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Studying the Effects of Code Inspection and Structural Testing on Software Quality

: Laitenberger, O.

Volltext urn:nbn:de:0011-px-572146 (139 KByte PDF)
MD5 Fingerprint: 5b97dfcda25c6f890b6e3d2ebe1d0af9
Erstellt am: 09.08.2000

Kaiserslautern, 1998, 10 S. : Ill., Lit.
IESE-Report, 024.98/E
Reportnr.: IESE-Report 024.98/E
Bericht, Elektronische Publikation
Fraunhofer IESE ()
controlled experiment; coverage criteria; software inspection; software quality; structural testing

The most common techniques for detecting defects in software artifacts are inspection and testing. Since both techniques are effort consuming, they are often presented as being counterparts or even rivals rather than as being complementary. Hence, few controlled empirical studies investigate the effects of inspection and testing on software quality when applied in sequence. This paper contributes a controlled experiment to shed light on this issue. Twenty subjects performed sequentially code inspection and structural testing using different coverage values as test criteria on a C-code module. We adopted this sequence because it is recommended for use in industry. The results of this experiment show that inspection significantly outperforms structural testing with respect to (cost-)effectiveness for defect detection. Furthermore, the experimental results indicate little evidence to support the hypothesis that structural testing detects defects of a particular class that were missed b y inspection and vice versa. These findings lead us to the conclusion that inspection and structural testing do not complement each other well. In fact, prior inspection seems to hinder the (cost-)effectiveness of structural testing. Since inspection out-performs structural testing and since 39 percent (on average) of the defects were not detected at all, it might be more valuable to apply inspection together with other testing techniques, such as boundary value analysis, to achieve a better defect coverage. We are aware that a single experiment does not provide conclusive evidence. Hence, we consider it only one step in the determination of the optimal mix of defect detection techniques. Additional research as well as replication of this experiment are required to make further progress into this direction.