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Software Application Complexity and Code Complexity
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HEY is a Hardware / Software Engineer, Programmer / Analyst, Reliability, Safety, and Systems Engineer with world-class expertise in hardware / software engineering, reliability, safety, systems, quality assurance, risk, human / machine interface, design, test, data mining, finite state analysis, statistical analysis, expert systems, artificial intelligence, and robotics.
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There were two primary problems that this software reliability task was to solve:
(1) Complexity
Performing reliability analysis on software (S/W) that underwent changes due to repair, upgrades, and modification. This was based on the amount of code, type of code, number of interrupts, processor speed, and the interaction of S/W Functions.
(2) Accurate Operational Profile
The set of operations that the S/W was to execute, the probability of occurrence, identification of the potential users, identification of all potential conditions in the operating environment. Solving this problem necessitated the software be tested for the maximum conceivable scenarios associated with the operation profile.
For Application Complexity, S/W reliability was to:
- Test the Inputs and Outputs
- Create methods to Force S/W to exhibit specific behaviors
- Ensure Error Handling Routines Work Properly
- Determine specific behaviors caused by specific combinations of stored data
For Code Complexity for CEV, S/W reliability was to:
- Establish how the structure and semantics of the S/W interact with its environment
- Use Algorithms such as: Propagation, Infection, and Execution
- Perform Infection Analysis
- Use Runtime Created States
Software reliability made application of source S/W-based metrics and execution-based reliability modeling. It addressed both usage and Failure of software. It calculated S/W failure and repair rates using historical data and S/W reliability prediction models.
Examples of those models were:
(1) Cleanroom
This procedure regards reliability as a behavioral characteristic, not a structural characteristic. Increase in complexity does not mean increased failure rate, so decrease of reliability. Failure rate prediction is accomplished by addressing the S/W usage associated with the executable code of the source S/W.
(2) Musa
This method began reliability prediction of fault rates at the time system testing commenced.
(3) Putmam
This process performed prediction of fault rates at varying areas in the development process.
(4) Rome Laboratory (RAC)
Predictions of fault rates were made at delivery time. This was to predict the total number of inherent failures.
In addition to prediction, software reliability used estimation methods. These processes established the number of faults in the S/W prior to test. This enabled a formulating criterion that established when a S/W test could be stopped.
Examples estimation method models were:
(1) Loyd-Lipow
This process identified the number of faults remaining to be detected. These faults did not have to be immediately correctable when detected.
(2) Weibull Distribution Model
This procedure accommodated increasing and decreasing fault rates
(3) Bayesian Model
This method used prior knowledge. The Shooman model adjusted to the changing size of the S/W. The Goel-Okumoto model addressed condition of faults causing other faults.
BENEFITS
Accurate Software Prediction
Software errors caught during development resulted in less software failures after release.
Fewer failures meant less S/W repair
Cost of maintenance was reduced
Addressing complexity and operational profile results in S/W tailored to operational environment reduced hazards and increased safety.
The number of detrimental events was minimized or mitigated
A greater amount of error-free S/W was generated
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