Software reliability growth with test coverage

• Published in: IEEE transactions on reliability Vol. 51; no. 4; pp. 420 - 426
• Main Authors: Malaiya, Y.K., Li, M.N., Bieman, J.M., Karcich, R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2002; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automatic testing; Computation; Computer programs; Computer science; Computerized monitoring; Defects; Growth models; Knee; Mathematical analysis; Mathematical models; NASA; Predictive models; Software reliability; Software testing; Space technology; Strategy; Studies; Sun; Testing time
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2002.804489

"Software test-coverage measures" quantify the degree of thoroughness of testing. Tools are now available that measure test-coverage in terms of blocks, branches, computation-uses, predicate-uses, etc. that are covered. This paper models the relations among testing time, coverage, and reliability. An LE (logarithmic-exponential) model is presented that relates testing effort to test coverage (block, branch, computation-use, or predicate-use). The model is based on the hypothesis that the enumerable elements (like branches or blocks) for any coverage measure have various probabilities of being exercised; just like defects have various probabilities of being encountered. This model allows relating a test-coverage measure directly with defect-coverage. The model is fitted to 4 data-sets for programs with real defects. In the model, defect coverage can predict the time to next failure. The LE model can eliminate variables like test-application strategy from consideration. It is suitable for high reliability applications where automatic (or manual) test generation is used to cover enumerables which have not yet been tested. The data-sets used suggest the potential of the proposed model. The model is simple and easily explained, and thus can be suitable for industrial use. The LE model is based on the time-based logarithmic software-reliability growth model. It considers that: at 100% coverage for a given enumerable, all defects might not yet have been found.