Feature subset selection can improve software cost estimation accuracy

ABSTRACT

Cost estimation is important in software development for controlling and planning software risks and schedule. Good estimation models, such as COCOMO, can avoid insufficient resources being allocated to a project. In this study, we find that COCOMO's estimates can be improved via WRAPPER- a feature subset selection method developed by the data mining community. Using data sets from the PROMISE repository, we show WRAPPER significantly and dramatically improves COCOMO's predictive power.

REFERENCES

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INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.9 Management

Additional Classification:
  K. Computing Milieux
  K.6 MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS
      K.6.3 Software Management

General Terms:
Algorithms, Economics, Experimentation, Measurement, Theory, Verification

Keywords:
COCOMO, LSR, M5, WRAPPER, feature subset selection