EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 81 (2026) ITM Web Conf., 81 (2026) 01005 References
Open Access
Issue
ITM Web Conf.
Volume 81, 2026
International Conference on Emerging Technologies for Multidisciplinary Innovation and Sustainability (ETMIS 2025)
Article Number 01005
Number of page(s) 16
DOI https://doi.org/10.1051/itmconf/20268101005
Published online 23 January 2026
  1. N. Fenton. Software Metrics: A Rigorous and Practical Approach. Chapman & Hall. (1997). [Google Scholar]
  2. Y. Wang. On cognitive complexity of software and its measurement. Inter. Jr. of Cog. Informatics and Nat. Intel., 1(4), 17–36. (2007). [Google Scholar]
  3. S. Misra, I. Akman. Cognitive complexity metrics and their empirical evaluation. Jr. of Comp. Sci., 4(9), 707–713. (2008). [Google Scholar]
  4. J. K. Chhabra. Cognitive complexity measure of source code. ACM SIGSOFT Soft. Eng. Notes, 36(1), 1–6. (2011). [Google Scholar]
  5. Y. Wang. Cognitive informatics foundations of software engineering. Springer. (2009). [Google Scholar]
  6. R. Minelli, A. Mocci, M. Lanza. I know what you did last summer: An investigation of how developers spend their time. 23rd Intern. Conf. on Program Comprehension, 25–35; (2015). https://doi.org/10.1109/ICPC.2015.12. [Google Scholar]
  7. X. Xia et al. Measuring program comprehension: A large-scale field study with professionals. IEEE Transactions on Soft. Eng., 44, 10, 951–976, (2018). https://doi.org/10.1109/TSE.2017.2734091. [Google Scholar]
  8. G. D. Feitelson. From Code Complexity Metrics to Program Comprehension, Communications of the ACM, 66 (5), 52–61. (2023). https://doi.org/10.1145/3546576 [Google Scholar]
  9. J. Pantiuchina, J. M. Lanza, G. Bavota. The (mis) perception of quality metrics. In Intern. Conf. on Software Maintenance and Evolution, 80–91, (2018). https://doi.org/10.1109/ICSME.2018.00017. [Google Scholar]
  10. S. Scalabrino et al. Automatically Assessing code understandability. IEEE Transactions on Soft. Eng., 47, 3, 595–613, (2021). https://doi.org/10.1109/TSE.2019.2901468. [Google Scholar]
  11. T. J. McCabe. A complexity measure. IEEE Transactions on Soft. Eng., 2(4), 308–320. (1976). https://doi.org/10.1109/TSE. 1976.233837. [Google Scholar]
  12. M. H. Halstead Elements of Software Science. Elsevier North-Holland. (1977). [Google Scholar]
  13. K. Rim & Y. Choe. Scope information complexity number: A measure for cognitive complexity. Info. and Soft. Techn., 49(11-12), 1160–1170. (2007). [Google Scholar]
  14. R. Tiwari, S. Kaur, M. Gupta. Predicting code comprehension using neural networks. Jr. of Systems and Soft., 158, 110420. (2019). [Google Scholar]
  15. K. Amandeep, D. Sharma. Machine learning approaches to predict cognitive load in software comprehension. Applied Soft Computing, 108, 107421. https://doi.org/10.3390/make7020051. (2021). [Google Scholar]
  16. N. Ali, R. Al-Qutaish, M. Ahmad. Software complexity measurement: A review. International Journal of Advanced Comp. Sci., 11(6), 120–134. (2020). [Google Scholar]
  17. G. Bavota. Code comprehension: A survey of cognitive models and empirical results. ACM Computing Surveys, 54, 9, 1–39. (2022). [CrossRef] [Google Scholar]
  18. U. Onwudebelu, O. G. Igbinosa, C. U. Ugwoke. The Use of a Collegiate Software Exhibition & Competition in Software Development Education, World Journal of Computer Application and Technology (WJCAT), USA, 1(1): 6–9, (2013). https://doi.org/10.13189/wicat.2013.010102 [Google Scholar]
  19. M. K. Aregbesola, U. Onwudebelu. Experimental Evaluation of Software Quality Management and Assurance in the Nigerian Software Industry, Intern. Jr. of Latest Techn. in Eng., Managt. & Applied Sci. (IJLTEMAS), 8, 7, 77–82, ISSN 2278-2540. (2019) [Google Scholar]
  20. M. K. Aregbesola, U. Onwudebelu. Typical Software Quality Assurance and Quality Management Issues in the Nigerian Software Industry. The National Association for Science, Humanities and Education Research (NASHER) 8th Annual National Conference. September 14th-17th, 2011, pp. 107-113. (2011). [Google Scholar]
  21. S. R. Chidamber, C. F. Kemerer. A metric suite for object oriented design. IEEE Transactions on Soft. Eng., 20, 6, 476–493; https://doi.org/10.1109/32.295895. (1994). [Google Scholar]
  22. Politowski, C. et al. (2020) A large scale empirical study of the impact of Spaghetti Code and Blob anti-patterns on program comprehension. Information and Software Technology, 122; https://doi.org/10.1016/j.infsof.2020.106278. [Google Scholar]
  23. T. Sharma, D. Spinellis. A survey of code smells. J. of Systems and Software, 138, 158 173; https://doi.org/10.1016/i.iss.2017.12.034. (2018). [Google Scholar]
  24. Y. Gil, G. Lalouche. On the correlation between size and metric validity. Empirical Soft. Eng., 22, 5, 2585–2611; https://doi.org/10.1007/s10664-017-9513-5. (2017). [Google Scholar]
  25. O. Levy, D. G. Feitelson. Understanding large-scale software systems—Structure and flows. Empirical Soft Eng., 26, 3; https://doi.org/10.1007/s10664-021-09938-8. (2021) [Google Scholar]
  26. S. Abukhalaf, M. Hamdaqa, F. Khomh. On Codex prompt engineering for OCL generation: an empirical study. In 2023 IEEE/ACM 20th Intern. Conf. on Mining Soft. Repositories (MSR). IEEE, 148–157. (2023). https://doi.org/10.1109/MSR59073.2023.00033. [Google Scholar]
  27. S. Abukhalaf, M. Hamdaqa, F. Khomh. PathOCL: path-based prompt augmentation for OCL generation with GPT-4. In Proc. of the 2024 IEEE/ACM First Intern. Conf. on AI Found. Models and Soft. Eng. 108–118. (2024). https://doi.org/10.1145/3650105.3652290. [Google Scholar]
  28. L. A. Agrawal, A. Kanade, N. Goyal, S. Lahiri, S. Rajamani. Monitor-guided decoding of code LMS with static analysis of repository context. Advances in Neural Info. Proc. Systs, 36, 32270–32298. (2023). [Google Scholar]
  29. B. Wang, Z. Wang, X. Wang, Y. Cao, R. A. Saurous, Y. Kim. Grammar prompting for domain-specific language generation with large language models. Advances in Neural Info. Proc. Systs, 36, 65030–65055. (2023). https://doi.org/10.48550/arXiv.2305.19234. [Google Scholar]
  30. M. DeLorenzo et al. Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS. arXiv:2402.03289 [cs.LG] (2024). [Google Scholar]
  31. J. Wang. Guiding Large Language Models to Generate Computer-Parsable Content. arXiv:2404.05499 [cs.SE]. (2024). [Google Scholar]
  32. B. Athiwaratkun, et al. (2023). Multi-lingual evaluation of code generation models. https://www.amazon.science/publications/multi-lingualevaluation-of-code-generation-models [Google Scholar]
  33. Y. Wang. Cognitive informatics: The new science of information and intelligence. IGI Global. (2003). [Google Scholar]
  34. J. Sweller. Cognitive load theory and its application to computer programming. Edu. Psy. Review, 31(2), 261–278. (2019). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.