The bibliographic entry was discovered and compiled by a machine, and let through without anybody vouching for it. The publication itself is the authors' own work — this badge says nothing about it.

An Overview of Software Defect Density: A Scoping Study

The work

TitleAn Overview of Software Defect Density: A Scoping Study
AuthorsSyed Muhammad Ali Shah; Maurizio Morisio; Marco Torchiano
Typeconference paper
Year2012
Citekeyshah2012overview

Where it appeared

Published in2012 19th Asia-Pacific Software Engineering Conference
PublisherIEEE
Pages406--415

Identifiers

DOI10.1109/apsec.2012.93
OpenAlexW2101912966

Access

Landing pagehttps://doi.org/10.1109/apsec.2012.93

Abstract

Context: Defects are an ineludible component of software, Defect Density (DD) - defined as the number of defects divided by size - is often used as a related measure of quality. Project managers and researchers alike would benefit a lot from overview DD figures from software projects, the former for decision making the latter for state-of-the-practice assessment. Objective: In this paper, we collect and aggregate DD figures published in literature, in addition we characterize DD as a function of different project factors in terms of central tendency and dispersion. The factors considered include development mode -open vs. closed source-, programming language, size, and age. Results: We were able to identify 19 papers reporting defect density figures concerning 109 software projects. The mean DD for the studied sample of projects is 7.47 post release defects per thousand lines of code (KLoC), the median is 4.3 with a standard deviation of 7.99. Development mode, is characterized by statistically meaningful different DD, the same for Java vs. C. Besides, in the studied sample large projects exhibited lower DD than medium and small projects. Conclusion: The study is a first step in collecting and analyzing DD figures for the purpose of characterizing one important aspect of software quality. These figures can be used both by researchers and project managers interested to evaluate their projects. Further work is needed to extend the data set and to identify predictors of defect density.

Where this came from

How it got herethe agent went looking · found via openalex
First seen2026-08-04
Standingnot denied
Approved2026-08-07

Cite it as

@inproceedings{shah2012overview,
  title = {An Overview of Software Defect Density: A Scoping Study},
  author = {Syed Muhammad Ali Shah and Maurizio Morisio and Marco Torchiano},
  year = {2012},
  booktitle = {2012 19th Asia-Pacific Software Engineering Conference},
  pages = {406--415},
  publisher = {IEEE},
  doi = {10.1109/apsec.2012.93},
  url = {https://doi.org/10.1109/apsec.2012.93},
}

This record lives at https://refs.drheap.org/shah2012overview/ and will keep doing so.