Software Project Management Brochure Template Page 17

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Software Project Management
17
metrics for requirements specification – the number of requests for
change (RFC) in specification, the number of new requirements, and the
RFC diagram (showing the dynamics of RFC over time);
metrics for software testing – these metrics are used to track the
percentage of SLOC covered by the testing process; increasing that
percentage reduces the number of errors to be discovered by the users and
increases the product’s quality;
metrics for software quality – they typically show the fault density (the
number of errors per 1 KSLOC) and fault arrival and closing rates; as a
rule of thumb, the product’s quality is satisfactory if the fault density is
lower than 0.25;
metrics for project risk – they measure confidence in the product’s ready-
to-deployment date (typically an S-shaped curve over time).
The most widely used metrics models include COCOMO [11], which is
based on measuring SLOC, function points analysis [2], [19], [28], [47], GQM
(Goal-Question-Metrics, based on systematic translation of the company’s goals
into the measurement process goals, and refinement by defining the concrete
measurements to perform in order to support the goals) [32], and Chidamber-
Kemerer’s metrics suite for object-oriented software projects (specifying metrics
for the number of methods per class, depth of inheritance tree, number of
children, etc.) [15].
Productivity
Generally, productivity is an output divided by the effort required to produce
that output. In software development, the output is a completed software
development project. In order to consider a software company’s productivity, it
is necessary to somehow translate that output into a meaningful measurement.
Ideally, software project managers should base output measurement on a
combination of a project’s size, functionality, and quality [34]. However, such a
measurement doesn’t yet exist.
On the other hand, various databases of software projects from different
business sectors are available nowadays. They make possible to select some
projects that closely resemble specific projects in a software development
organization, and use the selected projects as a reference for measuring the
productivity of the organization’s completed projects or estimating the
productivity of the organization’s new projects.
Alternatively, a company can compare their software development
productivity to that of similar projects analytically, i.e. by using some empirical
benchmarking equations. Such equations typically take the values of some key
productivity factors and use them to calculate productivity in function points per
hour. The values are discrete (1 – very low, 2 – low, 3 – average, 4 – high, 5 –
very high). The key productivity factors include customer participation, staff

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