Abstract
Many businesses today still very much depend on software application service to process the daily transactions. Under such heavy dependency, it is frustrated whenever the software application service is unavailable. For the errors which can cause the software application service malfunction, it can be possible to arise mostly in either within the software application layer, or other factors which are falling outside the software application layer. In this complex situation, a lot of time consume to identify the root cause is unavoidable. The objective is not seeing from the angle of only solving the problem. More importantly, it aims to reduce the total time spent on root cause analysis, and to decide the preferred resolution for the root cause. Indeed this is crucial to propose an approach toward to develop a logic model, that makes a precise decision on identifying the root cause, as well as on identifying the preferred resolution to the root cause. The proposed logic model will consist of the algorithm incorporated with both Analytic Hierarchy Process (AHP) and Supervised Learning.
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References
da Silva Neves, A.J., Camanho, R.: The use of AHP for IT project priorization – a case study for oil & gas company. Procedia Comput. Sci. 55, 1097–1105 (2015). ScienceDirect. ELSEVIER. https://www.sciencedirect.com/science/article/pii/S1877050915015513. Accessed 20 Apr 2020
Valdman, J.: Log file analysis (2001). https://www.kiv.zcu.cz/site/documents/verejne/vyzkum/publikace/technicke-zpravy/2001/tr-2001-04.pdf. Accessed 18 Jan 2017
Murínová, J.: Application Log Analysis (2015). http://is.muni.cz/th/374567/fi_m/thesis_murinova.pdf. Accessed 18 Jan 2017
Brownlee, J.: Linear regression for machine learning. Machine Learning Mastery Pty. Ltd. (2019). https://machinelearningmastery.com/linear-regression-for-machine-learning/. Accessed 24 Feb 2020
Management Logic: Root-Cause Analysis (2012). http://www.management-logic.com/toolbox/sales/Root-Cause%20Analysis/Index.html. Accessed 28 Nov 2015
Omkarprasad, S.V., Kumar, S.: Analytic hierarchy process: an overview of applications. Department of Mechanical Engineering, Army Institute of Technology, Pune 411 015, India. National Institute of Industrial Engineering (NITIE), Vihar Lake, Mumbai 400 087, India (2004). http://ac.els-cdn.com/S0377221704003054/1-s2.0-S0377221704003054-main.pdf?_tid=39850adc-5d78-11e7-b5bf-00000aab0f26&acdnat=1498815840_e0c9a10c99c46ad30db8da4ef17e817b. Accessed 12 Sept 2018
Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.3124&rep=rep1&type=pdf. Accessed 12 Sept 2018
Saaty, R.W.: The analytic hierarchy process - what it is and how it is used. Pergamon Journals Ltd. Great Britain (1987). http://ac.els-cdn.com/0270025587904738/1-s2.0-0270025587904738-main.pdf?_tid=55956250-6b90-11e7-b75e-00000aab0f02&acdnat=1500365511_5a54a3ecf035ad3e45ab72b909b7632e. Accessed 12 Sept 2018
Wilson, A.: A brief introduction to supervised learning. Towards Data Science (2019). https://towardsdatascience.com/a-brief-introduction-to-supervised-learning-54a3e3932590. Accessed 21 Feb 2020
Hoo Meng, W., Amalathas, S.S.: A new approach towards developing a prescriptive analytical logic model for software application error analysis. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) Intelligent Systems Applications in Software Engineering. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol. 1046. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30329-7_24. Print ISBN:978-3-030-30328-0
Wong, H.M., Amalathas, S.S.: An Approach towards developing an Algorithm for Software Application Error Analysis. David Publishing Company (2019). https://doi.org/10.17265/2328-2185/2019.04.006
Wong, H.M., Amalathas, S., Zitkova, T.: A prescriptive logic model for software application root cause analysis. Eur. J. Electr. Eng. Comput. Sci. 3, 5 (2019). https://doi.org/10.24018/ejece.2019.3.5.133
Wong, H.M., Amalathas, S.S., Zitkova, T.: A Business Operation Stability by Improving the Speed of Restoration on Software Application Service. David Publishing Company (2020). https://doi.org/10.17265/2328-2185/2020.01.008
Wong, H.M., Amalathas, S.S., Zitkova, T.: An approach towards designing a prescriptive analytical logic model for software application root cause analysis. Int. J. Adv. Res. Publ. (IJARP) (2019). http://www.ijarp.org/published-research-papers/nov2019/An-Approach-Towards-Designing-A-Prescriptive-Analytical-Logic-Model-For-Software-Application-Root-Cause-Analysis.pdf
Wong, H.M., Amalathas, S.: A new analysis approach incorporated with analytic hierarchy process for the software application in a multiple tiers environment. Eur. J. Electr. Eng. Comput. Scie. 3, 6 (2019). https://doi.org/10.24018/ejece.2019.3.6.160
Wong, H.M., Amalathas, S.: The root cause analysis algorithm design incorporated with analytic hierarchy process for software application error. Eur. J. Electr. Eng. Comput. Sci. 4, 1 (2020). https://doi.org/10.24018/ejece.2020.4.1.166
Peng, W.W., Dolores, R.: Wallace. Software Error Analysis. NIST Special Publication (1993). http://www.geocities.ws/itopsmat/SoftwareErrorAnalysis.pdf. Accessed 12 Dec 2015
Cmielowski, L.: Adoption of machine learning to software failure prediction. IBM Watson Data (2017). https://medium.com/ibm-data-science-experience/adoption-of-machine-learning-to-software-failure-prediction-e8d85ed0338f. Accessed 2 Apr 2019
Klass, L.: Machine Learning - Definition and application examples. MM International (2018). https://www.spotlightmetal.com/machine-learning–definition-and-application-examples-a-746226/?cmp=go-aw-art-trf-SLM_DSA-20180820&gclid=Cj0KCQjwxYLoBRCxARIsAEf16-vBblTDXGp3-EuK0AsxQu90ckOqGbTyXEvjQBWSA3D3MHB51TxDhqUaAlPDEALw_wcB. Accessed 21 Feb 2020
Brownlee, J.: Linear Regression for Machine Learning. Machine Learning Mastery Pty. Ltd. (2019). https://machinelearningmastery.com/linear-regression-for-machine-learning/. Accessed 24 Feb 2020
Guru99: Unsupervised Machine Learning: What is, Algorithms, Example (2020). www.guru99.com. https://www.guru99.com/unsupervised-machine-learning.html. Accessed 24 Feb 2020
Roman, V.: Unsupervised machine learning: clustering analysis. towards data science (2019). https://towardsdatascience.com/unsupervised-machine-learning-clustering-analysis-d40f2b34ae7e. Accessed 9 Mar 2020
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Wong, H.M., Amalathas, S.S. (2020). The Error Analysis for Enterprise Software Application Using Analytic Hierarchy Process and Supervised Learning: A Hybrid Approach on Root Cause Analysis. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1295. Springer, Cham. https://doi.org/10.1007/978-3-030-63319-6_59
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