Software defect indicator

<h2 id="see-also">See also</h2>
<ul><li><a href="/facts/Cyclomatic_complexity/m3FYrLNR">Cyclomatic complexity</a></li>
<li><a href="/facts/Anti-pattern/BSSJC6os">Anti-pattern</a></li>
<li><a href="/facts/Computer_program/e38jH9N9">Computer program</a></li>
<li><a href="/facts/Computer_programming/QmSsFnu0">Computer programming</a></li>
<li><a href="/facts/Control_flow/2XTDfErC">Control flow</a></li>
<li><a href="/facts/Software_engineering/KX6cE9kz">Software engineering</a></li></ul>

<h2 id="external-links">External links</h2>
<ul><li><a href="https://web.archive.org/web/20070521072828/http://hissa.ncsl.nist.gov/sw_assurance/strtest.html">NIST Special Publication 500-235 Structured Testing: A Testing Methodology Using the Cyclomatic Complexity Metric</a></li></ul>

<h2 id="references">References</h2>

<ol>
<li id="fn:1"><p>William T. Ward: "Software Defect Prevention Using McCabe's Complexity Metric", Hewlett-Packard Journal, April 1989, pp 64-69: Control-flow complexity has been correlated with low reliability and frequent errors. <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li>
<li id="fn:2"><p>David N. Card, Victor E. Church, and William W. Agresti: "An Empirical Study of Software Design Practices", IEEE Transactions on Software Engineering, SE-12, no. 2, February 1986, pp 264-71: 46 percent of routines with no unused variables had no errors compared to only 17 to 29 percent for those with one or more unreferenced variables. <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li>
<li id="fn:3"><p>Madeyski, Lech, and Marian Jureczko. “Which Process Metrics Can Significantly Improve Defect Prediction Models? An Empirical Study.” Software Quality Journal 23.3 (2015): 393–422. Web. <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li>
</ol>

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Software defect indicator