An empirical characterization of scientific software development projects according to the Boehm and Turner model: A progress report

Authors:
Carlton A. Crabtree;A. Gunes Koru;Carolyn Seaman;Hakan Erdogmus
Affiliations:
UMBC, Department of Information Systems, Baltimore, MD, USA;UMBC, Department of Information Systems, Baltimore, MD, USA;UMBC, Department of Information Systems, Baltimore, MD, USA;National Research Council of Canada, (NRC/CNRC), Ottawa, Ontario, Canada
Venue:
SECSE '09 Proceedings of the 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering
Year:
2009

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Abstract

A number of recent studies reported on the success of applying agile methods in scientific software development projects. These studies found that agile methods are well suited to the exploratory, iterative, and collaborative nature of scientific inquiry. However, these findings might not be applicable in all situations pertaining to scientific software development projects. In addition, they only constitute a subset of the important factors while deciding which development methods and practices should be adopted. Therefore, it becomes important to conduct further research before making recommendations regarding the adoption of certain development methods and practices in this domain. In this progress report, we discuss our on-going research that will empirically study the characteristics of various scientific software development projects according to a model suggested by Boehm and Turner. We plan to conduct interviews and collect data from various scientific software development projects in the Baltimore-Washington area. We expect that our qualitative results will increase our understanding of the characteristics in those projects favoring plan-driven approaches or agile methods, and the needs and conditions associated with those characteristics. Our research provides guidance to scientific software developers by enhancing their capacity to evaluate and understand their own project characteristics and select effective software practices. As a long-term benefit in the same direction, our qualitative results will generate a set of hypotheses that can be tested in different project environments to better understand and categorize scientific software development projects. Consequently, in the future, more generalizable and actionable recommendations can be made for scientific software development projects.