Métricas de legibilidad del código fuente: revisión sistemática de literatura

Resumen

La calidad del software es un aspecto ligado directamente a los costos futuros de mantenimiento y, generalmente, se cuantifica mediante métricas de calidad de los productos de software. Uno de los aspectos a tener en cuenta para evaluar la calidad del software es su mantenibilidad, ya que tiene un alto impacto sobre los costos totales de los proyectos de software. En particular, se estima que el 70% del tiempo de mantenimiento se destina a comprender el código, por lo que resulta importante poder medir la legibilidad de un fragmento de código fuente adecuadamente. La legibilidad se define como la facilidad con la que una persona puede leer y comprender un fragmento de código escrito por otra persona. Esta característica es crucial para facilitar la compresión del código durante las tareas de mantenimiento de software, por lo que resulta necesario poder medir la legibilidad del código fuente. Por lo tanto, a lo largo de los últimos años, los investigadores en el área han propuesto múltiples métricas para medir la legibilidad del código fuente. No obstante, sigue siendo un reto poder evaluar con precisión la legibilidad del código fuente de una forma ampliamente aceptada. Por ello, es indispensable dar continuidad a este tipo de investigaciones y, para esto, es necesario conocer los avances recientes. Este artículo presenta una síntesis y análisis de las métricas de legibilidad de código, mediante una revisión sistemática de literatura, mostrando una recopilación de las características y los métodos utilizados para su medición. Los resultados de este trabajo serán de utilidad para que los investigadores en el área puedan proponer nuevas métricas de legibilidad del código fuente.

Palabras clave

legibilidad de código, código fuente, calidad de software, mantenibilidad, métricas, ingeniería de software

Biografía del autor/a

Ingrid Sofía Echavarría-Flórez, M.Sc.

Roles: análisis formal, investigación, curaduría de datos, software, redacción – borrador original.

Felipe Restrepo-Calle, Ph. D.

Roles: conceptualización, metodología, supervisión, validación, redacción – revisión y edición.

Citas

[1] R. P. L. Buse, and W. R. Weimer, “Learning a Metric for Code Readability,” IEEE Transactions on Software Engineering, vol. 36 (4), pp. 546-558, Jul. 2010. https://doi.org/10.1109/TSE.2009.70

[2] S. Scalabrino, G. Bavota, C. Vendome, M. Linares-Vasquez, D. Poshyvanyk, and R. Oliveto, “Automatically assessing code understandability: How far are we?,” in 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), 2017, pp. 417-427.

[3] D. Posnett, A. Hindle, and P. Devanbu, “A simpler model of software readability,” in Proceeding of the 8th working conference on Mining software repositories, 2011, pp. 73-82. https://doi.org/10.1145/1985441.1985454

[4] M. Akour, and B. Falah, “Application domain and programming language readability yardsticks,” in 7th International Conference on Computer Science and Information Technology (CSIT), 2016, pp. 1-6. https://doi.org/10.1109/CSIT.2016.7549476

[5] U. A. Mannan, I. Ahmed, and A. Sarma, “Towards understanding code readability and its impact on design quality,” in Proceedings of the 4th ACM SIGSOFT International Workshop on NLP for Software Engineering, 2018, pp. 18-21. https://doi.org/10.1145/3283812.3283820

[6] B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering – A systematic literature review,” Information and Software Technology, vol. 51 (1), pp. 7-15, 2009. https://doi.org/10.1016/j.infsof.2008.09.009

[7] D. Alawad, M. Panta, M. Zibran, and R. Islam, “An Empirical Study of the Relationships between Code Readability and Software Complexity,” in 27th Int Conf on Software Engineering and Data Engineering, 2018, pp. 1-7.

[8] M. Allamanis, E. T. Barr, C. Bird, and C. Sutton, “Suggesting accurate method and class names,” in Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, 2015, pp. 38-49. https://doi.org/10.1145/2786805.2786849

[9] H. Aman, S. Amasaki, T. Sasaki, and M. Kawahara, “Lines of Comments as a Noteworthy Metric for Analyzing Fault-Proneness in Methods,” IEICE Transactions on Information and Systems, vol. E98.D (12), pp. 2218-2228, 2015. https://doi.org/10.1587/transinf.2015EDP7107

[10] J. Borstler, and B. Paech, “The Role of Method Chains and Comments in Software Readability and Comprehension-An Experiment,” IEEE Transactions on Software Engineering, vol. 42 (9), pp. 886-898, Sep. 2016. https://doi.org/10.1109/TSE.2016.2527791

[11] H. M. Chen, W. H. Chen, and C. C. Lee, “An automated assessment system for analysis of coding convention violations in Java programming assignments,” Journal of Information Science and Engineering, vol. 34 (5), pp. 1203-1221, 2018. https://doi.org/10.6688/JISE.201809_34(5).0006

[12] S. Choi, S. Kim, J.-H. Lee, J. Kim, and J.-Y. Choi, “Measuring the Extent of Source Code Readability Using Regression Analysis,” in International Conference on Computational Science and Its Applications, 2018, pp. 410-421. https://doi.org/10.1007/978-3-319-95171-3_32

[13] R. Coleman, “Aesthetics Versus Readability of Source Code,” International Journal of Advanced Computer Science and Applications, vol. 9 (9), pp. 12-18, 2018. https://doi.org/10.14569/IJACSA.2018.090902

[14] A. De Renzis, M. Garriga, A. Flores, A. Cechich, C. Mateos, and A. Zunino, “A domain independent readability metric for web service descriptions,” Computer Standards & Interfaces, vol. 50, pp. 124-141, Feb. 2017. https://doi.org/10.1016/j.csi.2016.09.005

[15] R. M. dos Santos, and M. A. Gerosa, “Impacts of coding practices on readability,” in Proceedings of the 26th Conference on Program Comprehension, 2018, pp. 277-285. https://doi.org/10.1145/3196321.3196342

[16] S. Fakhoury, Y. Ma, V. Arnaoudova, and O. Adesope, “The effect of poor source code lexicon and readability on developers’ cognitive load,” in IEEE/ACM 26th International Conference on Program Comprehension, 2018, pp. 286-296.

[17] L. Frunzio, B. Lin, M. Lanza, and G. Bavota, “RETICULA: Real-time code quality assessment,” in IEEE 25th International Conference on Software Analysis, Evolution and Reengineering, 2018, pp. 542-546. https://doi.org/10.1109/SANER.2018.8330256

[18] Y. Liu, X. Sun, and Y. Duan, “Analyzing program readability based on WordNet,” in Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering, 2015, pp. 1-2. https://doi.org/10.1145/2745802.2745837

[19] Q. Mi, J. Keung, X. Mei, Y. Xiao, and W. Chan, “A Gamification Technique for Motivating Students to Learn Code Readability in Software Engineering,” in International Symposium on Educational Technology, 2018, pp. 250-254. https://doi.org/10.1109/ISET.2018.00062

[20] Q. Mi, J. Keung, Y. Xiao, S. Mensah, and Y. Gao, “Improving code readability classification using convolutional neural networks,” Information and Software Technology, vol. 104, pp. 60-71, Dec. 2018. https://doi.org/10.1016/j.infsof.2018.07.006

[21] Q. Mi, J. Keung, Y. Xiao, S. Mensah, and X. Mei, “An Inception Architecture-Based Model for Improving Code Readability Classification,” in Proceedings of the 22nd International Conference on Evaluation and Assessment in Software Engineering, 2018, pp. 139-144. https://doi.org/10.1145/3210459.3210473

[22] Q. Mi, J. Keung, and Y. Yu, “Measuring the Stylistic Inconsistency in Software Projects using Hierarchical Agglomerative Clustering,” in The 12th International Conference on Predictive Models and Data Analytics in Software Engineering, 2016, pp. 1-10. https://doi.org/10.1145/2972958.2972963

[23] D. J. Ferreira Novais, M. J. V. Pereira, and P. R. Henriques, “Program analysis for Clustering Programmers’ Profile,” in Federated Conf on Computer Science and Information Systems, Prague, 2017, pp. 701-705. https://doi.org/10.15439/2017F147

[24] A. Pahal, and R. S. Chillar, “Code Readability: A Review of Metrics for Software Quality,” International Journal of Computer Trends and Technology, vol. 46 (1), pp. 1-4, 2017. https://doi.org/10.14445/22312803/IJCTT-V46P101

[25] S. Scalabrino, M. Linares-Vásquez, R. Oliveto, and D. Poshyvanyk, “A comprehensive model for code readability,” Journal of Software: Evolution and Process, vol. 30 (6), e1958, Jun. 2018. https://doi.org/10.1002/smr.1958

[26] T. Sedano, “Code Readability Testing, an Empirical Study,” in IEEE 29th International Conference on Software Engineering Education and Training, 2016, pp. 111-117. https://doi.org/10.1109/CSEET.2016.36

[27] A. Wulff-Jensen, K. Ruder, E. Triantafyllou, and L. E. Bruni, “Gaze Strategies Can Reveal the Impact of Source Code Features on the Cognitive Load of Novice Programmers,” in International Conference on Applied Human Factors and Ergonomics, 2019. https://doi.org/10.1007/978-3-319-94866-9_9

[28] W. Xu, D. Xu, and L. Deng, “Measurement of Source Code Readability Using Word Concreteness and Memory Retention of Variable Names,” in IEEE 41st Annual Computer Software and Applications Conf, 2017, pp. 33-38. https://doi.org/10.1109/COMPSAC.2017.166

[29] S. Butler, M. Wermelinger, Yijun Yu, and H. Sharp, “Exploring the Influence of Identifier Names on Code Quality: An Empirical Study,” in Proceedings of the 14th European Conference on Software Maintenance and Reengineering, 2010, pp. 156-165. https://doi.org/10.1109/CSMR.2010.27

[30] J. Dorn, A General Software Readability Model, 2012. https://www.cs.virginia.edu/~weimer/students/dorn-mcs-paper.pdf

[31] I. B. Sampaio, and L. Barbosa, “Software readability practices and the importance of their teaching,” in 7th International Conference on Information and Communication Systems, 2016, pp. 304-309. https://doi.org/10.1109/IACS.2016.7476069