Introduction

Software projects have a high risk of cost and schedule overruns. The ability to make reliable predictions of development effort and delay risks would help software companies to improve customer satisfaction, while reducing costs and optimizing delivery speed. Nowadays most software companies operate in highly dynamic environments that require software development teams to respond fast to change and continuously deliver business value. These circumstances call for novel estimation methods that are able to continuously learn and adapt to changes in the environment of modern software development teams.

Machine learning models have recently gained popularity in the areas of effort estimation and risk prediction. Recent work used deep learning to estimate the size of user stories through learning a team’s previous estimates. Other work focused on predicting delay risks and sprint delivery risks. Predicting risks requires the capability of processing large amounts of historical project data, using dynamic and contextual features, and tracking dependencies between (subsets of) development tasks.

Papers

The paper to be discussed in this session is: [1].

Bibliography

[1]
M. Choetkiertikul, H. K. Dam, T. Tran, T. Pham, A. Ghose, and T. Menzies, “A deep learning model for estimating story points,” IEEE Transactions on Software Engineering, vol. 45, no. 7, pp. 637–656, 2018.
[2]
P. Abrahamsson, R. Moser, W. Pedrycz, A. Sillitti, and G. Succi, “Effort prediction in iterative software development processes–incremental versus global prediction models,” in First international symposium on empirical software engineering and measurement (ESEM 2007), 2007, pp. 344–353.
[3]
M. Choetkiertikul, H. K. Dam, T. Tran, and A. Ghose, “Predicting the delay of issues with due dates in software projects,” Empirical Software Engineering, vol. 22, no. 3, pp. 1223–1263, 2017.
[4]
C. Maddila, C. Bansal, and N. Nagappan, “Predicting pull request completion time: A case study on large scale cloud services,” in Proceedings of the 2019 27th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering, 2019, pp. 874–882.
[5]
R. Kikas, M. Dumas, and D. Pfahl, “Using dynamic and contextual features to predict issue lifetime in GitHub projects,” in 2016 IEEE/ACM 13th working conference on mining software repositories (MSR), 2016, pp. 291–302.