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Software fault prediction using deep learning techniques

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Abstract

Software fault prediction (SFP) techniques identify faults at the early stages of the software development life cycle (SDLC). We find machine learning techniques commonly used for SFP compared to deep learning methods, which can produce more accurate results. Deep learning offers exceptional results in various domains, such as computer vision, natural language processing, and speech recognition. In this study, we use three deep learning methods, namely, long short-term memory (LSTM), bidirectional LSTM (BILSTM), and radial basis function network (RBFN) to predict software faults and compare our results with existing models to show how our results are more accurate. Our study uses Chidamber and Kemerer (CK) metrics-based datasets to conduct experiments and test our proposed algorithm. We conclude that LSTM and BILSTM perform better, whereas RBFN is faster in producing the required results. We use k-fold cross-validation to do the model evaluation. Our proposed models provide software developers with a more accurate and efficient SFP mechanism.

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All datasets used in this paper are publicly available.

References

  • Akour, M., Alsghaier, H., & Al Qasem, O. (2020). The effectiveness of using deep learning algorithms in predicting students achievements. Indonesian Journal of Electrical Engineering and Computer Science, 19, 387–393.

    Article  Google Scholar 

  • Al Qasem, O., & Akour, M. (2019). Software fault prediction using deep learning algorithms. International Journal of Open Source Software and Processes (IJOSSP), 10, 1–19.

    Article  Google Scholar 

  • Al Qasem, O., Akour, M., & Alenezi, M. (2020). The influence of deep learning algorithms factors in software fault prediction. IEEE Access, 8, 63945–63960.

    Article  Google Scholar 

  • Ali, A., & Gravino, C. (2021). An empirical comparison of validation methods for software prediction models. Journal of Software: Evolution and Process, 33, e2367.

    Google Scholar 

  • Ali, H., & Khan, T. A. (2019). On fault localization using machine learning techniques. In: 2019 International Conference on Frontiers of Information Technology (FIT), IEEE. pp. 357–3575.

  • Aziz, S. R., Khan, T., & Nadeem, A. (2019). Experimental validation of inheritance metrics’ impact on software fault prediction. IEEE Access, 7, 85262–85275. https://doi.org/10.1109/ACCESS.2019.2924040

    Article  Google Scholar 

  • Aziz, S. R., Khan, T. A., & Nadeem, A. (2020). Efficacy of inheritance aspect in software fault prediction - A survey paper. IEEE Access, 8, 170548–170567. https://doi.org/10.1109/ACCESS.2020.3022087

    Article  Google Scholar 

  • Aziz, S. R., Khan, T. A., & Nadeem, A. (2021). Exclusive use and evaluation of inheritance metrics viability in software fault prediction - an experimental study. PeerJ Computer Science, 7, e563. https://doi.org/10.7717/peerj-cs.563

    Article  Google Scholar 

  • Batool, I., & Khan, T. A. (2022). Software fault prediction using data mining, machine learning and deep learning techniques: A systematic literature review. Computers and Electrical Engineering, 100, 107886. https://www.sciencedirect.com/science/article/pii/S0045790622001744, https://doi.org/10.1016/j.compeleceng.2022.107886

  • Borandag, E., Ozcift, A., Kilinc, D., & Yucalar, F. (2019). Majority vote feature selection algorithm in software fault prediction. Computer Science and Information Systems, 16, 515–539.

    Article  Google Scholar 

  • Boucher, A., & Badri, M. (2018). Software metrics thresholds calculation techniques to predict fault-proneness: An empirical comparison. Information and Software Technology, 96, 38–67.

    Article  Google Scholar 

  • Bowes, D., Hall, T., & Petrić, J. (2018). Software defect prediction: do different classifiers find the same defects? Software Quality Journal, 26, 525–552.

    Article  Google Scholar 

  • Cai, X., Niu, Y., Geng, S., Zhang, J., Cui, Z., Li, J., & Chen, J. (2020). An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search. Concurrency and Computation: Practice and Experience, 32, e5478.

    Article  Google Scholar 

  • Catal, C., Sevim, U., & Diri, B. (2011). Practical development of an eclipse-based software fault prediction tool using naive bayes algorithm. Expert Systems with Applications, 38, 2347–2353.

    Article  Google Scholar 

  • Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2019). Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models. arXiv preprint arXiv:1908.01529

  • Chatterjee, S., Nigam, S., & Roy, A. (2017). Software fault prediction using neuro-fuzzy network and evolutionary learning approach. Neural Computing and Applications, 28, 1221–1231.

    Article  Google Scholar 

  • Dam, H. K., Pham, T., Ng, S. W., Tran, T., Grundy, J., Ghose, A., Kim, T., & Kim, C. J. (2018). A deep tree-based model for software defect prediction. arXiv preprint arXiv:1802.00921

  • Erturk, E., & Sezer, E. A. (2016). Iterative software fault prediction with a hybrid approach. Applied Soft Computing, 49, 1020–1033.

    Article  Google Scholar 

  • Fan, G., Diao, X., Yu, H., Yang, K., & Chen, L. (2019). Software defect prediction via attention-based recurrent neural network. Scientific Programming, 2019.

  • Farhadi, F. (2017). Learning activation functions in deep neural networks. Montreal (Canada): Ecole Polytechnique.

    Google Scholar 

  • Gao, K., Khoshgoftaar, T. M., Wang, H., & Seliya, N. (2011). Choosing software metrics for defect prediction: An investigation on feature selection techniques. Software: Practice and Experience, 41, 579–606.

  • Han, J., Pei, J., & Tong, H. (2022). Data mining: Concepts and techniques. Morgan kaufmann.

  • Hoang, T., Dam, H. K., Kamei, Y., Lo, D., & Ubayashi, N. (2019). Deepjit: An end-to-end deep learning framework for just-in-time defect prediction. In: 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), IEEE. pp. 34–45.

  • Huda, S., Alyahya, S., Ali, M. M., Ahmad, S., Abawajy, J., Al-Dossari, H., & Yearwood, J. (2017). A framework for software defect prediction and metric selection. IEEE access, 6, 2844–2858.

    Article  Google Scholar 

  • Jayanthi, R., & Florence, L. (2019). Software defect prediction techniques using metrics based on neural network classifier. Cluster Computing, 22, 77–88.

    Article  Google Scholar 

  • Jin, C., & Jin, S. W. (2015). Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization. Applied Soft Computing, 35, 717–725.

    Article  Google Scholar 

  • Jin, C., Jin, S. W., & Ye, J. M. (2012). Artificial neural network-based metric selection for software fault-prone prediction model. IET software, 6, 479–487.

    Article  Google Scholar 

  • Jones, C., & Bonsignour, O. (2011). The economics of software quality. Addison-Wesley Professional.

  • Jothi, R. (2018). A comparative study of unsupervised learning algorithms for software fault prediction. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE. pp. 741–745.

  • Karlik, B., & Olgac, A. V. (2011). Performance analysis of various activation functions in generalized mlp architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1, 111–122.

    Google Scholar 

  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  • LeCun, Y., Bengio, Y., Hinton, G., et al. (2015). Deep learning. Nature, 521(7553), 436-444. Google Scholar Cross Ref.

  • Li, J., He, P., Zhu, J., & Lyu, M. R. (2017). Software defect prediction via convolutional neural network. In: 2017 IEEE international conference on software quality, reliability and security (QRS), IEEE. pp. 318–328.

  • Li, X., Xu, Y., Li, N., Yang, B., & Lei, Y. (2022). Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks. IEEE/CAA Journal of Automatica Sinica, 10, 121–134.

    Article  Google Scholar 

  • Liang, H., Yu, Y., Jiang, L., & Xie, Z. (2019). Seml: A semantic lstm model for software defect prediction. IEEE Access, 7, 83812–83824.

    Article  Google Scholar 

  • Lin, G., Zhang, J., Luo, W., Pan, L., Xiang, Y., De Vel, O., & Montague, P. (2018). Cross-project transfer representation learning for vulnerable function discovery. IEEE Transactions on Industrial Informatics, 14, 3289–3297.

    Article  Google Scholar 

  • Malhotra, R., & Bansal, A. J. (2015). Fault prediction considering threshold effects of object-oriented metrics. Expert Systems, 32, 203–219.

    Article  Google Scholar 

  • Malhotra, R., & Jain, A. (2012). Fault prediction using statistical and machine learning methods for improving software quality. Journal of Information Processing Systems, 8, 241–262.

    Article  Google Scholar 

  • Manjula, C., & Florence, L. (2019). Deep neural network based hybrid approach for software defect prediction using software metrics. Cluster Computing, 22, 9847–9863.

    Article  Google Scholar 

  • Mercioni, M. A., Tiron, A., & Holban, S. (2019). Dynamic modification of activation function using the backpropagation algorithm in the artificial neural networks. IJACSA International Journal of Advanced Computer Science and Applications, 10.

  • Nevendra, M., & Singh, P. (2021). Software defect prediction using deep learning. Acta Polytechnica Hungarica, 18, 173–189.

    Article  MATH  Google Scholar 

  • Padhy, N., Satapathy, S., & Singh, R., (2018). State-of-the-art object-oriented metrics and its reusability: A decade review. Smart Computing and Informatics, pp. 431–441.

  • Peng, S., Jiang, H., Wang, H., Alwageed, H., & Yao, Y. D. (2017). Modulation classification using convolutional neural network based deep learning model. In: 2017 26th Wireless and Optical Communication Conference (WOCC), IEEE. pp. 1–5.

  • Phan, A. V., & LeNguyen, M. (2017). Convolutional neural networks on assembly code for predicting software defects. In: 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES), IEEE. pp. 37–42.

  • Pornprasit, C., & Tantithamthavorn, C. (2022). Deeplinedp: Towards a deep learning approach for line-level defect prediction. IEEE Transactions on Software Engineering.

  • Radjenović, D., Heričko, M., Torkar, R., & Živkovič, A. (2013). Software fault prediction metrics: A systematic literature review. Information and Software Technology, 55, 1397–1418.

  • Rathore, S. S., & Kumar, S. (2016). A decision tree regression based approach for the number of software faults prediction. ACM SIGSOFT Software Engineering Notes, 41, 1–6.

    Article  Google Scholar 

  • Rosli, M. M., Teo, N. H. I., Yusop, N. S. M., & Mohamad, N. S. (2011). Fault prediction model for web application using genetic algorithm. In: International conference on computer and software Modeling (IPCSIT), pp. 71–77.

  • Sandhu, P. S., Singh, J., Gupta, V., Kaur, M., Manhas, S., & Sidhu, R. (2010). A k-means based clustering approach for finding faulty modules in open source software systems. World academy of science, Engineering and technology, 72, 654–658.

    Google Scholar 

  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85–117.

    Article  Google Scholar 

  • Shaik, A., Reddy, K., & Damodaram, A. (2012). Object oriented software metrics and quality assessment: Current state of the art. International Journal of Computer Applications, 37, 6–15.

    Google Scholar 

  • Sharma, A., & Dubey, S. K. (2012). Comparison study and review on object-oriented metrics. Global Journal of Computer Science and Technology, 12, 47–56.

    Google Scholar 

  • Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). The performance of LSTM and BiLSTM in forecasting time series. In: 2019 IEEE International Conference on Big Data (Big Data), IEEE. pp. 3285–3292.

  • Singh, P., Chaudhary, K., & Verma, S. (2011). An investigation of the relationships between software metrics and defects. International Journal of Computer Applications, 28, 13–17.

    Article  Google Scholar 

  • Singh, P., Pal, N. R., Verma, S., & Vyas, O. P. (2016). Fuzzy rule-based approach for software fault prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 826–837.

    Article  Google Scholar 

  • Singh, Y. (2011). Software Testing. Cambridge University Press. https://doi.org/10.1017/CBO9781139196185

    Article  Google Scholar 

  • Snuverink, I. (2017). Deep learning for pixelwise classification of hyperspectral images. Ph.D. thesis. Thesis. Delft, Netherlands: Faculty of Mechanical, Maritime and Materials.

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15, 1929–1958.

    MathSciNet  MATH  Google Scholar 

  • Suresh, Y., Kumar, L., & Rath, S. K. (2014). Statistical and machine learning methods for software fault prediction using CK metric suite: A comparative analysis. International Scholarly Research Notices, 2014.

  • Suresh, Y., Pati, J., & Rath, S. K. (2012). Effectiveness of software metrics for object-oriented system. Procedia technology, 6, 420–427.

    Article  Google Scholar 

  • Suri, B., & Singhal, S. (2015). Investigating the oo characteristics of software using ckjm metrics. 2015 4th International Conference on Reliability (pp. 1–6). IEEE: Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions).

    Google Scholar 

  • Turabieh, H., Mafarja, M., & Li, X. (2019). Iterated feature selection algorithms with layered recurrent neural network for software fault prediction. Expert systems with applications, 122, 27–42.

    Article  Google Scholar 

  • Uddin, M. N., Li, B., Ali, Z., Kefalas, P., Khan, I., & Zada, I. (2022). Software defect prediction employing bilstm and bert-based semantic feature. Soft Computing, 26, 7877–7891.

    Article  Google Scholar 

  • Verma, S., Chug, A., & Singh, A. P. (2020). Impact of hyperparameter tuning on deep learning based estimation of disease severity in grape plant. In: Recent Advances on Soft Computing and Data Mining: Proceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, January 22–23, 2020, Springer. pp. 161–171.

  • Wahono, R. S., & Herman, N. S. (2014). Genetic feature selection for software defect prediction. Advanced Science Letters, 20, 239–244.

    Article  Google Scholar 

  • Wan, Z., Xia, X., Hassan, A. E., Lo, D., Yin, J., & Yang, X. (2018). Perceptions, expectations, and challenges in defect prediction. IEEE Transactions on Software Engineering, 46, 1241–1266.

    Article  Google Scholar 

  • Wang, H., Zhuang, W., & Zhang, X. (2021). Software defect prediction based on gated hierarchical lstms. IEEE Transactions on Reliability, 70, 711–727.

    Article  Google Scholar 

  • Wang, S., Liu, T., Nam, J., & Tan, L. (2018). Deep semantic feature learning for software defect prediction. IEEE Transactions on Software Engineering, 46, 1267–1293.

    Article  Google Scholar 

  • Wu, Y., Wang, H., Zhang, B., & Du, K. L. (2012). 2012. International Scholarly Research Notices: Using radial basis function networks for function approximation and classification.

    Google Scholar 

  • Xu, J., Wang, F., & Ai, J. (2020). Defect prediction with semantics and context features of codes based on graph representation learning. IEEE Transactions on Reliability, 70, 613–625.

    Article  Google Scholar 

  • Yu, L. (2012). Using negative binomial regression analysis to predict software faults: A study of apache ant.

  • Yu, Q., Qian, J., Jiang, S., Wu, Z., & Zhang, G. (2019a). An empirical study on the effectiveness of feature selection for cross-project defect prediction. IEEE Access, 7, 35710–35718.

    Article  Google Scholar 

  • Yu, Y., Si, X., Hu, C., & Zhang, J. (2019b). A review of recurrent neural networks: Lstm cells and network architectures. Neural computation, 31, 1235–1270.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Survey Tutor, 1.

  • Zhang, W., Wang, Z., & Li, X. (2023). Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis. Reliability Engineering & System Safety, 229, 108885.

    Article  Google Scholar 

  • Zheng, J. (2010). Cost-sensitive boosting neural networks for software defect prediction. Expert Systems with Applications, 37, 4537–4543.

    Article  Google Scholar 

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  1. Department of Software Engineering, Bahria University, Islamabad, Pakistan

    Iqra Batool & Tamim Ahmed Khan

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  1. Iqra Batool
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Iqra Batool and Tamim Ahmed Khan have contributed equally to the preparation of this manuscript.

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Correspondence to Tamim Ahmed Khan.

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Batool, I., Khan, T.A. Software fault prediction using deep learning techniques. Software Qual J 31, 1241–1280 (2023). https://doi.org/10.1007/s11219-023-09642-4

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