Open Source Software (OSS) is widely used and is becoming a significant and
irreplaceable part of the software engineering community. Today a huge number of OSS
exist. This becomes a problem if one needs to choose from such a large pool of OSS
candidates in the same category. An OSS maturity model that facilitates the software
assessment and helps users to make a decision is needed. A few maturity models have
been proposed in the past. However, the parameters in the model are assigned not based
on experimental data but on human experiences, feelings and judgments. These models
are subjective and can provide only limited guidance for the users at the best.
This dissertation has proposed a quantitative and objective model which is built
from the statistical perspective. In this model, seven metrics are chosen as criteria for
OSS evaluation. A linear multiple-regression model is created to assign a final score
based on these seven metrics. This final score provides a convenient and objective way
for the users to make a decision. The coefficients in the linear multiple-regression model
are calculated from 43 OSS. From the statistical perspective, these coefficients are considered random variables. The joint distribution of the coefficients is discussed based
on Bayesian statistics. More importantly, an updating rule is established through
Bayesian analysis to improve the joint distribution, and thus the objectivity of the
coefficients in the linear multiple-regression model, according to new incoming data.
The updating rule provides the model the ability to learn and improve itself continually.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-1454 |
| Date | 15 May 2009 |
| Creators | Zhang, Dongmin |
| Contributors | Simmons, Dick |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
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