Experimental Validation of Source Code Reviews on Mobile Devices

  • Wojciech FrączEmail author
  • Jacek Dajda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10408)


The practice of code reviews is fundamental for producing and maintaining high-quality source code. However, because it is not the most favourite and enjoyable task of a developer, it is still not acknowledged as the industry worldwide standard. The idea behind this research is to encourage developers by providing them with an accessible way to perform reviews by using mobile devices. This paper presents the results from the experiment-driven investigation aimed at comparative analysis of code reviews performed on a dedicated mobile tool and a desktop application. After comparing results from 79 mobile and 102 desktop reviews and analysing almost 2500 comments we claim that mobile devices can be used to effectively read, understand and review source code of any size.



The research leading to these results has received funding from the Dean’s Grant Programme (grant no. funded by Faculty of Computer Science, Electronics and Telecommunications at AGH University of Science and Technology.


  1. 1.
    Bacchelli, A., Bird, C.: Expectations, outcomes, and challenges of modern code review. In: Proceedings of the 2013 International Conference on Software Engineering, pp. 712–721. IEEE Press (2013)Google Scholar
  2. 2.
    Barnett, M., Bird, C., Brunet, J., Lahiri, S.K.: Helping developers help themselves: automatic decomposition of code review changesets. In: Proceedings of the 37th International Conference on Software Engineering, vol. 1, pp. 134–144. IEEE Press (2015)Google Scholar
  3. 3.
    Baysal, O., Kononenko, O., Holmes, R., Godfrey, M.W.: Investigating technical and non-technical factors influencing modern code review. Empirical Softw. Eng. 21(3), 932–959 (2016)CrossRefGoogle Scholar
  4. 4.
    Beller, M., Bacchelli, A., Zaidman, A., Juergens, E.: Modern code reviews in open-source projects: which problems do they fix? In: Proceedings of the 11th Working Conference on Mining Software Repositories, pp. 202–211. ACM (2014)Google Scholar
  5. 5.
    Conway, E., Stanford, J., Rettschlag, D., et al.: mGerrit - a Gerrit instance viewer (2016). Accessed 3 May 2017
  6. 6.
    Czerwonka, J., Greiler, M., Tilford, J.: Code reviews do not find bugs: how the current code review best practice slows us down. In: Proceedings of the 37th International Conference on Software Engineering, vol. 2, pp. 27–28. IEEE Press (2015)Google Scholar
  7. 7.
    Frącz, W.: Code review task (2016). Accessed 3 May 2017
  8. 8.
    Frącz, W., Dajda, J.: Source code reviews on mobile devices. Comput. Sci. 17(2), 143 (2016)CrossRefGoogle Scholar
  9. 9.
    Jawnnypoo, M., et al.: Labcoat - gitlab app for android (2017). Accessed 3 May 2017
  10. 10.
    Kemerer, C.F., Paulk, M.C.: The impact of design and code reviews on software quality: an empirical study based on psp data. IEEE Trans. Softw. Eng. 35(4), 534–550 (2009)CrossRefGoogle Scholar
  11. 11.
    Mäntylä, M.V., Lassenius, C.: What types of defects are really discovered in code reviews? IEEE Trans. Softw. Eng. 35(3), 430–448 (2009)CrossRefGoogle Scholar
  12. 12.
    Martin, R.C.: Clean Code. A Handbook of Agile Software Craftsmanship. Pearson Education, Inc., Upper Saddle River (2009)Google Scholar
  13. 13.
    McIntosh, S., Kamei, Y., Adams, B., Hassan, A.E.: An empirical study of the impact of modern code review practices on software quality. Empirical Softw. Eng. 21(5), 2146–2189 (2016)CrossRefGoogle Scholar
  14. 14.
    Panichella, S., Arnaoudova, V., Di Penta, M., Antoniol, G.: Would static analysis tools help developers with code reviews? In: 2015 IEEE 22nd International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 161–170. IEEE (2015)Google Scholar
  15. 15.
    Rahman, M.M., Roy, C.K., Collins, J.A.: Correct: code reviewer recommendation in github based on cross-project and technology experience. In: Proceedings of the 38th International Conference on Software Engineering Companion, pp. 222–231. ACM (2016)Google Scholar
  16. 16.
    Thongtanunam, P., Tantithamthavorn, C., Kula, R.G., Yoshida, N., Iida, H., Matsumoto, K.I.: Who should review my code? A file location-based code-reviewer recommendation approach for modern code review. In: 2015 IEEE 22nd International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 141–150. IEEE (2015)Google Scholar
  17. 17.
    Xia, X., Lo, D., Wang, X., Yang, X.: Who should review this change?: putting text and file location analyses together for more accurate recommendations. In: 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 261–270. IEEE (2015)Google Scholar
  18. 18.
    Yu, Y., Wang, H., Yin, G., Wang, T.: Reviewer recommendation for pull-requests in github: what can we learn from code review and bug assignment? Inf. Softw. Technol. 74, 204–218 (2016)CrossRefGoogle Scholar
  19. 19.
    Zanjani, M.B., Kagdi, H., Bird, C.: Automatically recommending peer reviewers in modern code review. IEEE Trans. Softw. Eng. 42(6), 530–543 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland

Personalised recommendations