SOFTWARE MAINTENANCE ASSESSMENT: AN ANALYSIS OF DETERMINATION FACTORS
DOI:
https://doi.org/10.46754/jmsi.2025.06.006Keywords:
Software maintenance, Software maintenance assessment, Software process assessment, Assessment factor, Determination factorAbstract
Software maintenance is the longest and most expensive software development life cycle (SDLC) phase. It contributes a major part of a software system's total life cycle cost. An effective software maintenance assessment is crucial to ensure the software system's longevity and efficiency. The effective assessment will provide valuable insight into the decision-making guidance and foster continuous improvement. However, the factors that influence the assessment process are not ideally mapped with the existing software maintenance process. To provide effective planning and resource allocation, the software maintenance team needs to determine the factors that influence the process. This paper presents a review of existing literature to identify and categorise the determination factors that influence the software maintenance assessment. Literature studies show that the key factors can be identified and grouped into several categories. The analysis reveals factors that influence the software maintenance assessment, which is categorised into project characteristics, development teams' skills, client involvement, project constraint, organisational culture, and documentation. The determination factors will help the software maintenance team assess the priorities of their efforts and allocate the available resources efficiently. This paper contributes valuable knowledge on the assessment process and offers useful guidance for practitioners looking to improve their software maintenance efforts.
References
Almogahed, A., Mahdin, H., Omar, M., Zakaria, N. H., Mostafa, S. A., AlQahtani, S. A., Pathak, P., Shaharudin, S. M., & Hidayat, R. (2023). A refactoring classification framework for efficient software maintenance. IEEE Access, 11, 78904-78917. https://doi.org/10.1109/access.2023.3298678 DOI: https://doi.org/10.1109/ACCESS.2023.3298678
Ikram, A., Riaz, H., & Salman Khan, A. (2018). Eliciting theory of software maintenance outsourcing process: A systematic literature review. International Journal of Computer Science and Network Security, 18(4), 132-143.
Rehman, F. U., Maqbool, B., Riaz, M. Q., Qamar, U., & Abbas, M. (2018). Scrum software maintenance model: Efficient software maintenance in agile methodology. 2018 21st Saudi Computer Society National Computer Conference (NCC), Riyadh, Saudi Arabia, 2018 (pp. 1-5). https://doi.org/10.1109/ncg.2018.8593152 DOI: https://doi.org/10.1109/NCG.2018.8593152
Levin, S., & Yehudai, A. (2019). Visually exploring software maintenance activities. 2019 Working Conference on Software Visualization (VISSOFT), Cleveland, OH, USA, 2019 (pp. 110-114). https://doi.org/10.1109/VISSOFT.2019.00021 DOI: https://doi.org/10.1109/VISSOFT.2019.00021
Ikram, A., Jail, M. A., Ngah, A., & Khan, A. S. (2020). Towards offshore software maintenance outsourcing process model. International Journal of Computer Science and Network Security, 20(4), 6-14.
Almashhadani, M., Mishra, A., Yazici, A., & Younas, M. (2023). Challenges in agile software maintenance for local and global development: An empirical assessment. Information, 14(5), Article 261. https://doi.org/10.3390/info14050261 DOI: https://doi.org/10.3390/info14050261
Araujo, J., Melo, C., Oliveira, F., Pereira, P. A. A., & Matos, R. (2021). A software maintenance methodology: An approach applied to software aging. 2022 IEEE International Systems Conference (SysCon), 1-8. https://doi.org/10.1109/syscon48628.2021.9447082 DOI: https://doi.org/10.1109/SysCon48628.2021.9447082
Stojanov, Z. (2021). Software maintenance improvement in small software companies: Reflections on experiences. CEUR Workshop Proceedings, vol. 2913, pp. 182-197. https://doi.org/10.47350/iccs-de.2021.14 DOI: https://doi.org/10.47350/ICCS-DE.2021.14
Fernández-Sáez, A. M., Chaudron, M. R. V., & Genero, M. (2018). An industrial case study on the use of UML in software maintenance and its perceived benefits and hurdles. Empirical Software Engineering, 23(6), 3281-3345. https://doi.org/10.1007/s10664-018-9599-4 DOI: https://doi.org/10.1007/s10664-018-9599-4
Hassan, S. I., & Khan, A. S. (2017). Eliciting theory for software maintenance SLA management framework. 2017 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan (pp. 241-246). https://doi.org/10.1109/fit.2017.00050 DOI: https://doi.org/10.1109/FIT.2017.00050
Ali, S. S., Zafar, M. S., & Saeed, M. T. (2020). Effort estimation problems in software maintenance - A survey. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), January 2020. https://doi.org/10.1109/icomet48670.2020.9073823 DOI: https://doi.org/10.1109/iCoMET48670.2020.9073823
Masrat, A., Makki, M. A., & Gawde, H. (2021). Software maintenance models and processes: An overview. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3838444 DOI: https://doi.org/10.2139/ssrn.3838444
Stojanov, Z., Dobrilovic, D., & Stojanov, J. (2013). Analyzing trends for maintenance request process assessment: Empirical investigation in a very small software company. Theory and Applications of Mathematics & Computer Science, 3(2), 59-74.
Morgan, H. (2022). Conducting a qualitative document analysis. The Qualitative Report, 27(1), 64-77. https://doi.org/10.46743/2160-3715/2022.5044 DOI: https://doi.org/10.46743/2160-3715/2022.5044
Baharuddin, M. F., Masrek, M. N., Shuhidan, S. M., Razali, M. H. B. H., & Rahman, M. S. (2021). Evaluating the content validity of digital literacy instrument for school teachers in Malaysia through expert judgement. International Journal of Emerging Technology and Advanced Engineering, 11(7), 71-78. https://doi.org/10.46338/ijetae0721_09 DOI: https://doi.org/10.46338/ijetae0721_09
Beecham, S., Hall, T., Britton, C., Cottee, M., & Rainer, A. (2004). Using an expert panel to validate a requirements process improvement model. Journal of Systems and Software, 76(3), 251-275. https://doi.org/10.1016/j.jss.2004.06.004 DOI: https://doi.org/10.1016/j.jss.2004.06.004
Ramanujan, S., & Nerur, S. (2009). An exploratory analysis of the state of software maintenance research: An author co-citation analysis. Journal of Systems and Information Technology, 11(2), 117-130. https://doi.org/10.1108/13287260910955093 DOI: https://doi.org/10.1108/13287260910955093
Hönel, S., Ericsson, M., Löwe, W., & Wingkvist, A. (2020). Using source code density to improve the accuracy of automatic commit classification into maintenance activities. Journal of Systems and Software, 168, Article 110673. https://doi.org/10.1016/j.jss.2020.110673 DOI: https://doi.org/10.1016/j.jss.2020.110673
Ulziit, B., Warraich, Z. A., Gencel, C., & Petersen, K. (2015). A conceptual framework of challenges and solutions for managing global software maintenance. Journal of Software Evolution and Process, 27(10), 763-792. https://doi.org/10.1002/smr.1720 DOI: https://doi.org/10.1002/smr.1720
Nguyen, V., Boehm, B., & Danphitsanuphan, P. (2010). A controlled experiment in assessing and estimating software maintenance tasks. Information and Software Technology, 53(6), 682-691. https://doi.org/10.1016/j.infsof.2010.11.003 DOI: https://doi.org/10.1016/j.infsof.2010.11.003
Ferreira, M., Bigonha, M., & Ferreira, K. A. M. (2021). On the gap between software maintenance theory and practitioners’ approaches. Proceedings 2021 IEEE/ACM 8th International Workshop on Software Engineering Research and Industrial Practice (SER&IP), Madrid, Spain (pp. 41-48). https://doi.org/10.1109/ser-ip52554.2021.00015 DOI: https://doi.org/10.1109/SER-IP52554.2021.00015
Rahman, H. U., Raza, M., Alzayed, A., Afsar, P., Alharbi, A., Alosaimi, W., & Khan, U. H. (2022). OffshoringDSS: An automated tool of application maintenance offshoring. Applied Sciences, 12(21), Article 10913. https://doi.org/10.3390/app122110913 DOI: https://doi.org/10.3390/app122110913
Al Rababah, A., & Alzahrani, A. A. (2019). Software Maintenance Model through the Development Distinct Stages. International Journal of Computer Science and Network Security, 19(2), 23-28. http://paper.ijcsns.org/07_book/201902/20190204.pdf
Demelo, A., & Sanchez, A. (2006). Software maintenance project delays prediction using Bayesian Networks. Expert Systems With Applications, 34(2), 908-919. https://doi.org/10.1016/j.eswa.2006.10.040 DOI: https://doi.org/10.1016/j.eswa.2006.10.040
Wang, A. Y., Wang, D., Drozdal, J., Muller, M., Park, S., Weisz, J. D., Liu, X., Wu, L., & Dugan, C. (2022). Documentation matters: Human-Centered AI system to assist data science code documentation in computational notebooks. ACM Transactions on Computer-Human Interaction, 29(2), 1-33. https://doi.org/10.1145/3489465 DOI: https://doi.org/10.1145/3489465
Khan, J. Y., & Uddin, G. (2022). Automatic Code Documentation Generation Using GPT-3. ACM International Conference on Automated Software Engineering. https://doi.org/10.1145/3551349.3559548 DOI: https://doi.org/10.1145/3551349.3559548
Aghajani, E., Nagy, C., Linares-Vásquez, M., Moreno, L., Bavota, G., Lanza, M., & Shepherd, D. C. (2020). Software documentation: The practitioners’ perspective. ICSE ’20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (pp. 590-601). https://doi.org/10.1145/3377811.3380405 DOI: https://doi.org/10.1145/3377811.3380405
Bourrie, D. M., Cegielski, C. G., Jones‐Farmer, L. A., & Sankar, C. S. (2014). Identifying characteristics of dissemination success using an expert panel. Decision Sciences Journal of Innovative Education, 12(4), 357-380. https://doi.org/10.1111/dsji.12049 DOI: https://doi.org/10.1111/dsji.12049
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Mathematical Sciences and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
