This paper considers a software reliability model which allows for two types of imperfect debuggings at each failure of the software system. For one type of imperfect debugging, a fault that causes the failure is imperfectly debugged without altering the fault contents of the software system. For the other type of imperfect debugging, the fault is not only imperfectly debugged, but also a new fault is generated and introduced into the system. The probability of perfect debugging is assumed to be an increasing function of the number of debuggings performed prior to the current failure of the system. Based on the software reliability model presented, we consider three profit models to determine the optimal software release times which maximize the expected software profit. These models consider: (1) constant life cycle, (2) random life cycle, (3) random life cycle and penalty cost which is imposed when the software is delivered late. The optimal release times are shown to be finite and unique. Numerical examples are provided for illustrative purposes.

In this paper, we construct a software availability model considering the number of restoration actions. We correlate the failure and restoration characteristics of the software system with the cumulative number of corrected faults. Furthermore, we consider an imperfect debugging environment where the detected faults are not always corrected and removed from the system. The time-dependent behavior of the system alternating between up and down states is described by a Markov process. From this model, we can derive quantitative measures for software availability assessment considering the number of restoration actions. Finally, we show numerical examples of software availability analysis.

Actual debugging actions during the testing phase in the software development and the operation phase are not always performed perfectly. In other words, all detected software faults are not corrected and removed certainly. Generally, this is called imperfect debugging. In this paper, we discuss a software reliability growth model considering imperfect debugging that faults are not always corrected/removed when they are detected. Defining a random variable representing the cumulative number of faults corrected up to a specified testing time, this model is described by a semi-Markov process. We derive various quantitative measures for software reliability assessment and show their numercal examples.