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Towards the Measurement of Mental Effort in Software Engineering: A Research Agenda

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Lucian Goncales, Kleinner Farias

Lucian Goncales and Kleinner Farias. Towards the Measurement of Mental Effort in Software Engineering: A Research Agenda. International Journal of Computer Applications 177(34):1-8, January 2020. BibTeX

	author = {Lucian Goncales and Kleinner Farias},
	title = {Towards the Measurement of Mental Effort in Software Engineering: A Research Agenda},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2020},
	volume = {177},
	number = {34},
	month = {Jan},
	year = {2020},
	issn = {0975-8887},
	pages = {1-8},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2020919825},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Cognitive load refers to the mental effort applied to perform cognitive processes. In software engineering, developers are involved in cognitive processes such as program comprehension and change tasks. Measuring cognitive load would be a human-centered solution, instead of using measurements based on artifacts which have been shown to have no correlation with developers’ perception. Therefore, evaluate the cognitive load of the developer has potential to leverage the identification of source code issues and also improve the developers experience with their work environment. To determine a potential searcher to identify and organize this article a research agenda in relation to the measure of cognitive load of developers. This article also discusses the implications of using the cognitive load as a multipurpose indicator in software engineering. Finally, this article provides for practitioners and researchers a way to advance in the research about developers’ cognitive load in software engineering in realistic scenarios.


  1. Annushree Bablani, Damodar Reddy Edla, Diwakar Tripathi, and Ramalingaswamy Cheruku. Survey on brain-computer interface: An emerging computational intelligence paradigm. ACM Comput. Surv., 52(1):20:1–20:32, February 2019.
  2. Anthony D Bateson, Heidi A Baseler, Kevin S Paulson, Fayyaz Ahmed, and Aziz UR Asghar. Categorisation of mobile eeg: A researchers perspective. BioMed research international, 2017, 2017.
  3. Jorge Blasco, Thomas M. Chen, Juan Tapiador, and Pedro Peris-Lopez. A survey of wearable biometric recognition systems. ACM Comput. Surv., 49(3):43:1–43:35, September 2016.
  4. Daniel Cernea, Peter-Scott Olech, Achim Ebert, and Andreas Kerren. Measuring subjectivity. KI-K¨unstliche Intelligenz, 26(2):177–182, 2012.
  5. Celia Chen, Reem Alfayez, Kamonphop Srisopha, Lin Shi, and Barry Boehm. Evaluating human-assessed software maintainability metrics. In National Software Application Conference, pages 120–132. Springer, 2016.
  6. Igor Crk and Timothy Kluthe. Assessing the contribution of the individual alpha frequency (iaf) in an eeg-based study of program comprehension. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 4601–4604, Aug 2016.
  7. Jonathan Dorn. A general software readability model. MCS Thesis available from (http://www. cs. virginia. edu/˜ weimer/students/dorn-mcs-paper. pdf), 2012.
  8. Emotiv. Testbench specifications, emotiv, 2014.
  9. Martin Fowler, Kent Beck, John Brant, William Opdyke, and Don Roberts. Refactoring: improving the design of existing code. Addison-Wesley Professional, 1999.
  10. Daniel Graziotin, Fabian Fagerholm, Xiaofeng Wang, and Pekka Abrahamsson. What happens when software developers are (un) happy. Journal of Systems and Software, 140:32– 47, 2018.
  11. Qiong Gui, Maria V. Ruiz-Blondet, Sarah Laszlo, and Zhanpeng Jin. A survey on brain biometrics. ACM Comput. Surv., 51(6):112:1–112:38, February 2019.
  12. J Katona, I Farkas, T Ujbanyi, P Dukan, and A Kovari. Evaluation of the neurosky mindflex eeg headset brain waves data. In 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pages 91–94. IEEE, 2014.
  13. Katja Kevic, Braden M. Walters, Timothy R. Shaffer, Bonita Sharif, David C. Shepherd, and Thomas Fritz. Tracing software developers’ eyes and interactions for change tasks. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, pages 202–213, 2015.
  14. Seolhwa Lee, Danial Hooshyar, Hyesung Ji, Kichun Nam, and Heuiseok Lim. Mining biometric data to predict programmer expertise and task difficulty. Cluster Computing, Jan 2017.
  15. Yisi Liu, Olga Sourina, and Minh Khoa Nguyen. Real-time eeg-based human emotion recognition and visualization. In 2010 International Conference on Cyberworlds, pages 262– 269, Oct 2010.
  16. F Lotte, M Congedo, A L´ecuyer, F Lamarche, and B Arnaldi. A review of classification algorithms for EEG-based brain–computer interfaces. Journal of Neural Engineering, 4(2):R1–R13, jan 2007.
  17. Randall K. Minas, Rick Kazman, and Ewan Tempero. Neurophysiological impact of software design processes on software developers. In Augmented Cognition. Enhancing Cognition and Behavior in Complex Human Environments, pages 56–64. Springer International Publishing, 2017.
  18. R. Mohanani, I. Salman, B. Turhan, P. Rodrguez, and P. Ralph. Cognitive biases in software engineering: A systematic mapping study. IEEE Transactions on Software Engineering, 2018.
  19. Sebastian C. M¨uller. Measuring software developers’ perceived difficulty with biometric sensors. In Proceedings of the 37th International Conference on Software Engineering - Volume 2, ICSE ’15, pages 887–890, Piscataway, NJ, USA, 2015. IEEE Press.
  20. Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1–18, 2015.
  21. D Srinivasulu, Adepu Sridhar, and Durga Prasad Mohapatra. Evaluation of software understandability using rough sets. In Intelligent Computing, Networking, and Informatics, pages 939–946. Springer, 2014.
  22. John Sweller, Jeroen JG Van Merrienboer, and Fred GWC Paas. Cognitive architecture and instructional design. Educational psychology review, 10(3):251–296, 1998.
  23. Bryan A Wilbanks and Susan P McMullan. A review of measuring the cognitive workload of electronic health records. CIN: Computers, Informatics, Nursing, 36(12):579– 588, 2018.


Cognitive Load, Program Comprehension, Source code, Research Agenda