As a part of the quality management, product defectiveness prediction is vital for small software
organizations as for instutional ones. Although for defect prediction there have been conducted a
lot of studies, process enactment data cannot be used because of the difficulty of collection.
Additionally, there is no proposed approach known in general for the analysis of process
enactment data in software engineering.
In this study, we developed a method to show the applicability of process enactment data for
defect prediction and answered &ldquo / Is process enactment data beneficial for defect prediction?&rdquo / ,
&ldquo / How can we use process enactment data?&rdquo / and &ldquo / Which approaches and analysis methods can our
method support?&rdquo / questions. We used multiple case study design and conducted case studies
including with and without process enactment data in a small software development company. We
preferred machine learning approaches rather than statistical ones, in order to cluster the data
which includes process enactment informationsince we believed that they are convenient with the
pattern oriented nature of the data.
By the case studies performed, we obtained promising results. We evaluated performance values of prediction models to demonstrate the advantage of using process enactment data for the
prediction of defect open duration value. When we have enough data points to apply machine
learning methods and the data can be clusteredhomogeneously, we observed approximately 3%
(ranging from -10% to %17) more accurate results from analyses including with process enactment
data than the without ones.
Keywords:
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12614516/index.pdf |
| Date | 01 June 2012 |
| Creators | Sivrioglu, Damla |
| Contributors | Demirors, Onur |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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