Hardware Reference
In-Depth Information
have an approach guiding organizations for selecting an optimum set of measures.
This paper presented a model called “OMSD Model”, a systematic approach for deal-
ing with the challenge of 'finding an optimum measures set' out of the possibly large
set of measures.
In a nutshell, this model is developed to address one of the challenges organiza-
tions are facing; the risk for the failure of measurement programs due to improper
time and cost estimates, by minimizing the cost by supporting efficient and effective
measures selection process in organizations. There is little explicit discussion in the
literature about what constitutes a reasonable overhead for a measurement program.
In [6], it is stated that 90% of the practitioners reported to spend less than 3% of their
time on metrics-related work. OMSD model can also help the organizations to collect
such information so that they can also calculate the Return on Investment (ROI) for
initiating such programs.
Even though we evaluated the OMSD model by means of an industrial survey we
made to determine the factors considered in the model and by means of a thorough
experimentation of the rules of the model, in order to show evidences that the model
is valuable for the organizations, industrial case studies should be conducted.
One of the current constraints of the OMSD Model is that high levels of human
interaction are needed to enter the input required by the model such as measures
dependency, time and cost limits. Improvement and automation of this process can
reduce human effort resulting in less time and cost expense.
Other future works related to this study includes measures prioritization based on
the priority of the goals at Step 1 of GQM, developing a Measures pool that will make
initial measures selection easier (Step 3 of OMSD Model), incursion of new factors
based on more industrial surveys, industrial experimentation of the OMSD Model and
its integration with measurement frameworks other than GQM.
Acknowledgements.
We would like to thank Johan Holmgren for his support in the
development of the heuristics approach. We also thank the survey respondents for
their feedback on the model.
References
1.
Wang, Q., Li, M.: Measuring and improving software process in China. In: International
Symposium on Empirical Software Engineering, p. 10 (2005)
2.
Bundschuh, M., Dekkers, C.: The Measurement Compendium: Estimating and Bench-
marking Success with Functional Size Measurement. Springer, Heidelberg (2008)
3.
Goethert, W., Hayes, W.: Experiences in Implementing Measurement Programs. Technical
Note, Software Engineering Institute, Carnegie Mellon University, CMU/SEI-2001-TN-
026 (2001)
4.
Gopal, A., Krishnan, M.S., Mukhopadhyay, T., Goldenson, D.R.: Measurement Programs
in Software Development: Determinants of Success. IEEE Transactions on Software Engi-
neering 28(9), 863-875 (2002)
5.
Hall, T., Fenton, N.: Implementing Effective Software Metrics Programs. IEEE Soft-
ware 14(2), 55-65 (1997)
