Formulation and preliminary test of an empirical theory of coordination in software engineering

ABSTRACT

Motivated by evidence that coordination and dependencies among engineering decisions in a software project are key to better understanding and better methods of software creation, we set out to create empirically testable theory to characterize and make predictions about coordination of engineering decisions. We demonstrate that our theory is capable of expressing some of the main ideas about coordination in software engineering, such as Conway's law and the effects of information hiding in modular design. We then used software project data to create measures and test two hypotheses derived from our theory. Our results provide preliminary support for our formulations.

REFERENCES

• 1: Victor R. Basili, The role of experimentation in software engineering: past, current, and future, Proceedings of the 18th international conference on Software engineering, p.442-449, March 25-29, 1996, Berlin, Germany
• 2: Victor R. Basili , Frank E. McGarry , Rose Pajerski , Marvin V. Zelkowitz, Lessons learned from 25 years of process improvement: the rise and fall of the NASA software engineering laboratory, Proceedings of the 24th International Conference on Software Engineering, May 19-25, 2002, Orlando, Florida [doi>10.1145/581339.581351]
• 3: John Seely Brown , Paul Duguid, Knowledge and Organization: A Social-Practice Perspective, Organization Science, v.12 n.2, p.198-213, March 2001 [doi>10.1287/orsc.12.2.198.10116]
• 4: Campbell, D.T. and Stanley, J.C. Experimental and Quasi-Experimental Designs for Research. Houghton Mifflin, Boston, MA, 1963.
• 5: Conway, M.E. How Do Committees Invent? Datamation, 14 (4). 28--31.
• 6: Crowston, K. A taxonomy of organizational dependencies and coordination mechanisms. in Malone, T.W., Crowston, K. and Herman, G. eds. Tools for Organizing Business Knowledge: The MIT Process Handbook, MIT Press, Cambridge, MA, in press.
• 7: Bill Curtis , Herb Krasner , Neil Iscoe, A field study of the software design process for large systems, Communications of the ACM, v.31 n.11, p.1268-1287, Nov. 1988 [doi>10.1145/50087.50089]
• 8: Chrysanthos Nicholas Dellarocas, A coordination perspective on software architecture: towards a design handbook for integrating software components, Massachusetts Institute of Technology, Cambridge, MA, 1996
• 9: Durfee, E.H. Organisations, Plans, and Schedules: An Interdisciplinary Perspective on Coordinating AI Systems. Journal of Intelligent Systems, 3 (2-4). 157--187.
• 10: David Gelernter , Nicholas Carriero, Coordination languages and their significance, Communications of the ACM, v.35 n.2, p.97-107, Feb. 1992 [doi>10.1145/129630.129635]
• 11: James D. Herbsleb , Rebecca E. Grinter, Splitting the organization and integrating the code: Conway's law revisited, Proceedings of the 21st international conference on Software engineering, p.85-95, May 16-22, 1999, Los Angeles, California, United States [doi>10.1145/302405.302455]
• 12: Herbsleb, J.D. and Mockus, A. An Empirical Study of Speed and Communication in Globally-Distributed Software Development. IEEE Transactions on Software Engineering, To appear.
• 13: James Hollan , Edwin Hutchins , David Kirsh, Distributed cognition: toward a new foundation for human-computer interaction research, ACM Transactions on Computer-Human Interaction (TOCHI), v.7 n.2, p.174-196, June 2000 [doi>10.1145/353485.353487]
• 14: Edwin Hutchins, The technology of team navigation, Intellectual teamwork: social and technological foundations of cooperative work, Lawrence Erlbaum Associates, Inc., Mahwah, NJ, 1990
• 15: Jackson, M. Problem Frames. Addison-Wesley, Boston, MA, 2001.
• 17: Thomas W. Malone , Kevin Crowston, The interdisciplinary study of coordination, ACM Computing Surveys (CSUR), v.26 n.1, p.87-119, March 1994 [doi>10.1145/174666.174668]
• 18: Audris Mockus , David M. Weiss, Globalization by Chunking: A Quantitative Approach, IEEE Software, v.18 n.2, p.30-37, March 2001 [doi>10.1109/52.914737]
• 19: Mook, D.G. In Defense of External Invalidity. American Psychologist, April. 379--387.
• 20: D. L. Parnas, On the criteria to be used in decomposing systems into modules, Communications of the ACM, v.15 n.12, p.1053-1058, Dec. 1972 [doi>10.1145/361598.361623]
• 21: Dewayne E. Perry , Nancy Staudenmayer , Lawrence G. Votta, People, Organizations, and Process Improvement, IEEE Software, v.11 n.4, p.36-45, July 1994 [doi>10.1109/52.300082]
• 22: Reif, F. Fundamentals of Statistical and Thermal Physics. McGraw-Hill, New York, 1965.
• 23: Simon, H.A. The structure of ill structured problems. Artificial intelligence, 4. 145--180.
• 24: Walter F. Tichy, Should Computer Scientists Experiment More?, Computer, v.31 n.5, p.32-40, May 1998 [doi>10.1109/2.675631]
• 25: Lawrence G. Votta , Adam Porter, Experimental software engineering: a report on the state of the art, Proceedings of the 17th international conference on Software engineering, p.277-279, April 24-28, 1995, Seattle, Washington, United States [doi>10.1145/225014.225040]
• 26: Diane B. Walz , Joyce J. Elam , Bill Curtis, Inside a software design team: knowledge acquisition, sharing, and integration, Communications of the ACM, v.36 n.10, p.63-77, Oct. 1993 [doi>10.1145/163430.163447]
• 27: Claes Wohlin , Per Runeson , Martin Höst , Magnus C. Ohlsson , Bjöorn Regnell , Anders Wesslén, Experimentation in software engineering: an introduction, Kluwer Academic Publishers, Norwell, MA, 2000

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.9 Management
          Subjects: Productivity

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.9 Management
          Subjects: Software process models (e.g., CMM, ISO, PSP); Programming teams

General Terms:
Design, Economics, Experimentation, Management, Measurement, Performance, Theory

Keywords:
Conway's Law, coordination, empirical studies, empirical theory, engineering decisions