nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

On Data Analysis of Software Repositories

verfasst von : Dmitry Namiot, Vladimir Romanov

Erschienen in: Convergent Cognitive Information Technologies

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This article discusses the analysis of software repositories using data analysis methods. A review is made of methods for analyzing programs based on information retrieved from the program code stored in code repositories. A review is made of methods for analyzing programs based on information retrieved from the program code stored in repositories. The article reviews the works that apply methods of classification, clustering and depth learning in software development. For example, for classifying and predicting errors, changing the properties of code in the process of its evolution, detecting design flaws and debts, assist for code refactoring. The main ultimate goal for all models is, of course, an automation of programming. In practice, we are talking about more simple tasks. This includes, for example, information retrieval (program code), error prediction, clone detection, link analysis, evolution analysis, etc. Firstly, we discuss recurrent neural networks and their deployment for the analysis of software repositories. In the simplest case, recurrent networks model a programming language as a sequence of characters. Also, the paper covers clustering and topic modeling.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Simplified Rotor Angular Velocity Estimation for a Permanent Magnets Synchronous Motor by Current and Voltage Measurements

Nächstes Kapitel Development of BI-Platforms for Cybersecurity Predictive Analytics

Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(7–8), 1157–1182 (2003). https://doi.org/10.1162/153244303322753616CrossRefMATH

AI Predicts Coding Mistakes Before Developers Make Them. https://futurism.com/ai-predicts-coding-mistakes-before-developers-make-them. Accessed 09 Nov 2018

Rich, C., Waters, R.C. (eds.): Readings in Artificial Intelligence and Software Engineering. Morgan Kaufmann Publishers Inc., San Francisco (1986)

The Unreasonable Effectiveness of Recurrent Neural Networks. http://karpathy.github.io/2015/05/21/rnn-effectiveness/. Accessed 09 Nov 2018

White, M., Vendome, C., Linares-Vásquez, M., Poshyvanyk, D.: Toward deep learning software repositories. In: Proceedings of the 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, Florence, pp. 334–345 (2015). https://doi.org/10.1109/msr.2015.38

Hindle, A., Barr, E.T., Su, Z., Gabel, M., Devanbu, P.: On the naturalness of software. In: Proceedings of the 34th International Conference on Software Engineering (ICSE), Zurich, pp. 837–847 (2012). https://doi.org/10.1109/icse.2012.6227135

Nguyen, T.T., Nguyen, A.T., Nguyen, H.A., Nguyen, T.N.: A statistical semantic language model for source code. In: Proceedings of the 9th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2013), pp. 532–542. ACM, New York (2013). https://doi.org/10.1145/2491411.2491458

Afshan, S., McMinn, P., Stevenson, M.: Evolving readable string test inputs using a natural language model to reduce human oracle cost. In: Proceedings of the 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation, Luxembourg, pp. 352–361 (2013). https://doi.org/10.1109/icst.2013.11

Movshovitz-Attias, D., Cohen, W.W.: Natural language models for predicting programming comments. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, pp. 35–40. Association for Computational Linguistics (2013)

10.

Allamanis, M., Sutton, C.A.: Mining source code repositories at massive scale using language modeling. In: Proceedings of the 10th Working Conference on Mining Software Repositories (MSR 2013), San Francisco, CA, USA, May 2013, pp. 207–216 (2013)

11.

Campbell, J.C., Hindle, A., Amaral, J.N.: Syntax errors just aren’t natural: Improving error reporting with language models. In: Proceedings of the 11th Working Conference on Mining Software Repositories (MSR 2014), pp. 252–261. ACM, New York (2014). https://doi.org/10.1145/2597073.2597102

12.

Tonella, P., Tiella, R., Nguyen, D.C.: Interpolated n-grams for model based testing. In: Proceedings of the 36th International Conference on Software Engineering (ICSE 2014), pp. 562–572. ACM, New York (2014). https://doi.org/10.1145/2568225.2568242

13.

Tu, Z., Su, Z., Devanbu, P.: On the localness of software. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2014), pp. 269–280. ACM, New York (2014). https://doi.org/10.1145/2635868.2635875

14.

Allamanis, M., Barr, E.T., Bird, C., Sutton, C.: Learning natural coding conventions. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2014), pp. 281–293. ACM, New York (2014). https://doi.org/10.1145/2635868.2635883

15.

Shepperd, M., Bowes, D., Hall, T.: Researcher bias: the use of machine learning in software defect prediction. IEEE Trans. Softw. Eng. 40(6), 603–616 (2014). https://doi.org/10.1109/TSE.2014.2322358CrossRef

16.

The tera-PROMISE Repository. http://openscience.us/repo. Accessed 09 Nov 2018

17.

Malhotra, R.: A systematic review of machine learning techniques for software fault prediction. Appl. Soft Comput. 27, 504–518 (2015). https://doi.org/10.1016/j.asoc.2014.11.023CrossRef

18.

Di Martino, S., Ferrucci, F., Gravino, C., Sarro, F.: A genetic algorithm to configure support vector machines for predicting fault-prone components. In: Caivano, D., Oivo, M., Baldassarre, M.T., Visaggio, G. (eds.) PROFES 2011. LNCS, vol. 6759, pp. 247–261. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21843-9_20CrossRef

19.

Laradji, I.H., Alshayeb, M., Ghouti, L.: Software defect prediction using ensemble learning on selected features. Inf. Softw. Technol. 58, 388–402 (2015). https://doi.org/10.1016/j.infsof.2014.07.005CrossRef

20.

Kouroshfar, E., Mirakhorli, M., Bagheri, H., Xiao, L., Malek, S., Cai, Y.: A study on the role of software architecture in the evolution and quality of software. In: Proceedings of the 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, Florence, pp. 246–257 (2015). https://doi.org/10.1109/msr.2015.30

21.

Li, Z., Liang, P., Avgeriou, P., Guelfi, N., Ampatzoglou, A.: An empirical investigation of modularity metrics for indicating architectural technical debt. In: Proceedings of the 10th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA 2014), pp. 119–128. ACM, New York (2014). https://doi.org/10.1145/2602576.2602581

22.

Fernandes, E., Oliveira, J., Vale, G., Paiva, T., Figueiredo, E.: A review-based comparative study of bad smell detection tools. In: Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering (EASE 2016), Article 18, p. 18. ACM, New York (2016). https://doi.org/10.1145/2915970.2915984

23.

Blincoe, K., Harrison, F., Damian, D.K.: Ecosystems in GitHub and a method for ecosystem identification using reference coupling. In: Proceedings of the 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, Florence, pp. 202–211 (2015). https://doi.org/10.1109/msr.2015.26

24.

Chen, T.H., Thomas, S.W., Hassan, A.E.: A survey on the use of topic models when mining software repositories. Empirical Softw. Eng. 21(5), 1843–1919 (2016). https://doi.org/10.1007/s10664-015-9402-8CrossRef

25.

Thomas, S.W., Hassan, A.E., Blostein, D.: Mining unstructured software repositories. In: Mens, T., Serebrenik, A., Cleve, A. (eds.) Evolving Software Systems, pp. 139–162. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45398-4_5CrossRef

26.

Thomas, S.W.: Mining unstructured software repositories using IR models. Ph.D. thesis, Queen’s University, Canada (2012)

27.

Linstead, E., Rigor, P., Bajracharya, S., Lopes, C., Baldi, P.F.: Mining internet-scale software repositories. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems 20, pp. 929–936. Curran Associates, Red Hook (2008). http://papers.nips.cc/paper/3171-mining-internet-scale-software-repositories.pdf

28.

Papas, D., Tjortjis, C.: Combining clustering and classification for software quality evaluation. In: Likas, A., Blekas, K., Kalles, D. (eds.) SETN 2014. LNCS (LNAI), vol. 8445, pp. 273–286. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07064-3_22CrossRef

29.

Shtern, M., Tzerpos, V.: Clustering methodologies for software engineering. Adv. Softw. Eng. 2012, 1 (2012). Article ID 792024. https://doi.org/10.1155/2012/792024CrossRef

30.

Naim, S.M., Damevski, K., Hossain, M.S.: Reconstructing and evolving software architectures using a coordinated clustering framework. Autom. Softw. Eng. 24(3), 543–572 (2017). https://doi.org/10.1007/s10515-017-0211-8CrossRef

Titel: On Data Analysis of Software Repositories
verfasst von: Dmitry Namiot
Vladimir Romanov
Verlag: Springer International Publishing
Buch: Convergent Cognitive Information Technologies
Print ISBN: 978-3-030-37435-8

Electronic ISBN: 978-3-030-37436-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-37436-5_24

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"