Mills’ Error Seeding Model consists of the following steps
- Introduce Errors: Errors are intentionally introduced into the software code, in a controlled and systematic manner.
- Conduct Testing: The software is then tested to see if the introduced errors are detected.
- Measure Results: The results of the testing process are measured and analyzed to determine the effectiveness of the software testing process.
- Improve Testing Process: Based on the results of the testing process, the testing process can be improved to increase its effectiveness in detecting errors.
- The Mills’ Error Seeding Model is useful for improving the software testing process and for evaluating the effectiveness of the testing process. It can help to identify weaknesses in the testing process and to make improvements that will result in a more effective and efficient testing process.
Mills’ Error Seeding Model – Software Engineering
Mills’error seeding model proposed an error seeding method to estimate the number of errors in a program by introducing seeded errors into the program. From the debugging data, which consists of inherent errors and induced errors, the unknown number of inherent errors could be estimated. If both inherent errors and induced errors are equally likely to be detected, then the probability of k induced errors in r removed errors follows a hypergeometric distribution which is given by
Drawbacks:
- It is expensive to conduct testing of the software and at the same time, it increases the testing effort.
- This method was also criticized for its inability to determine the type, location, and difficulty level of the induced errors such that they would be detected equally likely as the inherent errors.
Another realistic method for estimating the residual errors in a program is based on two independent groups of programmers testing the program for errors using independent sets of test cases. Suppose that out of a total number of N initial errors, the first programmer detects n1 errors (and does not remove them at all) and the second independently detects r errors from the same program. Assume that k common errors are found by both programmers. If all errors have an equal chance of being detected, then the fraction detected by the first programmer (k) of a randomly selected subset of errors (e.g., r) should equal the fraction that the first programmer detects (n1) of the total number of initial errors N. In other words,
The Mills’ Error Seeding Model, also known as the Mills’ Model, is a software testing technique that was developed by E.K. Mills in the early 1980s. The model is based on the idea that faults, or errors, are intentionally introduced into the software code in order to test the effectiveness of the software’s testing process.