1. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 1 Verification and Validation

2. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 2 Objectives l To introduce software verification and validation and to discuss the distinction between them l To describe the program inspection process and its role in V & V l To explain static analysis as a verification technique l To describe the Cleanroom software development process

3. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 3 Topics covered l Introduction to testing l Sources of errors l Why do we need testing ? l Verification and validation planning l Software inspections l Automated static analysis l Cleanroom software development

4. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 4 Nasty question l Suppose you are being asked to lead the team to test the software that controls a new X-ray machine. Would you take that job? l Would you take it if you could name your own price? l What if the contract says you’ll be charged with murder in case a patient dies because of a mal- functioning of the software?

5. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 5 A few spectacular software failures

6. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 6 Failures in Production Software l NASA’s Mars lander, September 1999, crashed due to a units integration fault—over $50 million US ! l Huge losses due to web application failures Financial services : $6.5 million per hour Credit card sales applications : $2.4 million per hour l In Dec 2006, amazon.com’s BOGO offer turned into a double discount l 2007 : Symantec says that most security vulnerabilities are due to faulty software l Stronger testing could solve most of these problems 6 World-wide monetary loss due to poor software is staggering Thanks to Dr. Sreedevi Sampath

7. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 7 Bypass Testing Results v — Vasileios Papadimitriou. Masters thesis, Automating Bypass Testing for Web Applications, GMU 2006

8. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 8 Why Does Testing Matter? 8 n NIST report, “The Economic Impacts of Inadequate Infrastructure for Software Testing” (2002) –Inadequate software testing costs the US alone between $22 and $59 billion annually –Better approaches could cut this amount in half n Major failures: Ariane 5 explosion, Mars Polar Lander, Intel’s Pentium FDIV bug n Insufficient testing of safety-critical software can cost lives: n THERAC-25 radiation machine: 3 dead n We want our programs to be reliable –Testing is how, in most cases, we find out if they are Mars Polar Lander crash site? THERAC-25 design Ariane 5: exception-handling bug : forced self destruct on maiden flight (64-bit to 16-bit conversion: about 370 million $ lost)

9. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 9 Software is a Skin that Surrounds Our Civilization

10. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 10 Airbus 319 Safety Critical Software Control Loss of autopilot Loss of both the commander’s and the co ‑ pilot’s primary flight and navigation displays Loss of most flight deck lighting and intercom

11. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 11 Northeast Blackout of 2003 Affected 10 million people in Ontario, Canada Affected 40 million people in 8 US states Financial losses of $6 Billion USD 508 generating units and 256 power plants shut down The alarm system in the energy management system failed due to a software error and operators were not informed of the power overload in the system

12. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 12 Software testing is getting more important

13. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 13 Testing in the 21st Century l We are going through a time of change l Software defines behavior network routers, finance, switching networks, other infrastructure l Today’s software market : is much bigger is more competitive has more users l Agile processes put increased pressure on testers l Embedded Control Applications airplanes, air traffic control spaceships watches ovens remote controllers – PDAs – memory seats – DVD players – garage door openers – cell phones Industry is going through a revolution in what testing means to the success of software products

14. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 14 Testing in the 21st Century l More safety critical, real-time software l Enterprise applications means bigger programs, more users l Embedded software is ubiquitous … check your pockets l Paradoxically, free software increases our expectations ! l Security is now all about software faults Secure software is reliable software l The web offers a new deployment platform Very competitive and very available to more users Web apps are distributed Web apps must be highly reliable Industry desperately needs our inventions !

15. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 15 Mismatch in Needs and Goals l Industry wants testing to be simple and easy Testers with no background in computing or math l Universities are graduating scientists Industry needs engineers l Testing needs to be done more rigorously l Agile processes put lots of demands on testing Programmers must unit test – with no training, education or tools ! Tests are key components of functional requirements – but who builds those tests ? Bottom line—lots of crappy software

16. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 16 Here! Test This! MicroSteff – big software system for the mac V.1.5.1 Jan/2007 Verdatim DataLife MF2-HD 1.44 MB Big software program Jan/2007 My first “professional” job A stack of computer printouts—and no documentation

17. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 17 17 Cost of Testing l In the real-world, testing is the principle post-design activity l Restricting early testing usually increases cost l Extensive hardware-software integration requires more testing You’re going to spend at least half of your development budget on testing, whether you want to or not

18. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 18 State-of-the-Art l 30-85 errors are made per 1000 lines of source code l extensively tested software contains 0.5-3 errors per 1000 lines of source code l testing is postponed, as a consequence: the later an error is discovered, the more it costs to fix it. l error distribution: 60% design, 40% implementation. 66% of the design errors are not discovered until the software has become operational.

19. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 19 Relative cost of error correction 1 2 5 10 20 50 100 REdesigncodetestoperation

20. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 20 Why Test? l Written test objectives and requirements are rare l What are your planned coverage levels? l How much testing is enough? l Common objective – spend the budget … If you don’t know why you’re conducting a test, it won’t be very helpful

21. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 21 Lessons l Many errors are made in the early phases l These errors are discovered late l Repairing those errors is costly l  It pays off to start testing real early

22. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 22 22 Why Test? l 1980: “The software shall be easily maintainable” l Threshold reliability requirements? l What fact is each test trying to verify? l Requirements definition teams should include testers! If you don’t start planning for each test when the functional requirements are formed, you’ll never know why you’re conducting the test

23. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 23 Cost of Not Testing l Not testing is even more expensive l Planning for testing after development is prohibitively expensive l A test station for circuit boards costs half a million dollars … l Software test tools cost less than $10,000 !!! Program Managers often say: “Testing is too expensive.”

24. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 24 How then to proceed? l Exhaustive testing most often is not feasible l Random statistical testing does not work either if you want to find errors l Therefore, we look for systematic ways to proceed during testing

25. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 25 Some preliminary questions l What exactly is an error? l How does the testing process look like? l When is test technique A superior to test technique B? l What do we want to achieve during testing? l When to stop testing?

26. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 26 Error, fault, failure l an error is a human activity resulting in software containing a fault l a fault is the manifestation of an error l a fault may result in a failure

27. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 27 When exactly is a failure a failure? l Failure is a relative notion: e.g. a failure w.r.t. the specification document l Verification: evaluate a product to see whether it satisfies the conditions specified at the start: Have we built the system right? l Validation: evaluate a product to see whether it does what we think it should do: Have we built the right system?

28. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 28 What is our goal during testing? l Objective 1: find as many faults as possible l Objective 2: make you feel confident that the software works OK

29. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 29 l Verification: "Are we building the product right”. l The software should conform to its specification. l Validation: "Are we building the right product”. l The software should do what the user really requires. Verification vs validation

30. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 30 l Is a whole life-cycle process - V & V must be applied at each stage in the software process. l Has two principal objectives The discovery of defects in a system; The assessment of whether or not the system is useful and useable in an operational situation. The V & V process

31. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 31 V& V goals l Verification and validation should establish confidence that the software is fit for purpose. l This does NOT mean completely free of defects. l Rather, it must be good enough for its intended use and the type of use will determine the degree of confidence that is needed.

32. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 32 V & V confidence l Depends on system’s purpose, user expectations and marketing environment Software function The level of confidence depends on how critical the software is to an organisation. User expectations Users may have low expectations of certain kinds of software. Marketing environment Getting a product to market early may be more important than finding defects in the program.

33. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 33 l Software inspections. Concerned with analysis of the static system representation to discover problems (static verification) May be supplement by tool-based document and code analysis l Software testing. Concerned with exercising and observing product behaviour (dynamic verification) The system is executed with test data and its operational behaviour is observed Static and dynamic verification

34. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 34 Static and dynamic V&V

35. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 35 l Can reveal the presence of errors NOT their absence. l The only validation technique for non- functional requirements as the software has to be executed to see how it behaves. l Should be used in conjunction with static verification to provide full V&V coverage. Program testing

36. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 36 l Defect testing Tests designed to discover system defects. A successful defect test is one which reveals the presence of defects in a system. Covered in Chapter 23 l Validation testing Intended to show that the software meets its requirements. A successful test is one that shows that a requirements has been properly implemented. Types of testing

37. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 37 l Defect testing and debugging are distinct processes. l Verification and validation is concerned with establishing the existence of defects in a program. l Debugging is concerned with locating and repairing these errors. l Debugging involves formulating a hypothesis about program behaviour then testing these hypotheses to find the system error. Testing and debugging

38. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 38 The debugging process

39. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 39 l Careful planning is required to get the most out of testing and inspection processes. l Planning should start early in the development process. l The plan should identify the balance between static verification and testing. l Test planning is about defining standards for the testing process rather than describing product tests. V & V planning

40. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 40 The V-model of development

41. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 41 The structure of a software test plan l The testing process. l Requirements traceability. l Tested items. l Testing schedule. l Test recording procedures. l Hardware and software requirements. l Constraints.

42. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 42 The software test plan

43. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 43 Software inspections l These involve people examining the source representation with the aim of discovering anomalies and defects. l Inspections not require execution of a system so may be used before implementation. l They may be applied to any representation of the system (requirements, design,configuration data, test data, etc.). l They have been shown to be an effective technique for discovering program errors.

44. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 44 Inspection success l Many different defects may be discovered in a single inspection. In testing, one defect,may mask another so several executions are required. l The reuse domain and programming knowledge so reviewers are likely to have seen the types of error that commonly arise.

45. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 45 Inspections and testing l Inspections and testing are complementary and not opposing verification techniques. l Both should be used during the V & V process. l Inspections can check conformance with a specification but not conformance with the customer’s real requirements. l Inspections cannot check non-functional characteristics such as performance, usability, etc.

46. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 46 Program inspections l Formalised approach to document reviews l Intended explicitly for defect detection (not correction). l Defects may be logical errors, anomalies in the code that might indicate an erroneous condition (e.g. an uninitialised variable) or non-compliance with standards.

47. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 47 Inspection pre-conditions l A precise specification must be available. l Team members must be familiar with the organisation standards. l Syntactically correct code or other system representations must be available. l An error checklist should be prepared. l Management must accept that inspection will increase costs early in the software process. l Management should not use inspections for staff appraisal ie finding out who makes mistakes.

48. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 48 The inspection process

49. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 49 Inspection procedure l System overview presented to inspection team. l Code and associated documents are distributed to inspection team in advance. l Inspection takes place and discovered errors are noted. l Modifications are made to repair discovered errors. l Re-inspection may or may not be required.

50. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 50 Inspection roles

51. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 51 Inspection checklists l Checklist of common errors should be used to drive the inspection. l Error checklists are programming language dependent and reflect the characteristic errors that are likely to arise in the language. l In general, the 'weaker' the type checking, the larger the checklist. l Examples: Initialisation, Constant naming, loop termination, array bounds, etc.

52. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 52 Inspection checks 1

53. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 53 Inspection checks 2

54. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 54 Inspection rate l 500 statements/hour during overview. l 125 source statement/hour during individual preparation. l 90-125 statements/hour can be inspected. l Inspection is therefore an expensive process. l Inspecting 500 lines costs about 40 man/hours effort - about £2800 at UK rates.

55. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 55 Automated static analysis l Static analysers are software tools for source text processing. l They parse the program text and try to discover potentially erroneous conditions and bring these to the attention of the V & V team. l They are very effective as an aid to inspections - they are a supplement to but not a replacement for inspections.

56. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 56 Static analysis checks

57. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 57 Stages of static analysis l Control flow analysis. Checks for loops with multiple exit or entry points, finds unreachable code, etc. l Data use analysis. Detects uninitialised variables, variables written twice without an intervening assignment, variables which are declared but never used, etc. l Interface analysis. Checks the consistency of routine and procedure declarations and their use

58. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 58 Stages of static analysis l Information flow analysis. Identifies the dependencies of output variables. Does not detect anomalies itself but highlights information for code inspection or review l Path analysis. Identifies paths through the program and sets out the statements executed in that path. Again, potentially useful in the review process l Both these stages generate vast amounts of information. They must be used with care.

59. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 59 LINT static analysis

60. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 60 Use of static analysis l Particularly valuable when a language such as C is used which has weak typing and hence many errors are undetected by the compiler, l Less cost-effective for languages like Java that have strong type checking and can therefore detect many errors during compilation.

61. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 61 Verification and formal methods l Formal methods can be used when a mathematical specification of the system is produced. l They are the ultimate static verification technique. l They involve detailed mathematical analysis of the specification and may develop formal arguments that a program conforms to its mathematical specification.

62. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 62 Arguments for formal methods l Producing a mathematical specification requires a detailed analysis of the requirements and this is likely to uncover errors. l They can detect implementation errors before testing when the program is analysed alongside the specification.

63. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 63 Arguments against formal methods l Require specialised notations that cannot be understood by domain experts. l Very expensive to develop a specification and even more expensive to show that a program meets that specification. l It may be possible to reach the same level of confidence in a program more cheaply using other V & V techniques.

64. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 64 l The name is derived from the 'Cleanroom' process in semiconductor fabrication. The philosophy is defect avoidance rather than defect removal. l This software development process is based on: Incremental development; Formal specification; Static verification using correctness arguments; Statistical testing to determine program reliability. Cleanroom software development

65. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 65 The Cleanroom process

66. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 66 Cleanroom process characteristics l Formal specification using a state transition model. l Incremental development where the customer prioritises increments. l Structured programming - limited control and abstraction constructs are used in the program. l Static verification using rigorous inspections. l Statistical testing of the system (covered in Ch. 24).

67. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 67 Formal specification and inspections l The state based model is a system specification and the inspection process checks the program against this mode.l l The programming approach is defined so that the correspondence between the model and the system is clear. l Mathematical arguments (not proofs) are used to increase confidence in the inspection process.

68. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 68 l Specification team. Responsible for developing and maintaining the system specification. l Development team. Responsible for developing and verifying the software. The software is NOT executed or even compiled during this process. l Certification team. Responsible for developing a set of statistical tests to exercise the software after development. Reliability growth models used to determine when reliability is acceptable. Cleanroom process teams

69. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 69 l The results of using the Cleanroom process have been very impressive with few discovered faults in delivered systems. l Independent assessment shows that the process is no more expensive than other approaches. l There were fewer errors than in a 'traditional' development process. l However, the process is not widely used. It is not clear how this approach can be transferred to an environment with less skilled or less motivated software engineers. Cleanroom process evaluation

70. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 70 Key points l Verification and validation are not the same thing. Verification shows conformance with specification; validation shows that the program meets the customer’s needs. l Test plans should be drawn up to guide the testing process. l Static verification techniques involve examination and analysis of the program for error detection.

71. Sommerville, Ammann-Offutt, Mejia, 2009 Software Engineering Slide 71 Key points l Program inspections are very effective in discovering errors. l Program code in inspections is systematically checked by a small team to locate software faults. l Static analysis tools can discover program anomalies which may be an indication of faults in the code. l The Cleanroom development process depends on incremental development, static verification and statistical testing.