1. R Soup or Art: Making Software Development Decisions in an Imperfect World Shari Lawrence Pfleeger Senior Information Scientist RAND Pfleeger@rand.org

2. R Overview From the part to the whole: examining the body of evidence Ignorance, uncertainty and doubt Evidential force Multi-legged arguments Example: What to do about ephedra Moving forward

3. R From the Part to the Whole: Examining the Body of Evidence “Science is a particular way of knowing about the world. In science, explanations are limited to those based on observations and experiments that can be substantiated by other scientists. Explanations that cannot be based on empirical evidence are not a part of science.” Introduction to Science and Creationism: A View from the National Academy of Sciences, National Academies Press, 2000.

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5. R Soup or Art? “It appears to me that they who rely simply on the weight of authority to prove any assertion, without searching out the arguments to support it, act absurdly. I wish to question freely and to answer freely without any sort of adulation. That well becomes any who are sincere in the search for truth.” Vincenzo (father of Galileo) Galilei, 1574

6. R Terminology We make a case for something. The case has three parts: –One or more claims that properties are satisfied –A body of supporting evidence (from a variety of sources) –A set of arguments that link claims to evidence

7. R Two Key Uses of Evidence Hypothesis generation –Theories about the way processes, products and resources work alone and in concert Hypothesis testing –Is what we believe confirmed by the evidence?

8. R Key Questions for Empirical Software Engineering What do we mean when we say that a technology “works”? What kinds of evidence (and how much evidence) do we need to demonstrate that it works? Who provides the evidence, and who vets the evidence? (For instance, many of the claims about data mining are provided by the vendors.) If it works in one domain, does that tell us anything about other domains? How can evidence inform our thinking about the social, economic and political tradeoffs of using an imperfect technology?

9. R Ignorance, Uncertainty and Doubt “When a scientist doesn’t know the answer to a problem, he is ignorant. When he has a hunch as to what the result is, he is uncertain. And when he is pretty darn sure of what the result is going to be, he is in some doubt.” (Feynman 1999)

10. R More on Ignorance, Uncertainty and Doubt “We have found it of paramount importance that in order to progress we must recognize the ignorance and leave room for doubt. Scientific knowledge is a body of statements of varying degrees of certainty—some most unsure, some nearly sure, none absolutely certain.” (Feynman 1999)

11. R Types of Evidence (Schum 94) Tangible evidence –Can be examined to see what it reveals –Examples: objects, documents, images, measurements, charts Testimonial evidence: Unequivocal –Received from another person –Examples: Direct observation, hearsay, opinion Testimonial evidence: Equivocal –Examples: Complete equivocation, probabilistic Missing evidence (tangible or testimonial) Accepted facts (authoritative records)

12. R Evidential Credibility Depends on –Type of evidence Documented? Replicable? Well-designed? Measurable? –Creator –Conveyor Refereed publication? Trade journal? Self-published?

13. R Tests for Testimonial Credibility Sensitivity –Sensory defects? –Conditions of observation? –Quality/duration of observation? –Expertise/allocation of attention Objectivity –Expectations –Bias –Memory-related errors Veracity

14. R Putting It Together How to combine evidence when there are pieces Of dubious credibility? Missing? Ambiguous? Conflicting? Not replicable?

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24. R Examples Two conflicting studies of hormone replacement therapy (Kolata 2003) –Nurses’ health survey: Long-term study indicating that HRT helps protect against heart disease –Women’s Health Initiative: Recent study indicates that HRT increases risk of heart disease Curare study: confounding variable (natural vs. synthetic curare) discovered well after original study author embarrassed Conflicting studies of inspection teams –Some show team is useful, others don’t

25. R Evidential Force A body of evidence has evidential force, with each piece of evidence contributing to the whole. One piece of evidence can increase or diminish the evidential force.

26. R Assessing Evidential Force Jeremy Bentham (1839) proposed a numerical scale. Range from –10 to +10 Positive: gradations favoring H Negative: gradations against H Zero: no inferential force

27. R Bentham’s Four Questions to Determine Evidential Force How confident is the witness in the truth of the event asserted? How conformable to general experience (that is, how rare) is the event asserted? Are there grounds for suspicion of the untrustworthiness of the witness? Is the testimony supported or doubted by other evidence?

28. R Schum’s Approach Evidence marshalling Bayesian analysis Chains of reasoning Measures of likelihood: P(H|E)

29. R Multi-legged Arguments Work done by Bloomfield and Littlewood. General idea: Two heads are better than one. Example: Use a process-based argument (e.g. review of practices) and a product- based one (e.g. static code analysis). Another example: UK Def Std 00-55 –One leg is logical proof. –Another leg is probabilistic claim based on statistical analysis.

30. R More on Multi-legged Arguments Easier to analyze than one comprehensive argument. Handles different types of evidence. Legs need not be independent. More confidence than in one leg alone (but does extra confidence justify extra cost?)

31. R Criteria for Diversity Weaknesses in modeling assumptions –E.g. Is formal specification an accurate representation of higher-level requirements? Weaknesses in evidence –E.g. Is complete testing feasible?

32. R Relationship to Evidential Force Argument diversity increases confidence and thereby increases argument force. Example: “An argument that gives 99% confidence that the probability of failure on demand is smaller than 10 -3 is stronger than one that gives only 95% confidence in the same claim.”

33. R Dependence of Legs Not a bad thing: It can increase confidence in overall assertion. Assumption B Assumption A Evidence A Evidence B Assertion G

34. R Example: Safety Goal The formal specification correctly captures the informal requirements of the system. The formal specification correctly captures the informal requirements of the system. The statistical testing is representative of actual operational demands (which are statistically Independent). The statistical testing is representative of actual operational demands (which are statistically Independent). 4603 demands executed without failure 4603 demands executed without failure Successful mathematical verification that the program implements the specification Successful mathematical verification that the program implements the specification The PFD of the software is less than 10 -3. P(G A | E A, ass A ) > 1-  P(G B | E B, ass B ) = 1

35. R Things to Consider Extensiveness of evidence Assumption confidence Difficulty of assigning numerical values Need for simplifying assumptions Contribution of each piece of evidence to the whole

36. R What We Do at RAND “The RAND Corporation, America's original think tank, earns its money fishing truths out of murky political and social waters. The quantitative conscience of RAND [is] often the final arbiter of what constitutes the true story.” Bradley Efron,Stanford University

37. R Example: What to Do About Ephedra Ephedra is the herb (ma huang, as Chinese call it). Ephedrine is the drug.

38. R Claims About Ephedra Improves weight loss Enhances athletic performance Almost 18,000 reports of adverse effects (including death and illness)

39. R What About the Evidence? Dietary supplements not subject to same rigorous standards as drugs; therefore no need to show evidence of safety. Therefore limited evidence on ephedra. FDA seeks evidence of “significant or unreasonable risk of illness or injury.” Safety of ephedra cannot be demonstrated with scientific certainty.

40. R State of the Evidence 52 (published and unpublished) trials of ephedra or ephedrine for weight loss or athletic performance –Many had small numbers of people –Many had short periods of time –Other limitations, such as non-representative sample 1820 consumer complaints to FDA 71 reports in the medical literature 15,951 reports to Metabolife, a maker of ephedra-containing supplements

41. R Step 1: Set Criteria for Confidence in Evidence For weight loss studies: –Select studies that assess ephedra, ephedrine, or ephedrine plus other compounds used for weight loss. (Yield: 44 of 52 studies) –Select studies with periods of at least 8 weeks. (Yield: 26 of 44 studies) –Select studies with no serious other limitations. (Yield: 20 of 26 studies) For athletic performance studies: –Select studies that assess ephedra, ephedrine, or ephedrine plus other compounds used for athletic performance. (Yield: no studies of ephedra, only 8 studies of ephedrine, all but one of which included caffeine)

42. R Step 1: continued For safety studies: –Select studies with documentation of adverse event. –Select those confirming that ephedra or ephedrine had been consumed within 24 hours before adverse event OR with toxicological evidence of those substances in blood or urine. –Select those documenting exclusion of other possible causes. Yield: 284 possible events.

43. R Step 2: Categorize Treatments For weight loss studies: –Comparisons made in six categories Ephedrine vs. placebo (5 trials) Ephedrine and caffeine vs. placebo (12 trials) Ephedrine and caffeine vs. ephedrine alone (3 trials) Ephedrine and caffeine vs. another active pharmaceutical for weight loss (2 trials) Ephedra vs. placebo (1 trial) Ephedra with herbs containing caffeine vs. placebo (4 trials)

44. R Step 2: continued For athletic performance studies: –Each trial involved a different kind of exercise, so each trial was assessed separately.

45. R Step 3: Set Outcome Measures For weight loss studies: –Clear indicators of weight loss For athletic performance studies: –Measures of exercise performance, such as Oxygen consumption Time to exhaustion Carbon dioxide production Muscle endurance Reaction time Etc. For safety studies: –Group symptoms into clinically similar categories

46. R Taking Ephedrine or Ephedra Can Increase Weight Loss in the Short Term Ephedrine vs. placebo Ephedrine and caffeine vs. placebo Ephedrine and caffeine vs. ephedrine alone Ephedrine and caffeine vs. another active pharmaceutical for weight loss Ephedra vs. placebo Ephedra with herbs containing caffeine vs. placebo Additional pounds lost per month (95% confidence intervals) 0 0.5 1.5 2.5 3.5 1.0 2.0 3.0 1.3 2.2 2 trials: no significant difference 0.8 1.8 2.1 Source: Shekelle et al. March 2003

47. R Taking Ephedrine or Ephedra Increases the Odds of Suffering Adverse Events Psychiatric symptoms Autonomic hyperactivity PalpitationsHypertensionNausea, vomiting Headache Increased odds (95% confidence intervals) 0 2 6 10 14 4 8 12 3.6 3.4 2.32.2 1.6 2.2 Source: Shekelle et al. Spring 2003

48. R Software Engineering Example: Requirements Reviews Scenario-based reading –Defect-based reading (Porter et al. 1995) Each reviewer looks for a different kind of defect. –Perspective-based reading (Basili, Shull et al. 1997) Each reviewer takes the perspective of a user, a designer or a tester.

49. R Summary of Studies (Regnell 2000) StudyPurposeEnvironmentSubjectsSignificant? Maryland-95DBR/Ad hoc & Checklist Academic24+24Yes BariDBR/Ad hoc & Checklist Academic30No StrathclydeDBR/ChecklistAcademic50Inconclusive LinköpingDBR/ChecklistAcademic24No LucentDBR/Ad hoc & Checklist Industrial18Yes NASAPBR/Ad hocIndustrial12+13Yes KaiserslauternPBR/Ad hocAcademic25+26Yes TrondheimPBR/PBR2Academic44No Maryland-98PBR/Ad hocAcademic66Yes

50. R Summary of Studies—cont’d (Regnell 2000) StudyPurposeDocuments Maryland-95DBR/Ad hoc & ChecklistWater level monitoring system, cruise control BariDBR/Ad hoc & ChecklistWater level monitoring system, cruise control StrathclydeDBR/ChecklistWater level monitoring system, cruise control LinköpingDBR/ChecklistWater level monitoring system, cruise control LucentDBR/Ad hoc & ChecklistWater level monitor, cruise control NASAPBR/Ad hocAutomated teller, parking garage control, flight dynamics KaiserslauternPBR/Ad hocAutomated teller, parking garage control, flight dynamics TrondheimPBR/PBR2Automated teller, parking garage control Maryland-98PBR/Ad hocAutomated teller, parking garage control

51. R Lessons From Examples Different Goals Different Populations Different Study Designs Different Treatments Different Outcome Measures EphedraWeight loss, adverse health, athletic performance Athletes, non- athletes; different types of athletes Different exercises, different combinations of drugs Ephedra vs. ephedrine Weight loss vs. different exercises ReviewsEffectiveness vs. efficiency Students vs. practitioners; different years of experience Real teams vs. simulated teams DBR vs. PBR; comparison with ad hoc and checklist Time spent performing review, defects found, etc.

52. R Lessons to Learn There are techniques for combining data and results. Including uncertainty helps us evaluate risks. Empirical investigation is a process, not an end in itself. We must vet the evidence and consider multi- legged arguments instead of relying on replication. We can become more sophisticated—and knowledgeable—in software engineering by using these approaches.

53. R Steps Toward Improvement in Software Engineering Design families of studies Plan for imperfection Deal appropriately with uncertainty Survey existing evidence Hypothesis generation or testing? Confidence in evidence? For each study Set criteria for confidence in evidence Categorize treatments Set outcome measures Other examples: Agile methods? Cost models?

54. R Moving Forward: The Obstacles We tend to focus on individual studies or small aspects of technology. We tend to look to the “hard” sciences, to statistics, and to experimental design for our models. We hope that evidence won’t be conflicting, rather than plan for when it is.

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56. R Moving Forward: The Rewards Results and methods used in other disciplines can help us transform the field of empirical software engineering from a disparate collection of interesting results to a discipline rich with theory and theory-testing. We see software engineering in a larger context.

57. R Seeing the Big Picture

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66. R References Jeremy Bentham, The Rationale of Judicial Evidence, Bowring ed., William Tate, Edinburgh, 1839. Robin Bloomfield and Bev Littlewood, “Multi-legged Arguments: The Impact of Diversity Upon Confidence in Dependability Arguments,” Proceedings of DSN03, San Francisco, California, IEEE, 2003. Richard P. Feynman, The Pleasure of Finding Things Out: The Best Short Works of Richard P. Feynman, Helix Books/Perseus Books,1999. David A. Schum, Evidential Foundations of Probabilistic Reasoning, Wiley Series in Systems Engineering, New York, 1994. Gina Kolata, “Hormone Studies: What Went Wrong?”, New York Times, April 22, 2003, http://www.nytimes.com/2003/04/22/health/womenshealth/22HORM.htmlhttp://www.nytimes.com/2003/04/22/health/womenshealth/22HORM.html Bjőrn Regnell, Per Runeson and Thomas Thelin, “Are the Perspectives Really Different? Further Experimentation on Scenario-Based Reading of Requirements,” Empirical Software Engineering, 5, pp. 331-356, 2000. Paul G. Shekelle, Mary L. Hardy, Sally C. Morton, Margaret Maglione, Walter A. Mojica, Marika J. Suttorp, Shannon L. Rhodes, Lara Jungvig and James Gagné, “Efficacy and Safety of Ephedra and Ephedrine for Weight Loss and Athletic Performance: A Meta-Analysis,” Journal of the American Medical Association, 289(12), March 26, 2003, pp. 1537-1545. Paul G. Shekelle, Margaret Maglione and Sally C. Morton, “Preponderance of Evidence: Judging What to Do About Ephedra,” RAND Review, Spring 2003, pp. 16-21.