Ansong Ni
ABSTRACT
Continuous Integration (CI) reduces risk in software development, but a CI build usually brings huge time and resource consumption. Machine learning methods have been employed to cut the expenses of CI and provide instant feedback by predicting CI results. Nevertheless, effective learning requires massive training data which is not available for a new project. Moreover, due to the diversified characteristics of different projects, reusing models built on other projects leads to poor performance. To address this problem, we propose a novel active online model adaptation approach ACONA, which dynamically adapts a pool of classifiers trained on various projects to a new project using only a small fraction of new data it actively selects. With empirical study on Travis CI, we show that ACONA achieves an improvement of F-Measure by 40.0% while reducing Accumulated Error by 63.2% and the adapted model outperforms existing approaches.
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Digital Library
- Jacqui Finlay, Russel Pears, and Andy M Connor. 2014. Data stream mining for predicting software build outcomes using source code metrics. Information and Software Technology 56, 2 (2014), 183--198. Google ScholarDigital Library
- Ahmed E Hassan and Ken Zhang. 2006. Using decision trees to predict the certification result of a build. In 21st IEEE/ACM International Conference on Automated Software Engineering, 2006. ASE'06. IEEE, 189--198. Google ScholarDigital Library
- Michael Hilton, Timothy Tunnell, Kai Huang, Darko Marinov, and Danny Dig. 2016. Usage, costs, and benefits of continuous integration in open-source projects. In Automated Software Engineering (ASE), 2016 31st IEEE/ACM International Conference on. IEEE, 426--437. Google ScholarDigital Library
- Ansong Ni and Ming Li. 2017. Cost-effective build outcome prediction using cascaded classifiers. In Proceedings of the 14th International Conference on Mining Software Repositories. IEEE Press, 455--458. Google ScholarDigital Library
- N. Nagappan T. Zimmermann, E. Giger H. Gall, and B. Murphy. 2009. Cross-project defect prediction: a large scale experiment on data vs. domain vs. process. In Proceedings of the ACM SIGSOFT symposium on The foundations of software engineering. ACM, 91--100. Google ScholarDigital Library
- Martin Zinkevich. 2003. Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the 20th International Conference on Machine Learning (ICML-03). 928--936. Google ScholarDigital Library
Index Terms
- ACONA: active online model adaptation for predicting continuous integration build failures
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