Abstract
Software analysis and prediction system development is the significant and much-needed field of software testing in software engineering. The automatic software predictors analyze, predict, and classify a variety of errors, faults, and defects using different learning-based methods. Many research contributions have evolved in this direction. In recent years, however, they have faced the challenges of software validation, non-balanced and unequal data, classifier selection, code size, code dependence, resources, accuracy, and performance. There is, therefore, a great need for an effective and automated software defect-based prediction system that uses machine learning techniques, with great efficiency. In this paper, a variety of such studies and systems are discussed and compared. Their measurement methods such as metrics, features, parameters, classifiers, accuracy, and data sets are found and discriminated. In addition to this, their challenges, threats, and limitations are also stated to demonstrate their system’s effectiveness. Therefore, it was discovered that such systems accounted for 44% use of the NASA’s PROMISE data samples, 68.18% metrics use of software, and also 16% use of the Logistic Regression method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
P.K. Singh, R.K. Panda, O. Prakash, A critical analysis on software fault prediction techniques. World Appl. Sci. 33(3), 371–379 (2015)
R. Malhotra, A systematic review of machine learning techniques for software fault prediction. App. Soft Comput. 27, 504–518 (2015)
L. Goel, D. Damodaran, S.K. Khatri, M. Sharma, A literature review on cross-project defect prediction, in 4th International Conference on Electrical, Computer and Electronics (IEEE, 2017), pp. 680–685
N. Kalaivani, R. Beena, Overview of software defect prediction using machine learning algorithms. Int. J. Pure App. Math. 118(20), 3863–3873 (2018)
S. Kumar, S.S. Rathore, Types of software fault prediction, in Software Fault Prediction, Springer Briefs in Computer Science (Springer, 2018), pp. 23–30
S.S. Rathore, S. Kumar, A study on software fault prediction techniques. Art. Int. Rev. 51, 255–327 (2019)
Z. Tian, J. Xiang, S. Zhenxiao, Z. Yi, Y. Yunqiang, Software defect prediction based on machine learning algorithms, in International Conference on Computer and Communication Systems (IEEE, 2019), pp. 520–525
B. Eken, Assessing personalized software defect predictors, in 40th International Conference on Software Engineering: Companion (IEEE, 2018), pp. 488–491
G. Mauša, T.G. Grbac, B.D. Bašic, Multi-variate logistic regression prediction of fault-proneness in software modules, in Proceedings of the 35th International Convention MIPRO (IEEE, 2012), pp. 698–703
K. Gao, T.M. Khoshgoftaar, A. Napolitano, A hybrid approach to coping with high dimensionality and class imbalance for software defect prediction, in 11th International Conferences on Machine Learning and Apps (IEEE, 2012), pp. 281–288
K.V.S. Reddy, B.R. Babu, Logistic regression approach to software reliability engineering with failure prediction. Int. J. Softw. Eng. App. 4(1), 55–65 (2013)
A. Panichella, R. Oliveto, A.D. Lucia, Cross-project defect prediction models: L'Union fait la force, in Software Evolution Week-Conference on Software Maintenance, Reengineering, and Reverse Engineering (IEEE, 2014), pp. 164–173
D. Kumari, K. Rajnish, Comparing efficiency of software fault prediction models developed through binary and multinomial logistic regression techniques, in Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing, vol. 339, ed. by J. Mandal, S. Satapathy, M. Kumar Sanyal, P. Sarkar, A. Mukhopadhyay (Springer, 2015), pp. 187–197
F. Thung, X.D. Le, D. Lo, Active semi-supervised defect categorization, in 23rd International Conference on Program Comprehension (IEEE Press, 2015), pp. 60–70
G.K. Rajbahadur, S. Wang, Y. Kamei, A.E. Hassan, The impact of using regression models to build defect classifiers, in 14th International Conference on Mining Software Repositories (IEEE, 2017), pp. 135–145
S.O. Kini, A. Tosun, Periodic developer metrics in software defect prediction, in 18th International Working Conference on Source Code Analysis & Manipulation (IEEE, 2018), pp. 72–81
K. Bashir, T. Ali, M. Yahaya, A.S. Hussein, A hybrid data preprocessing technique based on maximum likelihood logistic regression with filtering for enhancing software defect prediction, in 14th International Conferences on Intelligent Systems and Knowledge Engineering (IEEE, 2019), pp. 921–927
P. Singh, R. Malhotra, S. Bansal, Analyzing the effectiveness of machine learning algorithms for determining faulty classes: a comparative analysis, in 9th International Conference on Cloud Computing, Data Science and Engineering (IEEE, 2019), pp. 325–330
S. Agarwal, S. Gupta, R. Aggarwal, S. Maheshwari, L. Goel, S. Gupta, Substantiation of software defect prediction using statistical learning: an empirical study, in 4th International Conference on Internet of Things: Smart Innovation and Usages (IEEE Press, 2019), pp. 1–6
F. Wang, J. Ai, Z. Zou, A cluster-based hybrid feature selection method for defect prediction, in 19th International Conference on Software Quality, Reliability and Security (IEEE, 2019), pp. 1–9
H. Wang, T.M. Khoshgoftaar, A study on software metric selection for software fault prediction, in 18th International Conferences on Machine Learning and Applications (IEEE, 2019), pp. 1045–1050
P Singh, Stacking based approach for prediction of faulty modules, in Conference on Information and Communication Technology (IEEE, 2019) , pp. 1–6
S. Moudache, M. Badri, Software fault prediction based on fault probability and impact, in 18th International Conferences on Machine Learning and Applications (IEEE, 2019), pp. 1178–1185
T. Yu, W. Wen, X. Han, J.H. Hayes, ConPredictor: concurrency defect prediction in real-world applications. IEEE Trans. Softw. Eng. 45(6), 558–575 (2019)
K. Kaewbanjong, S. Intakosum, Statistical analysis with prediction models of user satisfaction in software project factors, in 17th International Conferences on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (IEEE, 2020), pp. 637–643
M. Cetiner, O.K. Sahingoz, A comparative analysis for machine learning based software defect prediction systems, in 11th International Conference on Computing Communication & Networking Technologies (IEEE, 2020), pp. 1–7
M.A. Ibraigheeth, S.A. Fadzli, Software project failures prediction using logistic regression modeling, in 2nd International Conference on Information Science (IEEE, 2020), pp. 1–5
E. Elahi, S. Kanwal, A.N. Asif, A new ensemble approach for software fault prediction, in 17th International Bhurban Conference on Applied Sciences and Technology (IEEE, 2020), pp. 407–412
J. Deng, L. Lu, S. Qiu, Y. Ou, A suitable AST node granularity and multi-kernel transfer convolutional neural network for cross-project defect prediction. IEEE (2020), pp. 66647–66661
F. Yucalar, A. Ozcift, E. Borandag, D Kilinc, Multiple-classifiers in software quality engineering: combining predictors to improve software fault prediction ability. Eng. Sci. Tech. Int. J. 23(4), 938–950 (2020)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Goyal, J., Ranjan Sinha, R. (2022). Software Defect-Based Prediction Using Logistic Regression: Review and Challenges. In: Luhach, A.K., Poonia, R.C., Gao, XZ., Singh Jat, D. (eds) Second International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1235. Springer, Singapore. https://doi.org/10.1007/978-981-16-4641-6_20
Download citation
DOI: https://doi.org/10.1007/978-981-16-4641-6_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4640-9
Online ISBN: 978-981-16-4641-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)