Gradient integrated regression sustainable approach with machine learning towards software quality assurance
Abstract
With the increase of enormous software suites and modules, there is a need to evaluate the cumulative quality and overall performance of newly developed modules using advanced algorithms and approaches including machine learning, statistical analysis, deep learning, and many others. In this work, the software quality assurance with the dataset’s evaluation using statistical analysis and regression integrated approach is presented. The aim is to acquire a much-enhanced acceptance of the subject matter by providing an investigation of software quality analysis using regression which is done in the last fifteen or twenty years. The combination of applying regression technique along with machine learning to outline its application sphere of influence, the type of metrics used, the application strategy, and the stage of the software development progression wherever they are useful. An outcome on or after going through around five hundred papers, a set of around more than fifty papers are unfolding the use of more than thirty software quality analysis can be identified. On the other hand, the lowest amount is given to maintain and apply software regression techniques in the industry and education. The graphical representation and the results showing the good and innovative performance of the gradient integrated regression approach with machine learning technology for increasing the quality of software.
Keywords
Full Text:
PDFReferences
1. Lewis WE, Dobbs D, Veerapillai G. Software Testing and Continuous Quality Improvement. Auerbach Publications; 2017. doi: 10.1201/9781439834367
2. Goyal LM, Mittal M, Sethi JK. Fuzzy model generation using Subtractive and Fuzzy C-Means clustering. CSI Transactions on ICT. 2016; 4(2-4): 129-133. doi: 10.1007/s40012-016-0090-3
3. Takanen A, Demott JD, Miller C, Kettunen A. Fuzzing for software security testing and quality assurance. Artech House. 2018.
4. Odzaly EE, Greer D, Stewart D. Agile risk management using software agents. Journal of Ambient Intelligence and Humanized Computing. 2017; 9(3): 823-841. doi: 10.1007/s12652-017-0488-2
5. Palma SD, Di Nucci D, Palomba F, Tamburri DA. Towards a Catalogue of Software Quality Metrics for Infrastructure Code. Journal of Systems and Software. 2020; 170: 110726.
6. Zavvar M, Yavari A, Mirhassanni SM, et al. Classification of risk in software development projects using support vector machine. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 2017; 9(1): 1-5.
7. Ghobadi S, Mathiassen L. Risks to Effective Knowledge Sharing in Agile Software Teams: A Model for Assessing and Mitigating Risks. Information Systems Journal. 2016; 27(6): 699-731. doi: 10.1111/isj.12117
8. Lu F, Bi H, Huang M, et al. Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Project. Mathematical Problems in Engineering. 2017; 2017: 1-17. doi: 10.1155/2017/6916575
9. Méndez Fernández D, Tießler M, Kalinowski M, et al. On Evidence-based Risk Management in Requirements Engineering. arXiv. 2017.
10. Rauter T, Höller A, Kajtazovic N, Kreiner C. Asset-Centric Security Risk Assessment of Software Components. In: Workshop on MILS: Architecture and Assurance for Secure Systems; 2016.
11. Elzamly A, Hussin B. Managing Software Project Risks (Analysis Phase) with Proposed Fuzzy Regression Analysis Modelling Techniques with Fuzzy Concepts. Journal of Computing and Information Technology. 2014; 22(2): 131. doi: 10.2498/cit.1002324
12. Krishna BC, Subrahmanyam K. A Decision Support System for Assessing risk using Halstead approach and Principal Component Analysis. Journal of Chemical and Pharmaceutical Sciences. 2016; 9(4): 3383–3387.
13. Purandare P. An entropy-based approach for risk factor analysis in a software development project. International Journal of Applied Engineering Research. 2016; 11(4): 2258-2262.
14. Choetkiertikul M, Dam HK, Tran T, et al. Predicting Delays in Software Projects Using Networked Classification (T). In: 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE). Published online November 2015. doi: 10.1109/ase.2015.55
15. Elzamly A, Hussin B, Abu-Naser SS, Doheir M. Predicting Software Analysis Process Risks Using Linear Stepwise Discriminant Analysis: Statistical Methods.Int. J. Adv. Inf. Sci. Technol. 2015; 38: 108–115.
16. Mohanty R, Ravi V. Machine Learning Techniques to Predict Software Defect. Artificial Intelligence.: 1473-1487. doi: 10.4018/978-1-5225-1759-7.ch059
17. Singh R, Raja R, Chopra J. Software Defect Prediction Using Averaging Likelihood Ensemble Technique. International Journal. 2017; 4: 213-223.
18. Ali MM, Huda S, Abawajy J, et al. A parallel framework for software defect detection and metric selection on cloud computing. Cluster Computing. 2017; 20(3): 2267-2281. doi: 10.1007/s10586-017-0892-6
19. Osman H, Ghafari M, Nierstrasz O. Automatic feature selection by regularization to improve bug prediction accuracy. 2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE). Published online February 21, 2017. doi: 10.1109/maltesque.2017.7882013
20. Maddipati SS, Pradeepini G, Yesubabu A. Software Defect Prediction using Adaptive Neuro Fuzzy Inference System. International Journal of Applied Engineering Research. 2018; 13(1): 394-397.
21. Mittal M, Sharma RK, Singh VP. Modified single pass clustering with variable threshold approach. International Journal of Innovative Computing Information and Control. 2015; 11(1): 375-386.
22. Xuan J, Jiang H, Hu Y, et al. Towards Effective Bug Triage with Software Data Reduction Techniques. IEEE Transactions on Knowledge and Data Engineering. 2015; 27(1): 264-280. doi: 10.1109/tkde.2014.2324590
23. Neufelder AM. Ensuring Software Reliability. Published online October 8, 2018. doi: 10.1201/9781315217758
24. Van Deursen A, Aniche M, Boone C, et al. Software Quality and Testing. Delft University of Technology. 2019.
25. Leicht N, Blohm I, Leimeister JM. Leveraging the Power of the Crowd for Software Testing. IEEE Software. 2017; 34(2): 62-69. doi: 10.1109/ms.2017.37
26. Mittal M, Balas VE, Goyal LM, et al. Big Data Processing Using Spark in Cloud. Springer Singapore; 2019. doi: 10.1007/978-981-13-0550-4
27. Abdelrafe MS. Managing Software Project Risks Using Stepwise and Fuzzy Regression Analysis Modeling Techniques. 2016.
28. Elzamly, Hussin B, Naser SSA, Doheir M. Predicting Software Analysis Process Risks Using Linear Stepwise Discriminant Analysis: Statistical Methods. Int. J. Adv. Inf. Sci. Technol. 2015; 38: 108–115.
29. Yucalar F, Ozcift A, Borandag E, et al. Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability. Engineering Science and Technology, an International Journal. 2020; 23(4): 938-950. doi: 10.1016/j.jestch.2019.10.005
30. Rothermel G. Improving regression testing in continuous integration development environments (keynote). Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation. Published online November 5, 2018. doi: 10.1145/3278186.3281454
31. Podgorny IA, Cessna J, Gielow CC, Cannon M. U.S. Patent No. 10,162,734. Washington, DC: U.S. Patent and Trademark Office. 2018.
32. Mittal M, Sharma RK, Singh VP. Validation of k-means and threshold based clustering method. International Journal of Advancements in Technology. 2014; 5(2): 153-160.
33. Podgorny IA, Cessna J, Gielow CC, Cannon M. US Patent 10162734 Method and system for crowdsourcing software quality testing and error detection in a tax return preparation system. U.S. Patent No. 10,162,734, 25 December 2018.
34. Felderer M, Fourneret E. A systematic classification of security regression testing approaches. International Journal on Software Tools for Technology Transfer. 2015; 17(3): 305-319. doi: 10.1007/s10009-015-0365-2
35. Aggarwal A, Dhindsa KS, Suri PK. A Pragmatic Assessment of Approaches and Paradigms in Software Risk Management Frameworks. International Journal of Natural Computing Research. 2020; 9(1): 13-26. doi: 10.4018/ijncr.2020010102
36. Malik M, Prabha C, Soni P, et al. Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities. International Journal on Semantic Web and Information Systems (IJSWIS). 2023; 19(1): 1-20. doi: 10.4018/IJSWIS.324105
37. Paliwal M, Soni P, Chauhan S. Digit Recognition using the Artificial Neural Network. In: 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT); 5–6 May 2023; Gharuan, India. pp. 145-149. doi: 10.1109/InCACCT57535.2023.10141703
DOI: https://doi.org/10.32629/jai.v7i5.1510
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Alankrita Aggarwal, Vijay Bhardwaj, Sandeep Singh Bindra, Rajender Kumar, Preet Kamal
License URL: https://creativecommons.org/licenses/by-nc/4.0/