Abstract
Recently, many open source software are used for quick delivery, cost reduction, standardization. The bug tracking systems are managed by many open source projects. Then, many data sets are recorded on the bug tracking systems by many users and project members. The quality of open source software will be improved significantly if the software managers can make an effective use of these data sets on the bug tracking systems. In this paper, we propose a method of open source software reliability assessment based on the deep learning. Also, we show several numerical examples of open source software reliability assessment in the actual software projects. Moreover, we compare the methods to estimate the level of software faults based on the deep learning by using the fault data sets of actual software projects.
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Acknowledgments
This work was supported in part by the Telecommunications Advancement Foundation in Japan, and the JSPS KAKENHI Grant No. 15K00102 and No. 25350445 in Japan.
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Tamura, Y., Ashida, S., Matsumoto, M., Yamada, S. (2016). Identification Method of Fault Level Based on Deep Learning for Open Source Software. In: Lee, R. (eds) Software Engineering Research, Management and Applications. Studies in Computational Intelligence, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-319-33903-0_5
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DOI: https://doi.org/10.1007/978-3-319-33903-0_5
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