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Quantitative analysis of unit verification as predictor in large scale software enginering

Tihana, Galinac Grbac; Runeson, Per LU and Darko, Huljenic (2015) In Software Quality Journal
Abstract
Unit verification, including software inspections and unit tests, is usually the first code verification phase in the software development process. However, principles of unit verification are weakly explored, mostly due to the lack of data, since unit verification data are rarely systematically collected and only a few studies have been published with such data from industry. Therefore, we explore the theory of fault distributions, originating in the quantitative analysis by Fenton and Ohlsson, in the weakly explored context of unit verification in large-scale software development. We conduct a quantitative case study on a sequence of four development projects on consecutive releases of the same complex software product line system for... (More)
Unit verification, including software inspections and unit tests, is usually the first code verification phase in the software development process. However, principles of unit verification are weakly explored, mostly due to the lack of data, since unit verification data are rarely systematically collected and only a few studies have been published with such data from industry. Therefore, we explore the theory of fault distributions, originating in the quantitative analysis by Fenton and Ohlsson, in the weakly explored context of unit verification in large-scale software development. We conduct a quantitative case study on a sequence of four development projects on consecutive releases of the same complex software product line system for telecommunication exchanges. We replicate the operationalization from earlier studies, analyzed hypotheses related to the Pareto principle of fault distribution, persistence of faults, effects of module size, and quality in terms of fault densities, however, now from the perspective of unit verification. The patterns in unit verification results resemble those of later verification phases, e.g., regarding the Pareto principle, and may thus be used for prediction and planning purposes. Using unit verification results as predictors may improve the quality and efficiency of software verification. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
in press
subject
keywords
Software fault distributions - Unit verification - Software metrics - Empirical research - Replication
in
Software Quality Journal
publisher
Springer
external identifiers
  • scopus:84925610743
ISSN
0963-9314
DOI
10.1007/s11219-015-9273-7
language
English
LU publication?
yes
id
cd1068cd-e380-4f49-815d-f171b1613e35 (old id 5240122)
date added to LUP
2015-03-31 13:13:34
date last changed
2017-01-01 06:17:23
@article{cd1068cd-e380-4f49-815d-f171b1613e35,
  abstract     = {Unit verification, including software inspections and unit tests, is usually the first code verification phase in the software development process. However, principles of unit verification are weakly explored, mostly due to the lack of data, since unit verification data are rarely systematically collected and only a few studies have been published with such data from industry. Therefore, we explore the theory of fault distributions, originating in the quantitative analysis by Fenton and Ohlsson, in the weakly explored context of unit verification in large-scale software development. We conduct a quantitative case study on a sequence of four development projects on consecutive releases of the same complex software product line system for telecommunication exchanges. We replicate the operationalization from earlier studies, analyzed hypotheses related to the Pareto principle of fault distribution, persistence of faults, effects of module size, and quality in terms of fault densities, however, now from the perspective of unit verification. The patterns in unit verification results resemble those of later verification phases, e.g., regarding the Pareto principle, and may thus be used for prediction and planning purposes. Using unit verification results as predictors may improve the quality and efficiency of software verification.},
  author       = {Tihana, Galinac Grbac and Runeson, Per and Darko, Huljenic},
  issn         = {0963-9314},
  keyword      = {Software fault distributions - Unit verification - Software metrics - Empirical research - Replication},
  language     = {eng},
  publisher    = {Springer},
  series       = {Software Quality Journal},
  title        = {Quantitative analysis of unit verification as predictor in large scale software enginering},
  url          = {http://dx.doi.org/10.1007/s11219-015-9273-7},
  year         = {2015},
}