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Maintenance Effort Ineffectiveness

The Maintenance Effort Ineffectiveness (MEI) is a DETANGLE® Performance Indicator that measures the ineffectiveness of fixing issues of the “Defect Issue Category”. This filter allows selecting issue types capturing defects in the issue tracker and complements the (Primary) Effort Effectiveness performance indicator.

MEI takes the ratio of effort versus new code spent on fixing issues like defects. Measuring “Effort” and “New Code” are explained as part of the Primary Effort Effectiveness metric.

Since MEI is a measure of ineffectiveness it is computed in a different way than Primary Effort Effectiveness (PEE). In contrast to Primary Effort Effectiveness, Maintenance Effort Ineffectiveness takes the opposite ratio of effort versus new code. Due to technical reasons it can NOT be considered as a percentage value and it comes with its own specific thresholds.

The ratio of Effort versus New Code allows measuring whether code has been mainly modified when fixing defect issues instead of adding new code. Adding code instead of modifying the existing one indicates that code has been missing and functionality or tests have not been completely implemented.

MEI has a range of values beyond zero:

  • Mainly adding code by fixing defect issues results in a value between 1.0 and 1.5.
  • In case of many code modifications when fixing defect issues the value lies between 1.5 and 2.0.
  • Removing a lot of code, e.g. as part of refactoring several code places delivers values even higher than 2.0 and should not be a substantial part of fixing defect issues.

The icons in the diagrams comply to certain thresholds:

  1. thumb down : the MEI value is lower than 1.5 in the last time period, thus it indicates mainly adding code which should not be part of defect issue fixing.
  2. exclamation mark (orange): the MEI value is over 2.0 in the last time period, thus it indicates refactoring which should not be part of defect issue fixing.
  3. thumb up : the MEI value is between 1.5 and 2.0  in the last time period, thus indicating mainly code modifications as usually done when fixing defect issues.

Maintenance Effort Ineffectiveness is calculated along the code structure of the code base, for files, folders, the whole system, and for each time period:

Granularity/
KPIs or Effort Metrics
per 

bug

per

“functional” issue

per 

file

per

folder

per

entire

system

Maintenance

Effort (cLOC)

x x x x
Maintenance

Effort %

x x x x
Maintenance Effort Ineffectiveness x x x x
Primary Effort (cLOC) x x x x
Primary

Effort %

x x x x
Primary

Effort Effectiveness %

x x x x
System Effort (cLOC) x x x

Take a look at DETANGLE® Metrics at a Glance for an overview of all metrics.

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