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Introducing Quality Models Based On Joint Probabilities

EasyChair Preprint no. 128

2 pagesPublished: May 11, 2018

Abstract

Multi-dimensional goals can be formalized in so-called quality models. Often, each dimension is assessed with a set of metrics that are not comparable; they come with different units, scale types, and distributions of values. Aggregating the metrics to a single quality score in an ad-hoc manner cannot be expected to provide a reliable basis for decision making. Therefore, aggregation needs to be mathematically well-defined and interpretable. We present such a way of defining quality models based on joint probabilities. We exemplify our approach using a quality model with 30 standard metrics assessing technical documentation quality and study ca. 20,000 real-world files. We study the effect of several tests on the independence and results show that metrics are, in general, not independent. Finally, we exemplify our suggested definition of quality models in this domain.

Keyphrases: Bayesian networks, joint probability, quality assessment, software metrics

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:128,
  author = {Maria Ulan and Welf Löwe and Morgan Ericsson and Anna Wingkvist},
  title = {Introducing Quality Models Based On Joint Probabilities},
  howpublished = {EasyChair Preprint no. 128},
  doi = {10.29007/sgs5},
  year = {EasyChair, 2018}}
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