Abstract
The work offers the adaptation of the big data analysis methods for software reliability increase. We suggest using software with similar properties and with the known reliability indicators for reliability prediction of new software. The concept of similar programs is formulated on the basis of five principles. Search results of similar programs are shown. Analysis, visualization, and interpreting for offered reliability metrics of similar programs are executed. The conclusion is drawn on reliability similarity for similar software and on a possibility of use of metrics for prediction of new software reliability. The reliability prediction will allow developers to operate resources and processes of verification and refactoring and provide software reliability increase in cutting of costs for development.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mayevsky, D. A. (2013). A new approach to software reliability. Lecture notes in Computer Science. Software engineering for resilient systems (Vol. 8166, pp. 156–168). Berlin: Springer.
Yakovyna, V., Fedasyuk, D., Nytrebych, O., Parfenyuk, I., & Matselyukh, V. (2014). Software reliability assessment using high-order Markov chains. International Journal of Engineering Science Invention, 3(7), 1–6.
Yakovyna, V. S. (2013). Influence of RBF neural network input layer parameters on software reliability prediction. 4-th International Conference on Inductive Modelling, Kyiv, pp. 344–347.
Maevsky, D. A., Yaremchuk, S. A., & Shapa, L. N. (2014). A method of a priori software reliability evaluation. Reliability: Theory & Applications, 9(1, 31):64–72. Access mode: http://www.gnedenko-forum.org/Journal/2014_1.html
Yaremchuk, S. A., & Maevsky, D. A.. (2014). The software reliability increase method. Studies in Sociology of Science, 5(2):89–95. Access mode http://www.cscanada.net/index.php/sss/article/view/4845
Kharchenko, V. S., Sklar, V. V., & Tarasyuk, O. M. (2004). Methods for modeling and evaluation of the quality and reliability of the software. Kharkov: Nat. Aerospace. Univ.“KhAI”. 159 p.
Kharchenko, V. S., Tarasyuk, O. M., & Sklyar, V. V. (2002). The method of software reliability y growth models choice using assumptions matrix. In Proceedings of 26-th Annual International Computer Software and Applications Conference (pp. 541–546). Oxford, England: COMPSAC.
Carrozza, G., Pietrantuono, R., & Russo, S. (2012). Fault analysis in mission-critical software systems: A detailed investigation. Journal of Software: Evolution and Process, 2, 1–28. https://doi.org/10.1002/smr.
Manyika, J., et al. (2011). Big Data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. https://bigdatawg.nist.gov/pdf/MGI_big_data_full_report.pdf
Capgemini (2015). Big & fast data: The rise of insight-driven business. http://www.capgemini.com/insights-data
A ComputerWeekly buyer’s guide to data management. (2017). http://www.computerweekly.com
Big data poses weighty challenges for data integration best practices. Information management handbook. (2017). http://www.techtarget.com/news
Dijcks, J.-P. (2013). Oracle: Big Data for the enterprise. http://www.oracle.com
DLA Piper & BCG. (2015). Earning consumer trust in Big Data: A European perspective. Carol Umhoefer, Jonathan Rofé, Stéphane Lemarchand. DLA Piper, Elias Baltassis, François Stragier, Nicolas Telle – The Boston Consulting Group. pp. 20.
Bridget Botelho at all. (2016). Big Data warriors formulate winning analytics strategies. E-publication. TechTarget Inc., www.techtarget.com
Gartner: Seven Best Practices for Your Big Data Analytics Projects. (2015).
Best Practices for a Successful Big Data Journey. (2017). Datameer, Inc. http://www.bitpipe.com/fulfillment/1502116404_933
Meeker, W. Q., & Hong, Y. (2014). Reliability meets Big Data: Opportunities and challenges. Quality Engineering, 26(1), 102–116., Taylor & Francis Group.
Zenmin, L. (2014). Using Data Mining Techniques to improve software reliability. Dissertation for the degree of Doctor of Philosophy in Computer Science, p. 153. https://www.researchgate.net/publication/32964724/
Kharchenko, V., & Yaremchuk, S. (2017). Technology Oriented assessment of software reliability: Big Data based search of similar programs. In Proceedings of the 13th International Conference on ICT in Education, research and industrial applications (pp. 686–698). Integration, Harmonization and Knowledge Transfer. Workshop TheRMIT.
Leskovec, J., Rajaraman, A., & Jeffey, D. (2014). Mining of massive datasets. Stanford University, Milliway Labs., p. 495.
Lammel, R. (2007). Google’s MapReduce programming model—Revisited. Data Programmability Team Microsoft Corp. Redmond, WA, USA, pp. 1–42. https://userpages.uni-koblenz.de/~laemmel/MapReduce/paper.pdf
Belazzougui, D., Botelho, F. C., Dietzfelbinger, M. (2009). Hash, displace, and compress (pp. 1–17). Berlin/Heidelberg: Springer. http://cmph.sourceforge.net/papers/esa09.pdf
Dean, J., & Ghemawat, S. (2004). MapReduce: Simplified data processing on large clusters. OSDI’04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, pp. 1–13. https://research.google.com/archive/mapreduce.html
Tera-PROMISE Home. (2017). http://openscience.us/repo/fault/ck/
NASA’S DATA PORTAL. (2017). https://data.nasa.gov
Software Testing and Static Code Analysis. (2017). http://www.coverity.com
Topcoder | Deliver Faster through Crowdsourcing. https://www.topcoder.com (2017)
Chidamber, S., & Kemerer, C. (1994). A metrics suite for object-oriented design. IEEE Transactions on Software Engineering, 20(6), 476–493.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Yaremchuck, S., Kharchenko, V., Gorbenko, A. (2018). Search of Similar Programs Using Code Metrics and Big Data-Based Assessment of Software Reliability. In: Alani, M., Tawfik, H., Saeed, M., Anya, O. (eds) Applications of Big Data Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-76472-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-76472-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76471-9
Online ISBN: 978-3-319-76472-6
eBook Packages: Computer ScienceComputer Science (R0)