1. Software Reliability [Kehandalan Perangkat Lunak] Catur Iswahyudi

2. Agenda What is Software Reliability? Software Failure Mechanisms –Hardware vs Software Measuring Software Reliability Software Reliability Models Statistical Testing Conclusion

3. Functional and Non- functional Requirements System functional requirements may specify error checking, recovery features, and system failure protection System reliability and availability are specified as part of the non-functional requirements for the system.

4. System Reliability Specification Hardware reliability –probability a hardware component fails Software reliability –probability a software component will produce an incorrect output –software does not wear out –software can continue to operate after a bad result Operator reliability –probability system user makes an error

5. Failure Probabilities If there are two independent components in a system and the operation of the system depends on them both then P(S) = P(A) + P(B) If the components are replicated then the probability of failure is P(S) = P(A) n meaning that all components fail at once

6. Functional Reliability Requirements The system will check the all operator inputs to see that they fall within their required ranges. The system will check all disks for bad blocks each time it is booted. The system must be implemented in using a standard implementation of Ada.

7. Non-functional Reliability Specification The required level of reliability must be expressed quantitatively. Reliability is a dynamic system attribute. Source code reliability specifications are meaningless (e.g. N faults/1000 LOC) An appropriate metric should be chosen to specify the overall system reliability.

8. Hardware Reliability Metrics Hardware metrics are not suitable for software since its metrics are based on notion of component failure Software failures are often design failures Often the system is available after the failure has occurred Hardware components can wear out

9. Software Reliability Metrics Reliability metrics are units of measure for system reliability System reliability is measured by counting the number of operational failures and relating these to demands made on the system at the time of failure A long-term measurement program is required to assess the reliability of critical systems

10. Reliability Metrics - part 1 Probability of Failure on Demand (POFOD) –POFOD = 0.001 –For one in every 1000 requests the service fails per time unit Rate of Fault Occurrence (ROCOF) –ROCOF = 0.02 –Two failures for each 100 operational time units of operation

11. Reliability Metrics - part 2 Mean Time to Failure (MTTF) –average time between observed failures (aka MTBF) Availability = MTBF / (MTBF+MTTR) –MTBF = Mean Time Between Failure –MTTR = Mean Time to Repair Reliability = MTBF / (1+MTBF)

12. 12 What is Reliability? Reliability is a broad concept. –It is applied whenever we expect something to behave in a certain way. Reliability is one of the metrics that are used to measure quality. It is a user-oriented quality factor relating to system operation. –Intuitively, if the users of a system rarely experience failure, the system is considered to be more reliable than one that fails more often. A system without faults is considered to be highly reliable. –Constructing a correct system is a difficult task. –Even an incorrect system may be considered to be reliable if the frequency of failure is “acceptable.” Key concepts in discussing reliability: –Fault –Failure –Time –Three kinds of time intervals: MTTR, MTTF, MTBF

13. 13 What is Reliability? Failure –A failure is said to occur if the observable outcome of a program execution is different from the expected outcome. Fault –The adjudged cause of failure is called a fault. –Example: A failure may be cause by a defective block of code. Time –Time is a key concept in the formulation of reliability. If the time gap between two successive failures is short, we say that the system is less reliable. –Two forms of time are considered. Execution time (  ) Calendar time (t)

14. 14 What is Reliability? MTTF: Mean Time To Failure MTTR: Mean Time To Repair MTBF: Mean Time Between Failures (= MTTF + MTTR)

15. Relationship between MTTR, MTTF, and MTBF.

16. What is Software Reliability the probability of failure-free software operation for a specified period of time in a specified environment Probability of the product working “correctly” over a given period of time. Informally denotes a product’s trustworthiness or dependability.

17. What is Software Reliability Software Reliability is an important attribute of software quality, together with –functionality, –usability, –performance, –serviceability, –capability, –installability, –maintainability, –documentation.

18. What is Software Reliability Software Reliability Modeling Prediction Analysis Reliability Measurement Defect Classification Trend Analysis Field Data Analysis Software Metrics Software Testing and Reliability Fault-Tolerance Fault Trees Software Reliability Simulation Software Reliability Tools From Handbook of Software Reliability Engineering, Edited by Michael R. Lyu, 1996

19. What is Software Reliability Software Reliability is hard to achieve, because the complexity of software tends to be high. While the complexity of software is inversely related to software reliability, it is directly related to other important factors in software quality, especially functionality, capability.

20. 20 What is Reliability? Two ways to measure reliability –Counting failures in periodic intervals Observer the trend of cumulative failure count - µ(  ). –Failure intensity Observe the trend of number of failures per unit time – λ(  ). µ(  ) –This denotes the total number of failures observed until execution time  from the beginning of system execution. λ(  ) –This denotes the number of failures observed per unit time after  time units of executing the system from the beginning. This is also called the failure intensity at time . Relationship between λ(  ) and µ(  ) –λ(  ) = dµ(  )/d 

21. 21 Definitions of Software Reliability First definition –Software reliability is defined as the probability of failure-free operation of a software system for a specified time in a specified environment. Key elements of the above definition –Probability of failure-free operation –Length of time of failure-free operation –A given execution environment Example –The probability that a PC in a store is up and running for eight hours without crash is 0.99. Second definition –Failure intensity is a measure of the reliability of a software system operating in a given environment. Example: An air traffic control system fails once in two years. Comparing the two –The first puts emphasis on MTTF, whereas the second on count.

22. 22 Factors Influencing Software Reliability A user’s perception of the reliability of a software depends upon two categories of information. –The number of faults present in the software. –The ways users operate the system. This is known as the operational profile. The fault count in a system is influenced by the following. –Size and complexity of code –Characteristics of the development process used –Education, experience, and training of development personnel –Operational environment

23. 23 Applications of Software Reliability Comparison of software engineering technologies –What is the cost of adopting a technology? –What is the return from the technology -- in terms of cost and quality? Measuring the progress of system testing –Key question: How of testing has been done? –The failure intensity measure tells us about the present quality of the system: high intensity means more tests are to be performed. Controlling the system in operation –The amount of change to a software for maintenance affects its reliability. Thus the amount of change to be effected in one go is determined by how much reliability we are ready to potentially lose. Better insight into software development processes –Quantification of quality gives us a better insight into the development processes.

24. Software Failure Mechanisms Failure cause: Software defects are mainly design defects. Wear-out: Software does not have energy related wear- out phase. Errors can occur without warning. Repairable system concept: Periodic restarts can help fix software problems. Time dependency and life cycle: Software reliability is not a function of operational time. Environmental factors: Do not affect Software reliability, except it might affect program inputs. Reliability prediction: Software reliability can not be predicted from any physical basis, since it depends completely on human factors in design.

25. Software Failure Mechanisms Redundancy: Can not improve Software reliability if identical software components are used. Interfaces: Software interfaces are purely conceptual other than visual. Failure rate motivators: Usually not predictable from analyses of separate statements. Built with standard components: Well-understood and extensively-tested standard parts will help improve maintainability and reliability. But in software industry, we have not observed this trend. Code reuse has been around for some time, but to a very limited extent. Strictly speaking there are no standard parts for software, except some standardized logic structures.

26. Software Failure Mechanisms

28. Measuring Software Reliability Don’t define what you won’t collect.. Don’t collect what you won’t analyse.. Don’t analyse what you won’t use..

29. Measuring Software Reliability Measuring software reliability remains a difficult problem because we don't have a good understanding of the nature of software Even the most obvious product metrics such as software size have not uniform definition.

30. Measuring Software Reliability Current practices of software reliability measurement can be divided into four categories: –Product metrics –Project management metrics –Process metrics –Fault and failure metrics

31. Measuring Software Reliability Different categories of software products have different reliability requirements: –level of reliability required for a software product should be specified in the SRS document. A good reliability measure should be observer independent, –so that different people can agree on the reliability.

32. Measuring Software Reliability LOC, KLOC, SLOC, KSLOC McCabe's Complexity Metric Test coverage metrics ISO-9000 Quality Management Standards MTBF –Once a failure occurs, the next failure is expected after 100 hours of clock time (not running time).

33. Measuring Software Reliability Failure Classes –Transient: Transient failures occur only for certain inputs. –Permanent: Permanent failures occur for all input values. –Recoverable: When recoverable failures occur the system recovers with or without operator intervention. –Unrecoverable: the system may have to be restarted. –Cosmetic: May cause minor irritations. Do not lead to incorrect results. –Eg. mouse button has to be clicked twice instead of once to invoke a GUI function.

34. Measuring Software Reliability Errors do not cause failures at the same frequency and severity. –measuring latent errors alone not enough The failure rate is observer-dependent No simple relationship observed between system reliability and the number of latent software defects. Removing errors from parts of software which are rarely used makes little difference to the perceived reliability. removing 60% defects from least used parts would lead to only about 3% improvement to product reliability. Reliability improvements from correction of a single error depends on whether the error belongs to the core or the non-core part of the program. The perceived reliability depends to a large extent upon how the product is used. In technical terms on its operation profile.

35. Software Reliability Models Software reliability models have emerged as people try to understand the characteristics of how and why software fails, and try to quantify software reliability Over 200 models have been developed since the early 1970s, but how to quantify software reliability still remains largely unsolved There is no single model that can be used in all situations. No model is complete or even representative.

36. Software Reliability Models Most software models contain the following parts: –assumptions, –factors, –a mathematical function relates the reliability with the factors. is usually higher order exponential or logarithmic.

37. Software Reliability Models Software modeling techniques can be divided into two subcategories: –prediction modeling –estimation modeling. Both kinds of modeling techniques are based on observing and accumulating failure data and analyzing with statistical inference.

38. Boğaziçi University Software Reliability Modelling Computer Engineering Software Reliability Models ISSUESPREDICTION MODELSESTIMATION MODELS DATA REFERENCEUses historical dataUses data from the current software development effort WHEN USED IN DEVELOPMENT CYCLE Usually made prior to development or test phases; can be used as early as concept phase Usually made later in life cycle(after some data have been collected); not typically used in concept or development phases TIME FRAMEPredict reliability at some future time Estimate reliability at either present or some future time

39. Software Reliability Models There are two main types of uncertainty which render any reliability measurement inaccurate: Type 1 uncertainty: –our lack of knowledge about how the system will be used, i.e. its operational profile Type 2 uncertainty: –reflects our lack of knowledge about the effect of fault removal. When we fix a fault we are not sure if the corrections are complete and successful and no other faults are introduced Even if the faults are fixed properly we do not know how much will be the improvement to interfailure time.

40. Software Reliability Models Step Function Model –The simplest reliability growth model: a step function model –The basic assumption: reliability increases by a constant amount each time an error is detected and repaired. –Assumes: all errors contribute equally to reliability growth highly unrealistic: –we already know that different errors contribute differently to reliability growth.

41. Software Reliability Models Jelinski and Moranda Model –Realizes each time an error is repaired reliability does not increase by a constant amount. –Reliability improvement due to fixing of an error is assumed to be proportional to the number of errors present in the system at that time.

42. Software Reliability Models Littlewood and Verall’s Model –Assumes different fault have different sizes, thereby contributing unequally to failures. –Allows for negative reliability growth –Large sized faults tends to be detected and fixed earlier –As number of errors is driven down with the progress in test, so is the average error size, causing a law of diminishing return in debugging

43. Software Reliability Models Variations exists –LNHPP (Littlewood non homogeneous Poisson process) model Goel – Okumoto (G-O) Imperfect debugging model –GONHPP Musa – Okumoto (M-O) Logarithmic Poisson Execution Time model

44. Software Reliability Models Applicability of models: –There is no universally applicable reliability growth model. –Reliability growth is not independent of application. –Fit observed data to several growth models. Take the one that best fits the data.

45. Software Reliability Models

48. Observed failure intensity can be computed in a straightforward manner from the tables of failure time or grouped data (e.g. Musa et al. 1987).

49. Software Reliability Models Example: (136 failures total): Failure Times (CPU seconds): 3, 33, 146, 227, 342, 351, 353,444, 556, 571, 709, 759, 836..., 88682. Data are grouped into sets of 5 and the observed intensity, cumulative failure distribution and mean failure times are computed, tabulated and plotted.

50. Software Reliability Models

51. Two common models are the "basic execution time model“ and the "logarithmic Poisson execution time model" (e.g. Musa et al. 1987).

52. Software Reliability Models Basic Execution Time Model –Failure intensity λ(τ) with debugging time τ: –where λ0 is the initial intensity and ν0 is the total expected number of failures (faults).

53. Software Reliability Models where μ(τ) is the mean number of failures experienced by time τ.

54. Software Reliability Models In this case the Logarithmic- Poisson Model fits somewhat better than the Basic Execution Time Model. In some other projects BE model fits better than LP model.

55. Software Reliability Models Additional expected number of failures, Δμ, that must be experienced to reach a failure intensity objective where λP is the present failure intensity, and λF is the failure intensity objective. The additional execution time, Δτ, required to reach the failure intensity objective is

56. Software Reliability Models After fitting a model describing the failure process we can estimate its parameters, and the quantities such as the total number of faults in the code, future failure intensity and additional time required to achieve a failure intensity objective.

57. Statistical Testing The objective is to determine reliability rather than discover errors. Uses data different from defect testing.

58. Statistical Testing Different users have different operational profile: –i.e. they use the system in different ways –formally, operational profile: probability distribution of input Divide the input data into a number of input classes: –e.g. create, edit, print, file operations, etc. Assign a probability value to each input class: –a probability for an input value from that class to be selected.

59. Statistical Testing Determine the operational profile of the software: –This can be determined by analyzing the usage pattern. Manually select or automatically generate a set of test data: –corresponding to the operational profile. Apply test cases to the program: –record execution time between each failure –it may not be appropriate to use raw execution time After a statistically significant number of failures have been observed: –reliability can be computed.

60. Statistical Testing Relies on using large test data set. Assumes that only a small percentage of test inputs: –likely to cause system failure. It is straight forward to generate tests corresponding to the most common inputs: –but a statistically significant percentage of unlikely inputs should also be included. Creating these may be difficult: –especially if test generators are used.

61. Conclusions Software reliability is a key part in software quality Software reliability improvement is hard There are no generic models. Measurement is very important for finding the correct model. Statistical testing should be used but it is not easy again… Software Reliability Modelling is not as simple as described here.

62. Thank you. Q&A