1 |
Arshad, A., Riaz, S., Jiao, L. and Murthy, A., 2018. Semi-supervised deep fuzzy c-mean clustering for software fault prediction. IEEE Access, 6, pp.25675-25685.
DOI
|
2 |
Riaz, S., Arshad, A. and Jiao, L., 2018. Rough noise-filtered easy ensemble for software fault prediction. Ieee Access, 6, pp.46886-46899.
DOI
|
3 |
Aziz, S.R., Khan, T. and Nadeem, A., 2019. Experimental validation of inheritance Metrics' impact on software fault prediction. IEEE Access, 7, pp.85262-85275.
DOI
|
4 |
Bal, P.R. and Kumar, S., 2020. WR-ELM: Weighted Regularization Extreme Learning Machine for Imbalance Learning in Software Fault Prediction. IEEE Transactions on Reliability, 69(4), pp.1355-1375.
DOI
|
5 |
Aziz, S.R., Khan, T.A. and Nadeem, A., 2020. Efficacy of Inheritance Aspect in Software Fault Prediction-A Survey Paper. IEEE Access, 8, pp.170548-170567.
DOI
|
6 |
Tumar, I., Hassouneh, Y., Turabieh, H. and Thaher, T., 2020. Enhanced binary moth flame optimization as a feature selection algorithm to predict software fault prediction. IEEE Access, 8, pp.8041-8055.
DOI
|
7 |
Al Qasem, O., Akour, M. and Alenezi, M., 2020. The influence of deep learning algorithms factors in software fault prediction. IEEE Access, 8, pp.63945-63960.
DOI
|
8 |
Kumar, L., Misra, S. and Rath, S.K., 2017. An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes. Computer standards & interfaces, 53, pp.1-32.
DOI
|
9 |
Yucalar, F., Ozcift, A., Borandag, E. and Kilinc, D., 2020. Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability. Engineering Science and Technology, an International Journal, 23(4), pp.938-950.
DOI
|
10 |
Dejaeger, K., Verbraken, T. and Baesens, B., 2012. Toward comprehensible software fault prediction models using bayesian network classifiers. IEEE Transactions on Software Engineering, 39(2), pp.237-257.
DOI
|
11 |
Vandecruys, O., Martens, D., Baesens, B., Mues, C., De Backer, M. and Haesen, R., 2008. Mining software repositories for comprehensible software fault prediction models. Journal of Systems and software, 81(5), pp.823-839.
DOI
|
12 |
Catal, C. and Diri, B., 2009. Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem. Information Sciences, 179(8), pp.1040-1058.
DOI
|
13 |
Gyimothy, T., Ferenc, R. and Siket, I., 2005. Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions on Software engineering, 31(10), pp.897-910.
DOI
|
14 |
Ostrand, T.J., Weyuker, E.J. and Bell, R.M., 2005. Predicting the location and number of faults in large software systems. IEEE Transactions on Software Engineering, 31(4), pp.340-355.
DOI
|
15 |
Hall, T., Beecham, S., Bowes, D., Gray, D. and Counsell, S., 2011. A systematic literature review on fault prediction performance in software engineering. IEEE Transactions on Software Engineering, 38(6), pp.1276-1304.
DOI
|
16 |
Rathore, S.S. and Kumar, S., 2015. Predicting number of faults in software system using genetic programming. Procedia Computer Science, 62, pp.303-311.
DOI
|
17 |
Li, Y., Wong, W.E., Lee, S.Y. and Wotawa, F., 2019. Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction. IEEE Access, 7, pp.63066-63080.
DOI
|
18 |
Erturk, E. and Sezer, E.A., 2015. A comparison of some soft computing methods for software fault prediction. Expert systems with applications, 42(4), pp.1872-1879.
DOI
|
19 |
Rathore, S.S. and Kumar, S., 2017. Towards an ensemble based system for predicting the number of software faults. Expert Systems with Applications, 82, pp.357-382.
DOI
|
20 |
Arshad, A., Riaz, S., Jiao, L. and Murthy, A., 2018. The empirical study of semi-supervised deep fuzzy c-mean clustering for software fault prediction. IEEE Access, 6, pp.47047-47061.
DOI
|
21 |
Zhao, Y., Yang, Y., Lu, H., Zhou, Y., Song, Q. and Xu, B., 2015. An empirical analysis of package-modularization metrics: Implications for software fault-proneness. Information and Software Technology, 57, pp.186-203.
DOI
|
22 |
Moeyersoms, J., de Fortuny, E.J., Dejaeger, K., Baesens, B. and Martens, D., 2015. Comprehensible software fault and effort prediction: A data mining approach. Journal of Systems and Software, 100, pp.80-90.
DOI
|
23 |
Erturk, E. and Sezer, E.A., 2016. Iterative software fault prediction with a hybrid approach. Applied Soft Computing, 49, pp.1020-1033.
DOI
|
24 |
Haouari, A.T., Souici-Meslati, L., Atil, F. and Meslati, D., 2020. Empirical comparison and evaluation of Artificial Immune Systems in inter-release software fault prediction. Applied Soft Computing, 96, p.106686.
DOI
|
25 |
Jin, C. and 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, pp.717-725.
DOI
|
26 |
Malhotra, R., 2015. A systematic review of machine learning techniques for software fault prediction. Applied Soft Computing, 27, pp.504-518
DOI
|
27 |
Mahajan, R., Gupta, S.K. and Bedi, R.K., 2015. Design of software fault prediction model using BR technique. Procedia Computer Science, 46, pp.849-858.
DOI
|
28 |
Arar, O.F. and Ayan, K., 2016. Deriving thresholds of software metrics to predict faults on open source software: Replicated case studies. Expert Systems with Applications, 61, pp.106-121.
DOI
|
29 |
Chatterjee, S. and Roy, A., 2014. Web software fault prediction under fuzzy environment using MODULO-M multivariate overlapping fuzzy clustering algorithm and newly proposed revised prediction algorithm. Applied Soft Computing, 22, pp.372-396.
DOI
|
30 |
Binkley, D., Feild, H., Lawrie, D. and Pighin, M., 2009. Increasing diversity: Natural language measures for software fault prediction. Journal of Systems and Software, 82(11), pp.1793-1803.
DOI
|
31 |
Fenton, N.E. and Ohlsson, N., 2000. Quantitative analysis of faults and failures in a complex software system. IEEE Transactions on Software engineering, 26(8), pp.797-814.
DOI
|
32 |
Hu, Q.P., Xie, M., Ng, S.H. and Levitin, G., 2007. Robust recurrent neural network modeling for software fault detection and correction prediction. Reliability Engineering & System Safety, 92(3), pp.332-340.
DOI
|
33 |
Gao, K. and Khoshgoftaar, T.M., 2007. A comprehensive empirical study of count models for software fault prediction. IEEE Transactions on Reliability, 56(2), pp.223-236.
DOI
|