Software defect indicator explained

A Software Defect Indicator is a pattern that can be found in source code that is strongly correlated with a software defect, an error or omission in the source code of a computer program that may cause it to malfunction. When inspecting the source code of computer programs, it is not always possible to identify defects directly, but there are often patterns, sometimes called anti-patterns, indicating that defects are present.

Some examples of Software Defect Indicators:

• Disabled Code: Code has been written and the programmer has disabled it, or switched it off, without making it clear why it has been disabled, or when or whether it will be re-enabled.
• Routine Too Complex: A program (method, module, routine, subroutine, procedure, or any named block of code) contains more than 10 binary terms in conditional statements.^[1]
• Unused Variables: Unreferenced variables are a strong indicator for other errors.^[2]
• Number of Distinct Committers: The amount of unique developers that have made contributions to a project's commit history. This is a process metric that is useful in indicating software defects. ^[3]

See also

• Cyclomatic complexity
• Anti-pattern
• Computer program
• Computer programming
• Control flow
• Software engineering

External links

• NIST Special Publication 500-235 Structured Testing: A Testing Methodology Using the Cyclomatic Complexity Metric

Notes and References

1. 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.
2. 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.
3. 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.