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This paper discusses improvements to conventional software reliability analysis models by making the assumptions on which they are based more realistic. In an actual project environment, sometimes no more information is available than reliability data obtained from a test report. The models described here are designed to resolve the problems caused by this constraint on the availability of reliability data. By utilizing the technical knowledge about a program, a test, and test data, we can select an appropriate software reliability analysis model for accurate quality assessment. The delayed S-shaped growth model, the inflection S-shaped model, and the hyperexponential model are proposed.

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This paper discusses the issues of test coverage measurement in industry and justijies the benefits of the measurement using a framework developed by the authors. Experience with the measurement is formalized and packaged so that other researchers in industry can share and reuse it. In the paper, function test of large-scale system software is defined and analyzed. Based on the discussions of function test, a framework for analyzing the function test error removal process is developed. An experience-based error removal model and a cost model are proven to be useful tools for justifving tesf coverage measurement during function test. Data obtained from a real project is analyzed using the framework for validation.