to resolve bugs. Journal of Software Evolution and Process, 27(3), 195–220[10.1002/smr.1706]

17.K. K. Chaturvedi, V. B. Singh. (2012). Determining Bug severity using machine learning techniques. , 1–6[10.1109/conseg.2012.6349519]

18.Gitika Sharma, Sumit Sharma, Shruti Gujral. (2015). A Novel Way of Assessing Software Bug Severity Using Dictionary of Critical Terms. Procedia Computer Science, 70, 632–639[10.1016/j.procs.2015.10.059]

19.Tofigh Naghibi, Sarah Hoffmann, Beat Pfister. (2014). A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 1529–1541[10.1109/tpami.2014.2372791]

20.Nivir Kanti-Singha Roy, Bruno Rossi. (2014). Towards an Improvement of Bug Severity Classification. , 269–276[10.1109/seaa.2014.51]

21.Shruti Gujral, Gitika Sharma, Sumit Sharma, Diksha. (2015). Classifying bug severity using dictionary based approach. [10.1109/ablaze.2015.7154933]

22.Sadia Sharmin, Amin Ahsan Ali, Muhammad Asif Hossain Khan, Mohammad Shoyaib. (2017). Feature Selection and Discretization based on Mutual Information. , 20, 1–6[10.1109/icivpr.2017.7890885]

23.Ahmed Fawzi Otoom, Doaa Al-Shdaifat, Maen Hammad, Emad E. Abdallah. (2016). Severity prediction of software bugs. , 92–95[10.1109/iacs.2016.7476092]

24.Naresh Kumar Nagwani, Shrish Verma. (2014). A Comparative Study of Bug Classification Algorithms. International Journal of Software Engineering and Knowledge Engineering, 24(01), 111–138[10.1142/s0218194014500053]

25.S. A. Fattah, Chia-Chun Lin, Sun-Yuan Kung. (2011). A mutual information based approach for evaluating the quality of clustering. , 22, 601–604[10.1109/icassp.2011.5946475]

Cited By

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.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.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.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.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.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.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.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.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.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.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]

12.Md. Hasan Tarek, Md. Eusha Kadir, Sadia Sharmin, Abu Ashfaqur Sajib, Amin Ahsan Ali, Mohammad Shoyaib. (2021). Feature Subset Selection based on Redundancy Maximized Clusters. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 521–526[10.1109/icmla52953.2021.00087]

13.Tariq Saeed Mian, Abdullah Alsaeedi. (2024). Software Bug Severity Prediction Using Convolutional Neural Network and BiLSTM Models. Lecture notes on data engineering and communications technologies, 1–12[10.1007/978-3-031-59707-7_1]

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.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]