Abstract
Nowadays, there are many methods and good practices in software engineering that aim to provide high quality software. However, despite the efforts of software developers, there are often defects in projects, the removal of which is often associated with a large financial effort and time. The article presents an example approach to defect prediction in IT projects based on prediction models built on historical information and product metrics collected from various data repositories.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Petersen, K., Wohlin, C.: Software process improvement through the Lean Measurement (LEAM) method. J. Syst. Softw. 83(7), 1275–1287 (2010)
Wojszczyk, R.: Quality assessment of implementation of strategy design pattern. In: Advances in Intelligent Systems and Computing, vol. 620, pp. 37–44. Springer (2018)
Ramler, R., Himmelbauer, J.: Building defect prediction models in practice. In: Handbook of Research on Emerging Advancements in Software Engineering, pp. 540–565 (2014)
Jureczko, M., Magott, J.: QualitySpy: a framework for monitoring software development processes. J. Theor. Appl. Comput. Sci. 6(1), 35–45 (2012)
Jureczko, M. and Contributors: Quality Spy. http://java.net/projects/qualityspy
The Apache Software Foundation, Apache License, Version 2.0. http://www.apache.org/licenses/LICENSE-2.0.html
JIRA. Atlassian. https://www.atlassian.com/software/jira
SVN. Enterprise-class centralized version control. https://subversion.apache.org/
D’Ambros, M., Lanza, M.: Distributed and collaborative software evolution analysis with churrasco. Sci. Comput. Program. 75, 276–287 (2010)
Madeyski, L., Majchrzak, M.: Software Measurement and defect prediction with depress extensible framework. Found. Comput. Decis. Sci. 39(4), 249–270 (2014)
Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B.: KNIME: the konstanz information miner. In: Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007). Springer (2007)
Czyczyn-Egird, D., Wojszczyk, R.: The effectiveness of data mining techniques in the detection of DDoS attacks. In: 14th International Conference on Distributed Computing and Artificial Intelligence, vol. 620, pp. 53–60. Springer (2018)
Kazim, A.: A study of software development life cycle process models. Int. J. Adv. Res. Comput. Sci. 8(1) (2017)
RebelLabs. Developer Productivity Report 2013 – How Engineering Tools & Practices Impact Software Quality & Delivery. http://bit.ly/2nQVSFh. Accessed Feb 2018
Slowik, A., Kwasnicka, H.: Nature inspired methods and their industry applications - swarm intelligence algorithms. IEEE Trans. Ind. Inform. 14(3), 1004–1015 (2018)
Apache Maven. https://maven.apache.org/
GitHub Inc. http://www.github.com
GraphQL API v4. https://developer.github.com/v4/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Czyczyn-Egird, D., Slowik, A. (2019). Defect Prediction in Software Using Predictive Models Based on Historical Data. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-99608-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99607-3
Online ISBN: 978-3-319-99608-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)