research-article

Bug severity classification in software using ant colony optimization based feature weighting technique

Authors:

Ashutosh Sharma,

Jasminder Kaur SandhuAuthors Info & Claims

Volume 230, Issue C

https://doi.org/10.1016/j.eswa.2023.120573

Published: 15 November 2023 Publication History

Abstract

At the present, the delivery of the software should be time-bound without affecting the quality of the software. However, bug severity can affect the timely delivery of software. It is a crucial component of the software engineering, including maintenance and testing. Both phases are essential for bug severity classification but require much time. Generally, bug triage is responsible for classifying the bugs based on criticality/severeness. The manual execution of this process is error-prone. Consequently, a model for automatic bug classification is required to help the bug triage. In this work, the ant colony optimization (ACO) based feature extraction technique is proposed to extract more relevant features for bug severity classification. Furthermore, the ACO technique is integrated with NB, SVM, DeepFM and F-SVM techniques for predicting bug severity and classifying bugs into multi-severity classes. Several benchmark projects such as Eclipse, Mozilla, OpenFOAM, JBoss, and Firefox, are considered to evaluate the efficacy of the techniques above. The simulation outcomes are expressed in terms of Accuracy, Precision, Recall, and F1-measure. It is noted that the outcomes of the SVM, NB, DeepFM and F-SVM approaches are improved by the ACO-based feature weighting technique. The accuracy rate of ACO-F-SVM, ACO-NB, ACO-SVM, ACO-DeepFM, NB, SVM, F-SVM, DeepFM techniques are ranging in between 85.73 and 89.38%, 78% to 80%, 73% to 76%, 92.67% to 97.27 %, 71% to 77%,65% to 74%, 78.21% to 81.28% and 90.02% to 95.24% respectively for five benchmark projects. Further, proposed techniques are also produced better simulation results as compared with state-of –the-art techniques. Friedman and post hoc statistical tests are also conducted on proposed techniques.

References

[1]

M.H. Aghdam, N. Ghasem-Aghaee, M.E. Basiri, Application of ant colony optimization for feature selection in text categorization, IEEE World Congress on Computational Intelligence 4 (3) (2008).

[2]

Antoniol, G., Ayari, K., Penta, M. D., Khomh, F., & Guéhéneuc, Y. G. (2008). Is it a bug or an enhance- ment?: a text-based approach to classify change requests. Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds, 18, 23–28.

[3]

M.E. Basiri, N. Ghasem-Aghaee, M.H. Aghdam, Using ant colony optimization-based se- lected features for predicting post-synaptic activity in proteins, in: European Conference on Evolutionary Computation, Springer, 2008, pp. 12–23.

[4]

B. Beizer, Software testing techniques, Dreamtech Press, 2003.

Digital Library

[5]

Bibyan, R., Anand, S., Jaiswal, A., & Aggarwal, A. G. Bug severity prediction using LDA and sentiment scores: A CNN approach. Expert Systems, 13264–13264.

[6]

C. Catal, B. Diri, A systematic review of software fault prediction studies, Expert Systems with Applications 36 (4) (2009) 7346–7354.

[7]

K.K. Chaturvedi, V.B. Singh, Determining bug severity using machine learning techniques, Sixth International Conference on CSI (2012) 1–6.

[8]

Chauhan, A. & Kumar, R. (2020). Bug severity classification using semantic feature with convolution neural network. In Computing in Engineering and Technology: Proceedings of ICCET 2019, (pp. 327– 335). Springer.

[9]

Chawla, I. & Singh, S. K. (2014). Automatic bug labeling using semantic information from LSI. Seventh International Conference on Contemporary Computing (IC3).

[10]

Chawla, I. & Singh, S. K. (2015). An automated approach for bug categorization using fuzzy logic. Pro- ceedings of the 8th India Software Engineering Conference, 90–99.

[11]

H. Chen, X.Y. Jing, Y. Zhou, B. Li, B. Xu, Aligned metric representation based balanced multiset ensemble learning for heterogeneous defect prediction, Information and Software Technology 147 (2022).

[12]

M. Dorigo, T. Stützle, Ant colony optimization: Overview and recent advances, Springer International Publishing, 2019, pp. 311–351.

[13]

X. Du, T. Wang, L. Wang, W. Pan, C. Chai, X. Xu, J. Wang, CoreBug: Improving effort- aware bug prediction in software systems using generalized k-core decomposition in class dependency networks, Axioms 11 (5) (2022) 205.

[14]

A. Dubey, U. Gupta, S. Jain, Medical data clustering and classification using TLBO and machine learning algorithms, Computers, Materials and Continua 70 (3) (2021) 4523–4543.

[15]

Gartner, T. & Flach, P. A. (2001). WBC-SVM: Weighted Bayesian Classification Based on Support Vector Machines. Proceedings of the Eighteenth International Conference on Machine Learning.

[16]

Gegick, M., Rotella, P., & Xie, T. (2010). Identifying security bug reports via text mining: An industrial case study. 7th IEEE working conference on Mining software repositories (MSR).

[17]

Giger, E., Pinzger, M., & Gall, H. (2010). Predicting the fix time of bugs. Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering, 52–56.

[18]

Goseva-Popstojanova, K. & Tyo, J. (2018). Identification of Security Related Bug Reports via Text Mining Using Supervised and Unsupervised Classification. IEEE International Conference on Software Quality, Reliability and Security.

[19]

Gujral, S., Sharma, G., & Sharma, S. (2015). Classifying bug severity using dictionary based approach. International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE).

[20]

Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247.

[21]

Guo, H., Tang, R., Ye, Y., Li, Z., He, X., & Dong, Z. (2018). Deepfm: An end-to-end wide & deep learning framework for CTR prediction. arXiv preprint arXiv:1804.04950.

[22]

M. Hall, A decision tree-based attribute weighting filter for naive Bayes, International Conference on Innovative Techniques and Applications of Artificial Intelligence (2007) 59–70.

[23]

W. Han, C.H. Lung, S. Ajila, Using Source Code and Process Metrics for Defect Prediction-A Case Study of Three Algorithms and Dimensionality Reduction, J. Softw. 11 (9) (2016) 883–902.

[24]

A. Iqbal, S. Aftab, I. Ullah, M.S. Bashir, M.A. Saeed, A Feature Selection based Ensemble Classification Framework for Software Defect Prediction, International Journal of Modern Education and Computer Science 11 (9) (2019).

[25]

K. Jin, A. Dashbalbar, G. Yang, J.W. Lee, B. Lee, Bug severity prediction by classifying normal bugs with text and meta-field information, Advanced Science and Technology Letters 129 (2016) 19–24.

[26]

R. Jindal, R. Malhotra, A. Jain, Prediction of defect severity by mining software project reports, International Journal of System Assurance Engineering and Management 8 (2) (2017) 334–351.

[27]

Kaur, K., Name, J. K., & Malhotra, J. (2017). Evaluation of imbalanced learning with entropy of source code metrics as defect predictors. 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), 403–409.

[28]

A. Kukkar, U.K. Lilhore, J. Frnda, J.K. Sandhu, R.P. Das, N. Goyal, F. Rezac, ProRE: An ACO-based programmer recommendation model to precisely manage software bugs, Journal of King Saud University-Computer and Information Sciences 35 (1) (2023) 483–498.

[29]

A. Kukkar, R. Mohana, A Supervised Bug Report Classification with Incorporate and Textual field Knowledge, Procedia Computer Science 132 (2018) 352–361.

[30]

A. Kukkar, R. Mohana, A. Nayyar, J. Kim, B.G. Kang, N. Chilamkurti, A novel deep- learning-based bug severity classification technique using convolutional neural networks and random forest with boosting, Sensors 19 (13) (2019) 2964.

[31]

A. Kukkar, R. Mohana, A. Nayyar, J. Kim, B.G. Kang, N. Chilamkurti, A novel deep- learning-based bug severity classification technique using convolutional neural networks and random forest with boosting, Sensors 19 (13) (2019).

[32]

A. Kumar, Y. Kumar, A. Kukkar, A feature selection model for prediction of software defects, International Journal of Embedded Systems 13 (1) (2020) 28–39.

[33]

Y. Kumar, P.K. Singh, Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering, Applied Intelligence 48 (9) (2018) 2681–2697.

[34]

Lamkanfi, A., Demeyer, S., Giger, E., & Goethals, B. (2010). Predicting the severity of a reported bug. 7th IEEE Working Conference on Mining Software Repositories (MSR).

[35]

Limsettho, N., Hata, H., Monden, A., & Matsumoto, K. (2014). Automatic unsupervised bug report categorization. 6th International Workshop on Empirical Software Engineering in Practice.

[36]

H. Lu, S. Uddin, A disease network-based recommender system framework for predictive risk modelling of chronic diseases and their comorbidities, Applied Intelligence 52 (9) (2022) 10330–10340.

[37]

R. Malhotra, A systematic review of machine learning techniques for software fault prediction, Applied Soft Computing 27 (2015) 504–518.

Digital Library

[38]

Malik, V., Mittal, R., Singh, J., Rattan, V., & Mittal, A. (2021). Feature selection optimization using ACO to improve the classification performance of web log data. 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), 671–675.

[39]

T. Menzies, A. Marcus, Automated severity assessment of software defect reports, IEEE Inter- national Conference on Software Maintenance (2008) 346–355.

[40]

A.M. Mesleh, G. Kanaan, Support vector machine text classification system: Using Ant Colony Optimization based feature subset selection, International Conference on Computer Engineering & Sys- tems (2008) 143–148.

[41]

Murphy, G. & Cubranic, D. (2004). Automatic bug triage using text categorization. Proceedings of the Sixteenth International Conference on Software Engineering & Knowledge Engineering.

[42]

Nagwani, N. K., Verma, S., & Mehta, K. K. (2013). Generating taxonomic terms for software bug classifi- cation by utilizing topic models based on Latent Dirichlet Allocation. 11th International Conference on ICT and Knowledge Engineering (ICT&KE).

[43]

Ohira, M., Kashiwa, Y., Yamatani, Y., Yoshiyuki, H., Maeda, Y., Limsettho, N., ... & Matsumoto, K. (2015, May). A dataset of high impact bugs: Manually-classified issue reports. In 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories (pp. 518-521). IEEE.

[44]

N. Ohlsson, M. Zhao, M. Helander, Application of multivariate analysis for software fault prediction, Software Quality Journal 7 (1) (1998) 51–66.

[45]

N. Pandey, D.K. Sanyal, A. Hudait, A. Sen, Automated classification of software issue reports using machine learning techniques: An empirical study, Innovations in Systems and Software Engineering 13 (4) (2017) 279–297.

[46]

Permata, S. G. I. (2012, September). An attribute selection for severity level determination according to the support vector machine classification result. In proceedings intl conf information system business competitiveness.

[47]

Y. Qu, Q. Zheng, J. Chi, Y. Jin, A. He, D. Cui, T. Liu, Using K-core Decomposition on Class Dependency Networks to Improve Bug Prediction Model’s Practical Performance, IEEE Transactions on Software Engineering 47 (2) (2019).

[48]

E. Raymond, The cathedral and the bazaar, Knowledge. Technology & Policy 12 (3) (1999) 23–49.

[49]

Rehman, L., Iqbal, M. J., Ramzan, S., Yaqoob, S., Haq, I. U., Jaffar, A., & Nawaz, S. (2023).

[50]

Roy, N. K. S. & Rossi, B. (2014). Towards an improvement of bug severity classification. 40th EUROMICRO Conference on Software Engineering and Advanced Applications, 269–276.

[51]

K.K. Sabor, M. Hamdaqa, A. Hamou-Lhadj, Automatic prediction of the severity of bugs using stack traces and categorical features, Information and Software Technology 123 (2020).

[52]

E. Saraç, S.A. Özel, An ant colony optimization based feature selection for web page classification, The Scientific World Journal (2014).

[53]

Sharma, G., Sharma, S., & Gujral, S. (2015). A Novel Way of Assessing Software Bug Severity Using Dictionary of Critical Terms. Proceedings of the 4th International Conference on Eco-friendly Computing and Communication Systems, 70, 632–639.

[54]

S. Sharmin, F. Aktar, A.A. Ali, M.A.H. Khan, M. Shoyaib, BFSp: A feature selection method for bug severity classification, Humanitarian Technology Conference R10-HTC (2017) 10.

[55]

Y. Shi, Y. Tian, G. Kou, Y. Peng, J. Li, Optimization based data mining: Theory and applications, Springer Science & Business Media, 2011.

[56]

V.B. Singh, S. Misra, M. Sharma, Bug severity assessment in cross project context and identifying training candidates, Journal of Information & Knowledge Management 16 (01) (2017) 1750005.

[57]

Thung, F., Lo, D., & Jiang, L. (2012). Automatic defect categorization. 9th Working Conference on Reverse Engineering (WCRE). 205–214.

[58]

Y. Tian, D. Lo, C. Sun, Drone: Predicting priority of reported bugs by multi-factor analysis, IEEE International Conference on Software Maintenance (2013) 200–209.

[59]

S. Verberne, M. Sappelli, D. Hiemstra, W. Kraaij, Evaluation and analysis of term scoring methods for term extraction, Information Retrieval Journal 19 (5) (2016) 510–545.

[60]

Wu, X., Zuo, W., Lin, L., Jia, W., & Zhang, D. (2018). F-SVM: Combination of feature transformation and SVM learning via convex relaxation. IEEE transactions on neural networks and learning systems. 29.

[61]

X. Wu, W. Zheng, X. Chen, Y. Zhao, T. Yu, D. Mu, Improving high-impact bug report prediction with combination of interactive machine learning and active learning, Information and Software Technology, 2021, p. 133.

[62]

J. Yang, X. Dong, S. Jin, Metro passenger flow prediction model using attention-based neural network, IEEE Access 8 (2020) 30953–30959.

[63]

T. Zhang, J. Chen, G. Yang, B. Lee, X. Luo, Towards more accurate severity prediction and fixer recommendation of software bugs, Journal of Systems and Software 117 (2016) 166–184.

[64]

Zhang, T., Yang, G., Lee, B., & Chan, A. T. (2015). Predicting severity of bug report by mining bug repository with concept profile. Proceedings of the 30th Annual ACM Symposium on Applied Computing, 1553–1558.

[65]

Y. Zhou, Y. Tong, R. Gu, H. Gall, Combining text mining and data mining for bug report classification. Journal of Software, Evolution. (2016).

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Bug severity classification in software using ant colony optimization based feature weighting technique

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 230, Issue C

Nov 2023

1487 pages

ISSN:0957-4174

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 15 November 2023

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