Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning

Purvi Sankhe; Mukesh Dixit

doi:10.17485/IJST/v17i19.833

Article

Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i19.833

Year: 2024, Volume: 17, Issue: 19, Pages: 1983-1993

Original Article

Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning

Purvi Sankhe^1*, Mukesh Dixit²

¹Research Scholar, Sanjeev Agrawal Global Educational University, Bhopal, Madhya Pradesh, India
²Associate Professor, Sanjeev Agrawal Global Educational University, Bhopal, Madhya Pradesh, India

*Corresponding Author
Email: [email protected]

Received Date:19 March 2024, Accepted Date:12 April 2024, Published Date:09 May 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: To create an AI-powered recommendation system that is designed for IT professionals to help them choose the best software development approaches. Through the use of specified data parameters. Methods: The recommendation system will make use of machine learning algorithms and data analysis methods to examine team dynamics, project needs, and other variables. The technology will enable developers to improve the quality of products and speed up the development process by recommending suitable development methodologies. Data parameters considered for the development of the recommendation model fall into four categories: requirements, user involvement, development team, type of project, and risk associated with it. Findings: Existing recommendation systems developed by different researchers are applicable for only requirement elicitation and to recommend different phases of the development process, whereas systems that will help select development methodology are not available in the existing systems. Among the five machine learning algorithms applied in the recommender system building process, the DecisionTree Classifier and RandomForest Classifier exhibit superior performance, achieving 100% accuracy, while the Kneighbors Classifier indicates 94.74% accuracy. Novelty: This study of systems introduces a novel approach to software development methodology, a recommender system, which helps IT developers select the best appropriate development approach for the development of a software product or project based on the type of project to be built and other data parameters.

Keywords: Agile, Development, Requirements, Methodology, User, Customer

References

Mishra A, Alzoubi YI. Structured software development versus agile software development: a comparative analysis. International Journal of System Assurance Engineering and Management. 2023;14(4):1504–1522. Available from: https://dx.doi.org/10.1007/s13198-023-01958-5
Edison H, Wang X, Conboy K. Comparing Methods for Large-Scale Agile Software Development: A Systematic Literature Review. IEEE Transactions on Software Engineering. 2022;48(8):2709–2731. Available from: https://dx.doi.org/10.1109/tse.2021.3069039
Behrens A, Ofori M, Noteboom C, Bishop D. A systematic literature review: how agile is agile project management? Issues In Information Systems. 2021;22(3):278–295. Available from: https://iacis.org/iis/2021/3_iis_2021_298-316.pdf
Akram F, Ahmad T, Sadiq M. Recommendation systems-based software requirements elicitation process—a systematic literature review. Journal of Engineering and Applied Science. 2024;71(1):1–21. Available from: https://dx.doi.org/10.1186/s44147-024-00363-4
Rocco JD, Ruscio DD, Sipio CD, Nguyen PT, Rubei R. Development of recommendation systems for software engineering: the CROSSMINER experience. Empirical Software Engineering. 2021;26(4):1–40. Available from: https://dx.doi.org/10.1007/s10664-021-09963-7
Herawati S, Negara YDP, Febriansyah HF, Fatah DA. Application of the Waterfall Method on a Web-Based Job Training Management Information System at Trunojoyo University Madura. In: International Conference on Science and Technology (ICST 2021), E3S Web of Conferences. (Vol. 328, pp. 1-6) EDP Sciences. 2021.
Bianchi MJ, Conforto EC, Rebentisch E, Amaral DC, Rezende SO, Pádua Rd. Recommendation of Project Management Practices: A Contribution to Hybrid Models. IEEE Transactions on Engineering Management. 2022;69(6):3558 –3571. Available from: https://doi.org/10.1109/TEM.2021.3101179
Barata SFPG, Ferreira FAF, Carayannis EG, Ferreira JJM. Determinants of E-Commerce, Artificial Intelligence, and Agile Methods in Small- and Medium-Sized Enterprises. IEEE Transactions on Engineering Management. 2023;71:6903 –6917. Available from: https://doi.org/10.1109/TEM.2023.3269601
Sankhe P, Dixit M, . A Review and Survey of Software Development Methodologies. International Journal of Creative Reasearch Thoughts. 2023;11(5):e726–e733. Available from: https://ijcrt.org/papers/IJCRT2305598.pdf
Sankhe P, Mathur S, Rehman TB, Dixit M. Review of an Agile Software Development Methodology with SCRUM & Extreme Programming. In: 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET). (pp. 5415-5420) IEEE. 2023.
Bianchi M, Marzi G, Dabic M. Guest Editorial: Agile Beyond Software—In Search of Flexibility in a Wide Range of Innovation Projects and Industries. IEEE Transactions on Engineering Management. 2022;69(6):3454–3458. Available from: https://dx.doi.org/10.1109/tem.2022.3206408
Kuhrmann M, Tell P, Hebig R, Klunder J, Munch J, Linssen O, et al. What Makes Agile Software Development Agile? IEEE Transactions on Software Engineering. 2022;48(9):3523–3539. Available from: https://dx.doi.org/10.1109/tse.2021.3099532
Kasauli R, Knauss E, Horkoff J, Liebel G, Neto FGdO. Requirements engineering challenges and practices in large-scale agile system development. Journal of Systems and Software. 2021;172:1–26. Available from: https://dx.doi.org/10.1016/j.jss.2020.110851
Ghimire D, Charters S. The Impact of Agile Development Practices on Project Outcomes. Software. 2022;1(3):265–275. Available from: https://dx.doi.org/10.3390/software1030012
Gurung G, Shah R, Jaiswal DP. Software Development Life Cycle Models-A Comparative Study. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2020;6(4):30–37. Available from: https://dx.doi.org/10.32628/cseit206410
Lunesu MI, Tonelli R, Marchesi L, Marchesi M. Assessing the Risk of Software Development in Agile Methodologies Using Simulation. IEEE Access. 2021;9:134240 –134258. Available from: https://doi.org/10.1109/ACCESS.2021.3115941
Hanslo R, Tanner M. Machine Learning models to predict Agile Methodology adoption. In: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems. (pp. 697-704) IEEE. 2020.
Jain H, Khunteta A, Srivastava S. Churn Prediction in Telecommunication using Logistic Regression and Logit Boost. Procedia Computer Science. 2020;167:101–112. Available from: https://dx.doi.org/10.1016/j.procs.2020.03.187
Suyal M, Goyal P. A Review on Analysis of K-Nearest Neighbor Classification Machine Learning Algorithms based on Supervised Learning. International Journal of Engineering Trends and Technology. 2022;70(7):43–48. Available from: https://dx.doi.org/10.14445/22315381/ijett-v70i7p205
Gadekallu TR, Khare N, Bhattacharya S, Singh S, Maddikunta PKR, Ra IH, et al. Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model. Electronics. 2020;9(2):1–16. Available from: https://dx.doi.org/10.3390/electronics9020274
Mrva J, Neupauer S, Hudec L, Sevcech J, Kapec P. Decision Support in Medical Data Using 3D Decision Tree Visualisation. In: 2019 E-Health and Bioengineering Conference (EHB). (pp. 1-4) IEEE. 2020.
Sarker IH, Kayes ASM, Watters P. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. Journal of Big Data. 2019;6(1):1–28. Available from: https://dx.doi.org/10.1186/s40537-019-0219-y
Jalal N, Mehmood A, Choi GS, Ashraf I. A novel improved random forest for text classification using feature ranking and optimal number of trees. Journal of King Saud University - Computer and Information Sciences. 2022;34(6, Part A):2733–2742. Available from: https://dx.doi.org/10.1016/j.jksuci.2022.03.012
Wei L, Capretz LF. Recommender Systems for Software Project Managers. In: Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering. (pp. 412-417) ACM. 2021.

Copyright

© 2024 Sankhe & Dixit. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)