The concept of Basic control structure (BCS) of software and their cognitive weights have been proposed in theory. However not much work has been done to validate weights assigned to various programming constructs. One of the primary reasons for same is that it is difficult to design the experiment to measure the mental effort involved in understanding the effect of various programming constructs and their interplay. The paper discusses some of the challenge involved in setting up such psychological experiment. In such experiments we cannot select and compare any random code snippets of various programming constructs- the variations are endless. We identified different approaches to conduct such experiments. We explained with example various factors and issues involved in selecting the code snippets which resulted in minimum variations in code snippets of various programming constructs, other than that is inherent in syntax. The code snippets design approach proposed here can be used to conduct series of psychological experiments in software studies. We need series of such experiments not only to validate the cognitive weights of different programming constructs, but also it will go long way in having robust metrics for software complexities. These types of experiments can be extremely useful in the field of computer science education in understanding the cognitive load required for learning the concepts of programming languages.

Software Complexity;Code complexity;Code Comprehension; Cognitive Weights;Basic control structure;cognitive metrics;Cognitive load; Software Experimentation; computer science education; Code snippets;human brain working

[1] T. J. McCabe, “A Complexity Measure,” IEEE Trans. Softw. Eng., vol. SE-2, no. 4, pp. 308–320, 1976.
[2] J. Shao and Y. Wang, “A new measure of software complexity based on cognitive weights,” Can. J. Elect. Comput. Eng., vol. 28, no. 2, pp. 1–6, 2003.
[3] Y. Wang, “Cognitive Complexity of Software and its Measurement,” in 5th IEEE International Conference on Cognitive Informatics, 2006, pp. 226–235.
[4] Y. Wang and S. Patel, “IJSSCI-1201-CogFundSE.pdf,” Int. J. Softw. Sci. Comput. Intell., vol. 1, no. June, pp. 1–19, 2009.
[5] O. Esther, O. Stephen, O. Elijah, A. Rafiu, T. Dimple, and Y. Olajide, “Development of an Improved Cognitive Complexity Metrics for Object- Oriented Codes,” Br. J. Math. Comput. Sci., vol. 18, no. 2, pp. 1–11, Jan. 2016.
[6] A. K. Jakhar and K. Rajnish, “A new cognitive approach to measure the complexity of software’s,” Int. J. Softw. Eng. its Appl., vol. 8, no. 7, pp. 185–198, 2014.
[7] D. De Silval and N. Kodagoda, “Improvements to a Complexity Metric : CB Measure,” in IEEE 10th International Conference on Industrial and Information Systems, ICIIS 2015, 2015, pp. 401–406.
[8] S. Misra, A. Adewumi, L. Fernandez-Sanz, and R. Damasevicius, “A Suite of Object Oriented Cognitive Complexity Metrics,” IEEE Access, vol. 6, no. January, pp. 8782–8796, 2018.
[9] S. Misra, A. Adewumi, R. Damasevicius, and R. Maskeliunas, “Analysis of Existing Software Cognitive Complexity Measures,” Int. J. Secur. Softw. Eng., vol. 8, no. 4, pp. 51–71, Oct. 2017.
[10] V. Gruhn and R. Laue, “On Experiments for Measuring Cognitive Weights for Software Control Structures,” in 6th IEEE International Conference on Cognitive Informatics, 2007, no. September 2007, pp. 116–119.
[11] D. J. Adamo, “An Experiment to Measure the Cognitive Weights of Code Control Structures.” pp. 1–16, Jul-2014.
[12] M. E. Hansen, A. Lumsdaine, and R. L. Goldstone, “An experiment on the cognitive complexity of code,” in Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science Society, 2013.
[13] H. Sackman, W. J. Erikson, and E. E. Grant, “Exploratory experimental studies comparing online and offline programming performance,” Communications of the ACM, vol. 11, no. 1, pp. 3–11, Jan-1968.
[14] B. T. Mynatt, “The effect of semantic complexity on the comprehension of program modules,” Int. J. Man-Machine Stud., vol. 21, pp. 91–103, 1984.
[15] E. R. Iselin, “Conditional statements, looping constructs, and program comprehension: an experimental study,” Int. J. Man. Mach. Stud., vol. 28, no. 1, pp. 45–66, Jan. 1988.
[16] B. Curtis, “Substantiating Programmer Variability,” in Proceedings of the IEEE, 1981, vol. 69, no. 7, p. 846.
[17] M. Klerer, “Experimental study of a two-dimensional language vs Fortran for first-course programmers,” Int. J. Man. Mach. Stud., vol. 20, pp. 445–467, 1984.
[18] S. Ajami, Y. Woodbridge, and D. G. Feitelson, “Syntax, Predicates, Idioms - What Really Affects Code Complexity?,” in 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC), 2017, vol. 24, no. 1, pp. 66–76.
[19] D. I. De Silva, N. Kodagoda, S. R. Kodituwakku, and A. J. Pinidiyaarachchi, “Analysis and enhancements of a cognitive based complexity measure,” in 2017 IEEE International Symposium on Information Theory (ISIT), 2017, pp. 241–245.
[20] U. Chhillar and S. Bhasin, “A New Weighted Composite Complexity Measure for Object-Oriented Systems,” Int. J. Inf. Commun. Technol. Res., vol. 1, no. 3, pp. 101–108, 2011.
[21] L. Jain and S. Singh, “A journey from cognitive metrics to cognitive computers,” IJARET, vol. 4, no. 4, pp. 60–66, 2013.
[22] V. R. Basili, “The role of experimentation in software engineering: past, current, and future,” in Proceedings of IEEE 18th International Conference on Software Engineering, 1996, pp. 442–449.
[23] V. Rajlich and G. S. Cowan, “Towards standard for experiments in program comprehension,” in Proceedings Fifth International Workshop on Program Comprehension. IWPC’97, 1997, pp. 160–161.