Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

A case study is presented using thirteen releases of a large industrial inventory tracking system. Several types of questions are addressed in this study. The first involved examining how faults are distributed over the different files. This included making a distinction between the release during which they were discovered, the lifecycle stage at which they were first detected, and the severity of the fault. The second category of questions we considered involved studying how the size of modules affected their fault density. This included looking at questions like whether or not files with high fault densities at early stages of the lifecycle also had high fault densities during later stages. A third type of question we considered was whether files that contained large numbers of faults during early stages of development, also had large numbers of faults during later stages, and whether faultiness persisted from release to release. Finally, we examined whether newly written files were more fault-prone than ones that were written for earlier releases of the product. The ultimate goal of this study is to help identify characteristics of files that can be used as predictors of fault-proneness, thereby helping organizations determine how best to use their testing resources.

REFERENCES

• 1: E. N. Adams. Optimizing Preventive Service of Software Products. IBM J. Res. Develop., Vol28, No1, Jan 1984, pp.2-14.
• 2: Victor R. Basili , Barry T. Perricone, Software errors and complexity: an empirical investigation0, Communications of the ACM, v.27 n.1, p.42-52, Jan. 1984 [doi>10.1145/69605.2085]
• 3: Norman E. Fenton , Niclas Ohlsson, Quantitative Analysis of Faults and Failures in a Complex Software System, IEEE Transactions on Software Engineering, v.26 n.8, p.797-814, August 2000 [doi>10.1109/32.879815]
• 4: Todd L. Graves , Alan F. Karr , J. S. Marron , Harvey Siy, Predicting Fault Incidence Using Software Change History, IEEE Transactions on Software Engineering, v.26 n.7, p.653-661, July 2000 [doi>10.1109/32.859533]
• 5: Les Hatton, Reexamining the Fault Density-Component Size Connection, IEEE Software, v.14 n.2, p.89-97, March 1997 [doi>10.1109/52.582978]
• 6: K-H. Moller and D. J. Paulish. An Empirical Investigation of Software Fault Distribution. Proc. IEEE First Internation Software Metrics Symposium, Baltimore, Md., May 21-22, 1993, pp.82-90.
• 7: John C. Munson , Taghi M. Khoshgoftaar, The Detection of Fault-Prone Programs, IEEE Transactions on Software Engineering, v.18 n.5, p.423-433, May 1992 [doi>10.1109/32.135775]

INDEX TERMS

Keywords:
empirical study, fault-prone, pareto, software faults, software testing