Understanding Software Reliability Metrics

Software reliability is a crucial quality attribute that measures the probability of failure-free software operation for a specified period of time in a specified environment. Describing and measuring software reliability often involves using different time scales, depending on the context and the specific reliability model being applied.

Time Scales for Describing Software Reliability

Several time scales are commonly used to describe software reliability. Let's look at the ones mentioned in the question:

• Execution Time (A): This refers to the actual processor time (CPU time) spent executing the software. Reliability models based on execution time are often used because failures typically occur during software execution. The rate of failures is often considered proportional to the execution time. Measures like "failures per CPU hour" use this time scale. It directly relates reliability to the actual usage and stress on the software code.
• Calendar Time (B): This is the ordinary wall-clock time that passes, irrespective of whether the software is running or not. Calendar time is important for project management, scheduling testing, and predicting release dates. It relates reliability to the real-world timeline. Reliability growth models often consider the relationship between execution time and calendar time, as the pace of testing and bug fixing in calendar time affects the reliability improvement over calendar time.
• Clock Time (C): Clock time can sometimes be used synonymously with calendar time or operational time. In the context of reliability, it often refers to the total elapsed time during which the system is operational or observed, including periods of execution and idle time within the operational window. While Execution Time focuses strictly on CPU cycles, Clock Time (or operational time) gives a broader view of reliability over the period the system is available for use, which is relevant to the end-user experience.

Different reliability models and contexts use these time scales to describe software reliability. For instance:

• Models focusing on the rate of defects discovered during testing might relate the number of failures to the cumulative execution time of test cases.
• Project managers tracking progress might use calendar time to predict when a certain reliability level will be achieved, considering the testing effort applied over that calendar time.
• Users experience reliability in terms of failure occurrences over the total operational clock time of the system.

Because software reliability can be analyzed and described from these different perspectives – the actual usage (execution time), the project timeline (calendar time), and the overall operational period (clock time) – all three time scales are relevant descriptors.

Conclusion

Software reliability can be described and measured using different time scales, including Execution Time, Calendar Time, and Clock Time. Each provides a valuable perspective on the software's dependability over time.

Therefore, software reliability is described with respect to (A) Execution Time, (B) Calendar Time, and (C) Clock Time.

Revision Table: Software Reliability Time Scales

Additional Information: Software Reliability Measurement

Beyond the time scales used, software reliability is often quantified using metrics such as:

• Mean Time Between Failures (MTBF): For repairable systems, this is the average time between consecutive failures. Often calculated as Total Operational Time / Total Number of Failures.
• Mean Time To Failure (MTTF): For non-repairable systems, this is the average time until the first failure occurs.
• Failure Rate: The frequency of failures occurring within a specific time interval (e.g., failures per hour of operation or per transaction).
• Availability: The probability that the system is operational at a given point in time. It depends on both MTBF and Mean Time To Repair (MTTR). Availability = MTBF / (MTBF + MTTR).

Reliability growth models use these metrics, often plotted against execution time or calendar time, to predict future reliability based on observed failure data during testing.