International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

A Survey of Software Systems Anomaly States Prediction Based on Artificial Techniques

Abstract

Predicting software anomaly is essential for improving software quality and saving costs and time associated with software testing and maintenance in advanced stages life of software development. Complex software systems can be made more reliable and field failures reduced by having the ability to anticipate failures before they occur, as the field of forecasting has witnessed. Software defect prediction (SDP) has seen recent developments, such as combining several classification algorithms to create an ensemble or hybrid approach. Many laterals have been conducted in the field of predicting, including predicting anomalies and disruptions in performance and predicting software errors. This paper presents a study and review of the literature on predicting anomalies in software systems, strategies and methods for detecting them, and a brief overview of predicting defects and future trends in forecasting, as the results of the reviews showed. Because ensemble predictors can, in some cases, enhance bug detection performance, many problems have been solved by machine learning techniques as a result of this recent success.

Country : Iraq

1 Raghda Azad Hassan2 Ibrahim Ahmed Saleh

  1. Student, Department of Software, College of Computer & Math., University of Mosul, Iraq
  2. Professor, Department of Software, College of Computer & Math., University of Mosul, Iraq

IRJIET, Volume 8, Issue 3, March 2024 pp. 173-180

doi.org/10.47001/IRJIET/2024.803023

Full Paper
Download

References

  1. GUOLIANG. Z., SAFWAT. H., YING. Z., DEREK .T and TOBY. C, “Predicting Performance Anomalies in Software Systems at Run-time”, ACM Transactions on Software Engineering and Methodology 30(3), https://www.researchgate.net/publication/350099594, DOI: 10.1145/3440757, January 2020.
  2. Jasmeet. K., “A Study on Software Fault Detection Prediction in Software Engineering Activities”, ISSN-2349-5162, JETIR January 2024, Volume 11, Issue paradigm for sensor networks,” in Proc. ACM SIGMOBILE Int. Conf. Mobile Computer Network (MOBICOM), pp. 56–67, 2000.
  3. Jens. E, André .v. H, Jan .W and Wilhelm. H, “Self-Adaptive Software System Monitoring for Performance Anomaly Localization”, DOI:10.1145/1998582.1998628,ICAC '11: Proceedings of the 8th ACM international conference on Autonomic computing June 2011Pages 197–200.
  4. Fabian. H, Mojdeh .G, Ana. P, and Alexander .K, “Machine learning-based run-time anomaly detection in software systems: An industrial evaluation”, DOI:10.1109/MALTESQUE.2018.8368453, Conference: 2018 IEEE Workshop.
  5. Olumuyiwa. I, “Performance Anomaly Detection and Bottleneck Identification”, http://dx.doi.org/10.1145/2791120, ACM Computing Surveys 2015 Volume 48 Issue 1 Article No.: 4pp 1–35.
  6. Mohammad .A and Wan .H, “Predicting Software Defects using Machine Learning Techniques”, https://doi.org/10.30534/ijatcse/2020/352942020, volume 9 No 4 August 2020.
  7. FASEEHA. M, TAHER M. G, NASSER .T, SHABIB. A, MUNIR .A, MUHAMMAD .A. K, SAGHEER .A and AND .T, “Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review”, Digital Object Identifier 10.1109/ACCESS.2021.3095559, VOLUME 9, 2021.
  8. Fatih. Y, Akin .O, Emin .B and Deniz .K, “Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability”, https://doi.org/10.1016/j.jestch.2019.10.005 ,Volume 23, Issue 4, August 2020, Pages 938-950.
  9. Donghun. L, Sang. k, Member, IEEE, and Arthur “A Performance Anomaly Detection and Analysis Framework for DBMS Development”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 8, AUGUST 2012.
  10. Santosh S. R, Sandeep K, “A study on software fault prediction techniques”, DOI 10.1007/s10462-017-9563-5, Article Published: 30 May 2017 Volume 51, pages 255–327, (2019).
  11. Ashraf A and Nagy R, “Early Prediction of Software Defect using Ensemble Learning: A Comparative Study”, International Journal of Computer Applications (0975 – 8887) Volume 179 – No.46, June 2018.
  12. Faseeha Matloob, Shabib Aftab and Ahmed Iqbal, “A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques”, DOI: 10.5815/ijmecs.2019.12.02, Published Online December 2019 in MECS (http://www.mecs-press.org/).
  13. YUE. T, CHUNJING. H, KUAN. Z, ETHAN A, and JAE .S. P, “LSTM-Based Anomaly Detection for Non-Linear Dynamical System”, Digital Object Identifier 10.1109/ACCESS.2020.2999065, VOLUME 8, 2020.
  14. Elena. N. A, Alexander Yu. B, Artem A. D, Konstantin. S. K, Anton .V. K, Ilya. P. M and Vladimir .E. M, “A Survey on Software Defect Prediction Using Deep Learning”, https://doi.org/10.3390/math9111180, 2021, MSC: 68N30; 68T07.
  15. Yu Tang, • Qi Dai, Tony Du and • Lifang Chen, “Software defect prediction ensemble learning algorithm based on adaptive variable sparrow search algorithm” , International Journal of Machine Learning and Cybernetics https://doi.org/10.1007/s13042-022-01740-2.
  16. Tao S., Zhuoliang Z and Jun A, “Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model”, Appl. Sci. 2023, 13, 11349. https://doi.org/10.3390/app132011349.
  17. Ruyue X, Hongyun L, Peng C, and Zhiming Z, “Robust and accurate performance anomaly detection and prediction for cloud applications: a novel ensemble learning-based framework”, https://doi.org/10.1186/s13677-022-00383-62023.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies