Spm unit v-software reliability-
Download as PPTX, PDF1 like1,455 views
This document discusses software reliability. It defines software reliability as the probability of failure-free software operation for a specified period of time in a specified environment. Traditional methods to improve reliability include manual testing, code reviews, and coding standards. Reliability can be measured using metrics like MTBF. Software reliability models discussed include error seeding, reliability growth, and non-homogeneous Poisson processes. Choice of model depends on factors like the application area and operational characteristics.
![Prof. Kanchana Devi
11
)
],[infailuresofNumber
()(
f
A typical measure (failures per unit time) is the
failure intensity (rate) given as:
where = program CPU time (in a time shared
computer) or wall clock time (in an
embedded system).](https://image.slidesharecdn.com/spm-unitv-softwarereliability-160106060715/85/Spm-unit-v-software-reliability-11-320.jpg)
Spm unit v-software reliability-
- 1. SPM-UNIT - V SOFTWARE RELIABILITY Prof. Kanchana Devi
- 2. Software Reliability Prof. Kanchana Devi 2 Categorising and specifying the reliability of software systems Informal Definition: Reliability is a measure of how well system users think it provides the services they require. Probability of a software component will produce an incorrect output Software can continue to operate after a bad result
- 3. ……… Prof. Kanchana Devi 3 Software Reliability is the probability of failure- free software operation for a specified period of time in a specified environment. Software Reliability is also an important factor affecting system reliability. It differs from hardware reliability in that it reflects the “design perfection”, rather than manufacturing perfection. The high complexity of software is the major contributing factor of Software Reliability problems.
- 4. Traditional Methods For Improving Software Reliability Prof. Kanchana Devi 4 Three main techniques are used in industrial and open source projects to improve software reliability: Manual Testing Code Reviews: Modifications are reviewed by experienced developers before being committed to the code base. Coding Standards: Requiring that all developers adhere to a set of rules when writing or maintaining code. Coding standards can improve source code readability, making it easier to spot defects, and Ban the use of programming idioms that are arguably dangerous.
- 5. Reliability Problems Prof. Kanchana Devi 5 They depend fundamentally on human reasoning and judgments They do not provide guarantees
- 6. Measuring Reliability Prof. Kanchana Devi 6 A simple measure of reliability can be given as: MTBF = MTTF + MTTR , where MTBF is mean time between failures MTTF is mean time to fail MTTR is mean time to repair
- 7. Software Reliability Models Prof. Kanchana Devi 7 Error Seeding Reliability growth Non-Homogeneous Poisson process (NHPP)
- 8. Error Seeding Prof. Kanchana Devi 8 Estimates the number of errors in a program. Errors are divided into Indigenous Errors Induced (seeded) Errors. The unknown number of indigenous errors is estimated from the number of induced errors. The ratio of the two types of errors obtained from the testing data.
- 9. Reliability Growth 9 Measures and predicts the improvement of reliability through the testing process using a growth function to represent the process. Growth Function has two types of variables: Independent Variables Dependent Variables Independent Variables of the growth function could be time and number of test cases (or testing stages) Dependent Variables can be reliability, failure rate or cumulative number of errors detected.
- 10. Non-homogeneous Poisson process (NHPP) Prof. Kanchana Devi 10 Provide an analytical framework for describing the software failure phenomenon during testing. The main issue is to estimate the mean value function of the cumulative number of failures.
- 11. Prof. Kanchana Devi 11 ) ],[infailuresofNumber ()( f A typical measure (failures per unit time) is the failure intensity (rate) given as: where = program CPU time (in a time shared computer) or wall clock time (in an embedded system).
- 12. Issues in SR: Prof. Kanchana Devi 12 SR Growth models are generally “black box” - no easy way to account for a change in the “operational profile” “Operational profile”: description of the input events expected to occur in actual software operation – how it will be used in practice
- 13. Prof. Kanchana Devi 13 Many models have been proposed, perhaps the most prominent are: Musa Basic model Musa/Okomoto Logarithmic model Some models work better than others depending on the application area and operating characteristics: i.e. interactive? data intensive? control intensive? real-time?
- 14. Choice of Model - Basic Model: Prof. Kanchana Devi 14 For studies or predictions before execution and failure data available Using study of faults to determine effects of a new software engineering technology The program size is changing continually or substantially (i.e. during integration)
- 15. Logarithmic Model Prof. Kanchana Devi 15 System subjected to highly non-uniform operational profiles. Highly predictive validity is needed early in the execution period. The rapidly changing slope of the failure intensity during early stages can be better fitted with the Logarithmic Poisson than the basic model .