Software fault detection plays a significant role in the management of software systems quality to locate the fault and to identify the cause. Few research works has been developed for detecting the cause of failure occurrence from event log files. Performance of conventional software failure prediction technique was not effective. In order to overcome such limitation, a Stochastic Bio-inspired Genetic-based Trapezoidal Fuzzy Logic (SBG-TFL) Model is proposed using event log files. The SBG-TFL Model is designed to identify the failure cause with the portfolio formation of good parameters. The SBG-TFL Model at first constructs the projects portfolio with help of optimal parameters selected from event log files with application of Stochastic Bio-inspired Gene Optimization (SBGO) Algorithm. The formation of projects portfolio assists for SBG-TFL Model to reduce the amount of time taken for analysing the failure behaviour of a systems application. SBG-TFL Model applies Trapezoidal Fuzzy Logic Model to formulated projects portfolio in order to effectively predict the failure causes of software application. SBG-TFL Model increases the accuracy and true positive rate of software failure prediction. The SBG-TFL Model conducts the experimental process on metrics such as recall precision and software failure identification time with respect to different software code size. The experimental result shows that SBG-TFL Model is able to improve the precision of software failure detection and also reduces software failure identification time when compared to state-of-the-art-works.

Event Logs, Fuzzy Rule, Paremeter, Projects Portfolio Software Failure, Trapezoidal Membership Function

[1] Marcello Cinque, Domenico Cotroneo, and Antonio Pecchia, “Event Logs for the Analysis of Software Failures: a Rule-Based Approach”, IEEE Transactions on Software Engineering, Volume 39, Issue 6, Pages 806 – 821, 2013
[2] De-Qing Zou, Hao Qin, Hai Jin, “UiLog: Improving Log-Based Fault Diagnosis by Log Analysis”, Journal of Computer Science and Technology, Springer, Volume 31, Issue 5, Pages 1038–1052, September 2016
[3] Michael Grottke, Dong Seong Kim, Rajesh Mansharamani, Manoj Nambiar, Roberto Natella, and Kishor S. Trivedi, “Recovery From Software Failures Caused by Mandelbugs”, IEEE Transactions On Reliability, Volume 65 , Issue 1, Pages 70 – 87, July 2015
[4] Subhashis Chatterjee, Bappa Maji, “A New Fuzzy Rule Based Algorithm for Estimating Software Faults in Early Phase of Development”, Soft Computing, Springer, Volume 20, Issue 10, Pages 4023–4035, June 2015
[5] Ilenia Fronza, Alberto Sillitti, Giancarlo Succi, Mikko Terho, Jelena Vlasenko, “Failure prediction based on log files using Random Indexing and Support Vector Machines”, Journal of Systems and Software, Volume 86, Issue 1, Pages 2-11, January 2013
[6] Yuan Yuan, Shiyu Zhou, Crispian Sievenpiper, Kamal Mannar, Yibin Zheng, “Event log modeling and analysis for system failure prediction”, IIE Transactions Journal, Volume 43, Issue 9, Pages 647-660, 2011
[7] Maggie Hamill, Katerina Goseva-Popstojanova, “Analyzing and predicting effort associated with finding and fixing software faults”, Information and Software Technology, Elsevier, Volume 87, Pages 1-18, July 2017
[8] Ruchika Malhotra, “A systematic review of machine learning techniques for software fault prediction”, Applied Soft Computing, Elsevier, Volume 27, Pages 504-518, February 2015
[9] Partha S. Bishnu, Vandana Bhattacherjee, “Software Fault Prediction Using Quad Tree-Based K-Means Clustering Algorithm”, IEEE Transactions on Knowledge and Data Engineering, Volume 24, Issue 6, Pages 1146 – 1150, June 2012
[10] Santosh S. Rathore, Sandeep Kumar, “An empirical study of some software fault prediction techniques for the number of faults prediction”, Soft Computing, Springer, Volume 21, Issue 24, Pages 7417–7434, 2016
[11] Karel Dejaeger, Thomas Verbraken, Bart Baesens, “Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers”, IEEE Transactions on Software Engineering, Volume 39, Pages 237-257, 2013
[12] Subhashis Chatterjee, Arunava Roy, “Novel Algorithms for Web Software Fault Prediction”, Quality and Reliability Engineering International, Wiley Publications, Volume 31, Pages 1517–1535, 2015
[13] Ezgi Erturk, Ebru Akcapinar Sezer, “Software fault prediction using Mamdani type fuzzy inference system”, International Journal of Data Analysis Techniques and Strategies, Volume 8, Issue 1, Pages 14 – 28, 2016
[14] Chubato Wondaferaw Yohannese, Tianrui Li, “A Combined-Learning Based Framework for Improved Software Fault Prediction”, International Journal of Computational Intelligence Systems, Volume 10, Issue 1, Pages 647 – 662, 2017
[15] Momotaz Begum, Tadashi Dohi, “A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation”, Journal of Software Engineering and Applications, Volume 10, Pages 288- 309, 2017
[16] Pradeep Singh and Shrish Verma, “An Efficient Software Fault Prediction Model using Cluster based Classification”, International Journal of Applied Information Systems, Volume 7, Issue 3, Pages 35-41, May 2014
[17] Feras A. Batarseh, Avelino J. Gonzalez, “Predicting failures in agile software development through data analytics”, Software Quality Journal, Springer, Pages 1–18, 2015
[18] Manjula Gandhi Selvaraj, Devi Shree Jayabal, Thenmozhi Srinivasan, and Palanisamy Balasubramanie, “Predicting Defects Using Information Intelligence Process Models in the Software Technology Project”, The Scientific World Journal, Hindawi Publishing Corporation, Volume 2015 (2015), Article ID 598645, Pages 1-6, 2015
[19] Jinsheng Ren, Ke Qin, Ying Ma, and Guangchun Luo, “On Software Defect Prediction Using Machine Learning”, Journal of Applied Mathematics, Hindawi Publishing Corporation, Volume 2014 (2014), Article ID 785435, Pages 1-8, 2014
[20] Ying Ma, Guangchun Luo, “Kernel Based Asymmetric Learning for Software Defect Prediction”, IEICE Transaction on Information & System, Issue 1, Pages 1-4, January 2010