solution

Software defects. The PROMISE Software Engineering Repository at the University of Ottawa provides researchers with data sets for building predictive software models. (See Exercise 2.160, p. 145.) Data on 498 modules of software code written in “C” language for a NASA spacecraft instrument are saved in the file. Recall that each module was analyzed for defects and classified as “true” if it contained defective code and “false” if not. One algorithm for predicting whether or not a module has defects is “essential complexity” (denoted EVG), where a module with at least 15 subflow graphs with D-structured primes is predicted to have a defect. When the method predicts a defect, the predicted EVG value is “yes”; otherwise, it is “no.” Would you recommend the essential complexity algorithm as a predictor of defective software modules? Explain.

Exercise 2.160

Software defects. The Promise Software Engineering Repository is a collection of data sets available to serve businesses in building predictive software models. One such data set, saved in the accompanying file, contains information on 498 modules of software code. Each module was analyzed for defects and classified as “true” if it contained defective code and “false” if not. Access the data file and produce a bar graph or a pie chart for the defect variable. Use the graph to make a statement about the likelihood of defective software code.