Hardware Reference
In-Depth Information
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Inadequate data collection and wrong interpretations of data that leads to ineffec-
tive decision making,
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Lack of trained and expert resources required to dedicate to measurement,
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Lack of management support for the measurement program,
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The cost for measurement not planned according to the organization's budget.
Various frameworks and models have been developed to overcome some of the above
mentioned difficulties software organizations are facing, such as Goal Question Met-
ric (GQM) paradigm [6], [7], Goal Question Indicator Model (GQIM) [8] and Meas-
urement Information Model [9]. GQM; developed by Basili and Weiss [6] and then
improved by Basili and Rombach [7], is one of the well-known frameworks used in
deriving measures from organization or business goals. Two reasons for the success
of GQM are stated in [10] as that it is adaptable to many different organizations and
environments and it aligns with the organizational directions and goals.
However, although these frameworks help the organizations to collect data on the
measures which are required to fulfill the goals of the organization, none of those
explicitly support the need to limit the number of measures to be collected [10]. In
fact, one of the major constraints for the organizations which are also one of the sig-
nificant reasons for measurement programs failure is the associated cost for the meas-
urement programs.
A well-known figure, Tom De Marco said [11]; “
Metrics are good, more would be
better and most is best but the importance of cost and time factor cannot be denied.
Faced with a high number of measures to be collected for software process improve-
ment reasons, most organizations want to know whether all those measures are
equally important or some are more important than the others
”. Two out of ten prob-
lems leading to failure in the implementation of software measurement programs are
reported by Howard Rubin to be: the intensive use of a single measure or, conversely,
the use of too many [12].
According to [13], software measurement programs usually fail as they require ex-
pert judgment for selecting appropriate number of measures in relation to the organ-
izational goals. The mapping of goals with appropriate measures requires experienced
resources in the field of software measurement. These goals are required to be priori-
tized. One important point to be considered is that this prioritization might also be
influenced by the cost associated to measures collection. Therefore, software organi-
zations require deciding on an optimum set of measures which are good enough and
at the same time less costly.
This paper suggests a model named 'Optimum Measure Set Decision (OMSD)
Model' which extends the GQM approach and aims to fill in the gap discussed above
by facilitating the managers in selecting an optimum set of measures from a large
number of possible measures. To develop the model, we identified the factors which
are significant when deciding on the measures to be collected as well as optimizing
the cost associated based on the findings of an extensive literature review and getting
feedback from the industry by conducting a survey. Then, we tested the model by
means of some sample cases we created.
The paper is divided into five main sections. Section 1 provides an introduction.
Section 2 explains the proposed OMSD model. Section 3 presents the empirical studies
