Question: Predicting software defects. Refer to the PROMISE Software Engineering Repository data on 498 modules of software code written in "C" language for a NASA spacecraft instrument, saved in the SWDEFECTS file. (See Exercise 3.74, pp. 154-155) Recall that the software code in cach module was evaluated for defects; 49 were classified as "true" (i.e., module has defective code), and 449 were classified as "false" (i.e., module has correct code). Consider these to be independent random samples of software code modules. Researchers predicted the defect status of each module using the simple algorithm, "If number of lines of code in the module exceeds 50 , predict the module to have a defect." The SPSS printout below shows the number of modules in each of the two samples that were predicted to have defects (PRED_LOC = "yes") and predicted to have no defects (PRED_LOC = "no"), Now, define the accuracy rate of the algorithm as the proportion of modules that were correctly predicted. Compare the accuracy rate of the algorithm when applied to modules with defective code to the accuracy rate of the algorithm when applied to modules with correct code. Use a 99% confidence interval.

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