Software maintenance is an important activity in
software engineering. Over the decades, software
maintenance costs have been continually reported to
account for a large majority of software costs
[Zelkowitz 1979, Boehm 1981, McKee 1984, Boehm
1988, Erlikh 2000]. This fact is not surprising. On the
one hand, software environments and requirements are
constantly changing, which lead to new software
system upgrades to keep pace with the changes. On
the other hand, the economic benefits of software
reuse have encouraged the software industry to reuse
and enhance the existing systems rather than to build
new ones [Boehm 1981, 1999]. Thus, it is crucial for
project managers to estimate and manage the software
maintenance costs effectively. / Accurate cost estimation of software projects is
one of the most desired capabilities in software
development Process. Accurate cost estimates not only help
the customer make successful investments but also assist
the software project manager in coming up with appropriate
plans for the project and making reasonable decisions
during the project execution. Although there have been
reports that software maintenance accounts for the
majority of the software total cost, the software estimation
research has focused considerably on new development and
much less on maintenance. Now if we talk about real time
software system(RTSS) development cost estimation and
maintenance cost estimation is not much differ from simple
software but some critical factor are considered for RTSS
development and maintenance like response time of
software for input and processing time to give correct
output. As like simple software maintenance cost estimation
existing models (i.e. Modified COCOMO-II) can be used
but after inclusion of some critical parameters related to
RTSS.
A Hypothetical Expert input and an industry data set of
eighty completed software maintenance projects were used
to build the model for RTSS maintenance cost. The full
model, which was derived through the Bayesian analysis,
yields effort estimates within 30% of the actual 51% of
the time,outperforming the original COCOMO II model
when it was used to estimate theseprojects by 34%.
Further performance improvement was obtained when
calibrating the full model to each individual program,
generating effort estimates within 30% of the actual 80%
of the time.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/219511 |
| Date | 15 February 2012 |
| Creators | Chakraverti, Sugandha, Kumar, Sheo, Agarwal, S. C., Chakraverti, Ashish Kumar |
| Contributors | Department of CSE, MBU Solan, HP-173229, India, Department of Mathematics, KCCEC Greater Noida UP-201308, India, Department of CSE, BBDIT Ghaziabad, UP-201002, India |
| Publisher | IJCSN |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article, Technical Report |
| Relation | IJCSN-2012-1-1-2, http://ijcsn.org/publications.html |
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