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  1. Proceedings of the The 12th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2016)
  2. Hidden Markov Models for the Prediction of Developer Involvement Dynamics and Workload
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Forecasting Communication Behavior in Student Software Projects
Estimating Story Points from Issue Reports
Search Based Training Data Selection For Cross Project Defect Prediction
On the Terms Within- and Cross-Company in Software Effort Estimation
Measuring the Stylistic Inconsistency in Software Projects using Hierarchical Agglomerative Clustering
An Empirical Evaluation of Distribution-based Thresholds for Internal Software Measures
Data Sets and Data Quality in Software Engineering: Eight Years On
Hidden Markov Models for the Prediction of Developer Involvement Dynamics and Workload
Predicting the Popularity of GitHub Repositories
A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability

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Hidden Markov Models for the Prediction of Developer Involvement Dynamics and Workload

Content Provider ACM Digital Library
Author Honsel, Verena Grabowski, Jens Herbold, Steffen
Abstract The evolution of software projects is driven by developers who are in control of the developed artifacts. When analyzing the behavior of developers, the observable behaviors are, e.g., commits, messages, or bug assignments. For defining dynamic activities and workload of developers, we consider underlying characteristics, which means the level of involvement according to their role in the project. In this paper, we propose to employ Hidden Markov Models (HMMs) to model this underlying behavior given the observable behavior as input. For this, we observe monthly commits, bugfixes, mailing list activity, and bug comments for each developer over the project duration. As output we get a model for each developer describing how likely it is to be in a low, medium, or high contribution state of every point in time. As a result, we discovered that same developer types exhibit similar models in terms of state patterns and transition matrices, which represent their involvement dynamics. Although the workload of the different developer roles related to this is more complex to model, we created a general model which performs nearly as well as individual developer contribution models. Moreover, to demonstrate the practical applicability, we present an example of the usage of our approach in project planning.
Starting Page 1
Ending Page 10
Page Count 10
File Format PDF
ISBN 9781450347723
DOI 10.1145/2972958.2972960
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-09-09
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Software development Hidden markov models Developer roles
Content Type Text
Resource Type Article
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