[
[1] Buse R. P. L., Weimer W.R., A metric for software readability. Proceedings of the 2008 international symposium on Software testing and analysis. ACM, 2008.10.1145/1390630.1390647
]Search in Google Scholar
[
[2] Buse R. P. L., Weimer W. R., Learning a Metric for Code Readability, In IEEE Transactions on Software Engineering, vol. 36, no. 4, pp. 546-558, July-Aug. 2010.10.1109/TSE.2009.70
]Search in Google Scholar
[
[3] Collar E., Ricardo V., Role of software readability on software development cost, In the proceedings of the 21st Forum on COCOMO and Software Cost Modeling, Herndon, VA, 2006.
]Search in Google Scholar
[
[4] Daryl P., Hindle A., Devanbu P., A simpler model of software readability, Proceedings of the 8th working conference on mining software repositories, ACM, 2011.
]Search in Google Scholar
[
[5] Dhabhai D., Dua A.K., Saroliya A., Review paper: A Study on Metric For Code Readability, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 6, June 2015.
]Search in Google Scholar
[
[6] Duaa A., An empirical study of the relationships between code readability and software complexity, 2018.
]Search in Google Scholar
[
[7] Gunning R., The Technique of Clear Writing. McGraw-Hill. pp. 36–37. 1952.
]Search in Google Scholar
[
[8] Harry Mc.G., SMOG grading-a new readability formula, Journal of reading, vol. 12, no. 8, 1969, pp. 639-646.
]Search in Google Scholar
[
[9] Hofmeister J., Siegmund J., Holt D. V., Shorter identifier names take longer to comprehend, 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), Klagenfurt, 2017, pp. 217-227.10.1109/SANER.2017.7884623
]Search in Google Scholar
[
[10] Hyndman R.J., Koehler A.B., Another Look at Measures of Forecast Accuracy, In International Journal of Forecasting, pp. 679-688, 2006.10.1016/j.ijforecast.2006.03.001
]Search in Google Scholar
[
[11] Kate R.J., Luo X., Patwardhan S., Franz M., Florian R., Joseph R., Roukos S., Welty C., Learning to predict readability using diverse linguistic features, In the proceedings of the 23rd International Conference on Computational Linguistics, August 2010, pp. 546–554.
]Search in Google Scholar
[
[12] Kincaid J.P., Fishburne R.P., Rogers R.L., Chissom B.S., Derivation of new readability formulas (automated readability index, fog count, and flesch reading ease formula) for Navy enlisted personnel. Research Branch Report 8–75. Chief of Naval Technical Training: Naval Air Station Memphis. 1975.10.21236/ADA006655
]Search in Google Scholar
[
[13] Levin R. I. Statistics for Management, Pearson Education India, 2008.
]Search in Google Scholar
[
[14] Lin J., Wu K., A Model for Measuring Software Understandability, The Sixth IEEE International Conference on Computer and Information Technology (CIT’06), Seoul, 2006, pp. 192-192.10.1109/CIT.2006.13
]Search in Google Scholar
[
[15] Mark T., Mean Absolute Deviation. Dynamic Portfolio Theory and Management, 2004.
]Search in Google Scholar
[
[16] Mccarthy P., Dufty D., McNamara D., Toward a new readability: A mixed model approach, In the proceedings of the 29th Annual Conference of the Cognitive Science Society, 2007.
]Search in Google Scholar
[
[17] McNamara D.S., Ozuru Y., Graesser A., Louwerse M., Validating coh-metrix, In the proceedings of the 28th annual conference of the cognitive science society. Mahwah, NJ: Erlbaum, 2006.
]Search in Google Scholar
[
[18] Namani R., Kumar J., A New Metric for Code Readability, IOSR Journal of Computer Engineering, vol. 6, Issue 6, (2012) November-December.10.9790/0661-0664448
]Search in Google Scholar
[
[19] Nielebock S., Krolikowski D., Krüger J., Leich T., Ortmeier F., Commenting source code: is it worth it for small programming tasks?, In Empirical Software Engineering, vol. 24, 2019, pp. 1418–1457.10.1007/s10664-018-9664-z
]Search in Google Scholar
[
[20] Relf P. A., Tool assisted identifier naming for improved software readability: an empirical study, 2005 International Symposium on Empirical Software Engineering, 2005.
]Search in Google Scholar
[
[21] Scalabrino S., Linares-Vásquez M., Poshyvanyk D., Oliveto R., Improving code readability models with textual features, 2016 IEEE 24th International Conference on Program Comprehension (ICPC), Austin, TX, 2016, pp. 1-10.10.1109/ICPC.2016.7503707
]Search in Google Scholar
[
[22] Scalabrino S., Linares-Vásquez M., Oliveto R., Poshyvanyk V, A comprehensive model for code readability, Journal of Software: Evolution and Process, vol. 30, no. 6, p. e1958, 2018.10.1002/smr.1958
]Search in Google Scholar
[
[23] Senter R.J., Smith E.A., Automated Readability Index. Wright-Patterson Air Force Base: iii. AMRL-TR-6620. Retrieved March 18, 2012.
]Search in Google Scholar
[
[24] Si L., Callan J., A statistical model for scientific readability, In proceedings of the 10th international conference on Information and knowledge management, October, 2001, pp. 574–576.10.1145/502585.502695
]Search in Google Scholar
[
[25] Taek L.E.E., Jung-Been L.E.E., Effect analysis of coding convention violations on readability of post-delivered code. IEICE TRANSACTIONS on Information and Systems, vol. 98, no.7, 2015, pp. 1286-1296.10.1587/transinf.2014EDP7327
]Search in Google Scholar
[
[26] Tashtoush Y., Odat Z., Alsmadi I., Yatim M., Impact of Programming Features on Code Readability, In International Journal of Software Engineering and Its Applications, vol. 7, 2013, pp. 441-458.10.14257/ijseia.2013.7.6.38
]Search in Google Scholar
[
[27] Tufano M., Pantiuchina J., Watson C., Bavota G., Poshyvanyk D., On learning meaningful code changes via neural machine translation, In the proceedings of the 41st International Conference on Software Engineering, IEEE Press, 2019.10.1109/ICSE.2019.00021
]Search in Google Scholar
[
[28] White M., Tufano M., Martinez M., Monperrus M., Poshyvanyk D., Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities, In the proceedings of the IEEE International Conference on Software Analysis, Evolution and Reengineering, 2019.10.1109/SANER.2019.8668043
]Search in Google Scholar
[
[29] Wu Y., Wang S., Bezemer C., Inoue K., How do developers utilize source code from stack overflow?. Empirical Software Engineering, vol. 24, no. 2, 2019, pp. 637-673.10.1007/s10664-018-9634-5
]Search in Google Scholar
