Using Readability Metrics in Estimating the Readability of REpresentational State Transfer State Transfer Uniform Resource Identifiers Schema

Fuad Alshraiedeh; Norliza Katuk; Hossam Almahasneh

doi:10.15575/join.v11i1.1653

Authors

Fuad Alshraiedeh Department of Software Engineering, Zarqa University, Jordan
Norliza Katuk School of Computing, Universiti Utara Malaysia, Malaysia
Hossam Almahasneh Department of Computer Science, Zarqa University, Jordan

DOI:

https://doi.org/10.15575/join.v11i1.1653

Keywords:

Readability methods, URI readability , URI schema

Abstract

Uniform Resource Identifiers (URIs) may have a direct impact on the understanding of REpresentational State Transfer State Transfer (RESTful) functionality, and thus, on the discovery of final RESTful product. RESTful Web Services (WS)/Application Programming Interfaces (APIs) are designed to expose data and functionality through resources accessed by dedicated URIs over HyperText Transfer Protocol (HTTP), which recently represents the direct descriptions schema of what functions does the concerned RESTful WS/API present. Furthermore, the discovery of suitable RESTful is heavily rely on the simplicity of understanding their URI schemas, which recently suffer from critical issues in how to measure their readability. For that, WS/APIs developers aspire to measure the readability of RESTful URI schemas before exposing them over the Internet to estimate their usability. Consequently, this research proposes four readability metrics for the stated purpose namely: Flesch-Kincaid (F-K), Flesch Reading Ease (FRES), Simple Measure of Gobbledygook (SMOG), and Coleman Liau Index (CLI). The research identifies the variables required to calculate the readability metric and formulate the equations for them. Four experts in linguistics were asked to validate the proposed metrics and their identified variables. The research successfully conducted empirical research on 8 well-known RESTful WSs/APIs of the dataset, and the proposed metrics were implemented on 6952 URIs schemas. The average values for the aforementioned metrics were 7.41%, 59.63%, 6.73%, and 17.55% respectively, where in certain metrics, a low average value signifies easy readability, but in others, it signifies hard readability, and vice versa.

References

[1] F. Palma, J. Dubois, N. Moha, and Y.-G. Guéhéneuc, “Detection of REST Patterns and Antipatterns: A Heuristics-Based Approach,” pp. 230–244, 2014, doi: 10.1007/978-3-662-45391-9_16.

[2] F. Alshraiedeh and N. Katuk, “SOAP and RESTful web service anti-patterns: A scoping review,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 5, pp. 1831–1840, 2019, doi: 10.30534/ijatcse/2019/05852019.

[3] F. Haupt, F. Leymann, A. Scherer, and K. Vukojevic-Haupt, “A Framework for the Structural Analysis of REST APIs Florian,” Proceedings - 2017 IEEE International Conference on Software Architecture, ICSA 2017, pp. 55–58, 2017, doi: 10.1109/ICSA.2017.40.

[4] F. Sabir, Y. G. Gueheneuc, F. Palma, N. Moha, G. Rasool, and H. Akhtar, “A Mixed-method Approach to Recommend Corrections and Correct REST Antipatterns,” IEEE Transactions on Software Engineering, no. November, 2021, doi: 10.1109/TSE.2021.3117023.

[5] F. S. Alshraiedeh and N. Katuk, “AURI parsing technique and algorithm for anti-pattern detection in RESTful Web services,” International Journal of Web Information Systems, vol. 17, no. 1, pp. 1–17, 2020, doi: 10.1108/IJWIS-08-2020-0052.

[6] F. Alshraiedeh, F. Alshraiedeh, N. Katuk, and M. Education, “SOAP and RESTful web service anti-patterns: A scoping review,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 5, 2019.

[7] A. Neagu and F. Palma, “A Systematic Literature Review on the Methodologies for Detecting REST Antipatterns in RESTful APIs,” 2022.

[8] A. Arcuri, “RESTful API automated test case generation,” Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security, QRS 2017, pp. 9–20, 2017, doi: 10.1109/QRS.2017.11.

[9] J. R. V. Dantas and P. P. M. Farias, “An architecture for REStful web service discovery using semantic interfaces,” Int J Semant Web Inf Syst, vol. 16, no. 1, pp. 1–24, 2020, doi: 10.4018/IJSWIS.2020010101.

[10] S. B. A. Ben Lamine, H. Baazaoui Zghal, M. Mrissa, and C. Ghedira Guegan, “An ontology-based approach for personalized RESTful Web service discovery,” Procedia Comput Sci, vol. 112, pp. 2127–2136, 2017, doi: 10.1016/j.procs.2017.08.235.

[11] E. Aghajani, C. Nagy, G. Bavota, and M. Lanza, “A Large-scale empirical study on linguistic antipatterns affecting apis,” Proceedings - 2018 IEEE International Conference on Software Maintenance and Evolution, ICSME 2018, pp. 25–35, 2018, doi: 10.1109/ICSME.2018.00012.

[12] P. K. Ojha, A. Ismail, and K. Kundumani Srinivasan, “Perusal of readability with focus on web content understandability,” Jan. 01, 2021, King Saud bin Abdulaziz University. doi: 10.1016/j.jksuci.2018.03.007.

[13] S. Ghosh, S. V. Gothe, C. Sanchi, and B. R. K. Raja, “EdATLAS: An Efficient Disambiguation Algorithm for Texting in Languages with Abugida Scripts,” Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021, pp. 325–332, 2021, doi: 10.1109/ICSC50631.2021.00061.

[14] J. K. Djoko, F. Ebobisse, A. T. McBride, and B. D. Reddy, “A discontinuous Galerkin formulation for classical and gradient plasticity. Part 2: Algorithms and numerical analysis,” Comput Methods Appl Mech Eng, vol. 197, no. 1–4, pp. 1–21, Dec. 2007, doi: 10.1016/j.cma.2007.06.027.

[15] A. De Renzis, M. Garriga, A. Flores, A. Cechich, C. Mateos, and A. Zunino, “A domain independent readability metric for web service descriptions,” Comput Stand Interfaces, vol. 50, pp. 124–141, 2017, doi: 10.1016/j.csi.2016.09.005.

[16] J. P. J. Kincaid et al., “Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel (No. RBR-8-75). Naval Technical Training Command Millington TN Research Branch.,” Naval Technical Training Command Millington TN Research Branch, 1975, [Online]. Available: http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA006655

[17] G. Thomas, R. Derald Hartley, and J. Peter Kincaid, “TEST-RETEST AND INTER-ANALYST RELIABILITY OF THE AUTOMATED READABILITY INDEX, FLESCH READING EASE SCORE, AND THE FOG COUNT.”

[18] D. Eleyan, A. Othman, and A. Eleyan, “Enhancing software comments readability using flesch reading ease score,” Information (Switzerland), vol. 11, no. 9, Sep. 2020, doi: 10.3390/INFO11090430.

[19] F. Zhang, Q. Zeng, H. Duan, and C. Liu, “Composition context-based web services similarity measure,” IEEE Access, vol. 7, pp. 65195–65206, 2019, doi: 10.1109/ACCESS.2019.2915371.

[20] S. Zhou, H. Jeong, and P. A. Green, “How consistent are the best-known readability equations in estimating the readability of design standards?,” IEEE Trans Prof Commun, vol. 60, no. 1, pp. 97–111, 2017, doi: 10.1109/TPC.2016.2635720.

[21] M. Hirsch, A. Rodriguez, J. M. Rodriguez, C. Mateos, and A. Zunino, “Spotting and Removing WSDL Anti-pattern Root Causes in Code-first Web Services Using NLP Techniques: A Thorough Validation of Impact on Service Discoverability,” Comput Stand Interfaces, vol. 56, pp. 116–133, 2018, doi: 10.1016/j.csi.2017.09.010.

[22] Y. Zhang, N. Lin, and S. Jiang, “A Study on Syntactic Complexity and Text Readability of ASEAN English News,” Proceedings of the 2019 International Conference on Asian Language Processing, IALP 2019, pp. 313–318, 2019, doi: 10.1109/IALP48816.2019.9037695.

[23] M. Zhang and A. Arcuri, “Open Problems in Fuzzing RESTful APIs: A Comparison of Tools,” 2022.

[24] H. Antunes and C. T. Lopes, “Readability of web content,” 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, no. June, pp. 1–4, 2019, doi: 10.23919/cisti.2019.8760889.

[25] S. Zhou, H. Jeong, and P. A. Green, “How consistent are the best-known readability equations in estimating the readability of design standards?,” IEEE Trans Prof Commun, vol. 60, no. 1, pp. 97–111, 2017, doi: 10.1109/TPC.2016.2635720.

[26] S. J. Cook, “Taxonomy of Software Readability Changes,” 2022, [Online]. Available: https://scholarworks.rit.edu/theses

[27] D. Bilal, “Comparing google’s readability of search results to the flesch readability formulae: A preliminary analysis on children’s search queries,” Proceedings of the ASIST Annual Meeting, vol. 50, no. 1, pp. 1–9, 2013, doi: 10.1002/meet.14505001094.