1. 1 An Exploration of Errors in Web- based Applications in the Context of Web-based Application Testing PhD Proposal Kinga Dobolyi May 2009

2. 2 The shopping cart

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5. 5 What is going on Problem: faults in web-based applications cause losses of revenue, and they are hard to test Approach: study errors in web-based applications in the context of web testing Solution: improve the state of the art in web testing techniques through guidelines targeted at high severity faults and automation and precision in regression testing

6. 6 Outline Introduction and motivation Thesis statement Background Goals and approaches Preliminary work Expected contributions

7. 7 Motivation Testing of web-based applications in particular deserves further examination due to economic considerations: –Monetary throughput: Backbone of e- commerce and communications businesses –Customers: low customer loyalty –Development: Companies are choosing not to test due to resource constraints

8. 8 Motivation: e-commerce Internet usage: 73% of people in the US in 2008 –Browsers are dominant application –$204 billion in Internet retail sales annually Global online B2B transactions total several $trillions annually One hour of downtime at Amazon.com cost $1.5 million dollars 70% of major online sites exhibit user-visible failures

9. 9 Motivation: customers Customer loyalty is notoriously low –Determined by the usability of the application [Offutt 2002] –Freedom and options

10. 10 Motivation: customers Lesson learned: web-based applications need to be well-designed and well tested Are they?

11. 11 Motivation: development Technology challenges: –Heterogeneous, opaque components –Dynamic page content generation –Persistent state operated upon by concurrent, global users around the clock

12. 12 Motivation: development Web-based applications are often not tested –Enormous pressure to change Short delivery times, high developer turnover rates, and quickly evolving user needs –No formal process model

13. 13 Motivation: summary Problem: faults in web-based applications cause losses of revenue, and they are hard to test Approach: study errors in web-based applications in the context of web application testing Solution: improve the state of the art in web testing techniques through guidelines targeted at high severity faults and automation and precision in regression testing

14. 14 Thesis statement Hypothesis: web-based applications have special properties that can be exploited to build tools and models that improve the current state of web application testing and development: –Tend to fail in predictable and similar ways –Human centric definition of acceptability

15. 15 Thesis statement Problem: faults in web-based applications cause losses of revenue, and they are hard to test Approach: study errors in web-based applications in the context of web testing Solution: improve the state of the art in web testing techniques through guidelines targeted at high severity faults and automation and precision in regression testing

16. 16 Background: testing techniques Non-functional (static) validation –Server load testing –Link testing –HTML/spelling validation Modeling approaches Capture-replay –User session-based testing

17. 17 Background: oracles Oracles (oracle-comparator) 1 The same table could be indented. 2 3 --- 4 > The same table could be indented. 5 > –False positives from diff-like tools –Want precise comparators

18. 18 Background: automation Automation –Test case generation: VeriWeb, PHP –Test case replay URL + post-data –Failure detection

19. 19 Background: metrics How do we measure success? –Code coverage –Fault seeding Human Automatic –Cost How do we know these are indicative of the real world?

20. 20 Background: fault definition Defining an error: –“the inability to obtain and deliver information, such as documents or computational results, requested by web users.” [Ma & Tian 2003] Fault taxonomies –Figure from [Marchetto et al 2007]

21. 21 Background: challenges Functional validation remains a challenge –Regression testing should be more precise and automatic We do not know if test suite efficacy metrics are indicative of the real world –We should examine the severity of uncovered faults

22. 22 Goals and approaches Problem: faults in web-based applications cause losses of revenue, and they are hard to test Approach: study errors in web-based applications in the context of web testing Solution: improve the state of the art in web testing techniques through guidelines targeted at high severity faults and automation and precision in regression testing

23. 23 Goals and approaches I propose to: –Model errors in web-based applications Identify them more accurately Automate the oracle-comparator process –Make web testing more cost-effective Devise a model of fault severity that will guide test case design, selection, and prioritization Validate or refute the current underlying assumption that all faults are equally severe in fault-based testing

24. 24 Goals and approaches: Goals Reduce the cost of regression testing web- based applications –Use special structure of web-based application output to precisely identify errors Automate web-based application regression testing –Unrelated web-based applications tend to fail in similar ways Understand customer-perceived severities of web application errors.

25. 25 Goals and approaches: Goals Formally ground the current state of industrial practice –Validate or refute fault injection as a standard for measuring web application test suite quality Understand how to avoid high-severity faults during web application design and development Reduce the cost of regression testing web applications by exposing high-severity faults –Test case design, selection, and prioritization (test suite reduction)

26. 26 Goals and approaches: Outline

27. 27 Goals and approaches: Step 1 – oracle-comparator Construct a precise oracle-comparator that uses the tree-structured nature of XML/HTML output and other features –Model errors on a per-project basis –Semantic distance metric to reduce false positives

28. 28 Goals and approaches: Step 2 – automated oracle-comparator Exploit the similar way in which web applications fail to avoid the need for human annotations in training a precise oracle-comparator –Train a precise oracle-comparator on data from other, unrelated web applications –Use fault injection to improve the results when necessary

29. 29 Goals and approaches: Step 3 – human study Conduct a human study of real-world fault severity to identify a model of fault severity –Severities different than self-reported in bug repositories –Screenshots of current-next idiom –Also survey developers

30. 30 Goals and approaches: Step 4 – fault seeding validation Compare the severities of real-world faults to seeded faults using human data (validate fault seeding) –The severities of seeded errors have uniform distributions? –The severity distribution of seeded errors matches the distribution of real-world errors, according to the results of the survey from Step 3?

31. 31 Goals and approaches: Step 5 – software engineering guidelines Identify underlying technologies and methodologies that correlate with high- severity faults –As an alternative to testing –Tie high severity errors to underlying code, components, programming languages, and software engineering practices

32. 32 Goals and approaches: Step 6 – testing techniques Identify testing techniques to maximize return on investment by targeting high- severity faults –Introduce a new metric for the (web application) test suite reduction research community

33. 33 Preliminary Work: Step 1 Step 1: Construct a precise oracle-comparator using tree structured XML/HTML output and other features –Multiple versions of 10 open-source benchmarks –7154 pairs of oracle- testcase output, 919 of which labeled as “possibly an error”

34. 34 Preliminary Work: Step 1 Evaluation: F-measure (precision and recall) using our model

35. 35 Preliminary Work: Step 1 Longitudinal study to measure effort saved –Calculate cost of looking : cost of missing –Low ratio means we are saving effort

36. 36 Preliminary Work: Step 2 Step 2: Exploit similarities in web application failures to avoid human annotations when training a precise oracle-comparator –Same setup as Step 1 –Use existing, annotated pairs of test-oracle output from unrelated applications to train a comparator for the application at test

37. 37 Preliminary Work: Step 2 Evaluation: measure precision and recall

38. 38 Preliminary Work: Step 2 Use fault seeding to introduce project- specific faults into the training data set

39. 39 Preliminary Work: Step 3 Step 3: Model real-world fault severity based on a human study. –Collect 400 real-world faults Evaluation: have subjects use the model to classify faults

40. 40 Preliminary Work: Step 4 Step 4: Compare the severities of real- world faults to seeded faults using human data. –Test same human subjects with 200 + 200 manually-injected and automatically-injected faults –Conduct a survey of developers for fault severity

41. 41 Preliminary Work: Step 5 Step 5: Identify technologies and methodologies that correlate with high-severity faults Do high severities correlate with: –Programming Language –Level in three-tier architecture –COTS component –User error (usability issue) –Type of error (business logic, resource allocation, syntax error, etc) –Fault taxonomies (existing) –Surface features of visible output: white screens, stack traces, misspellings, formatting errors Evaluation: developer survey (time permitting)

42. 42 Preliminary Work: Step 6 Step 6: Identify testing techniques to target high-severity faults –Targets testing –Assign a testcase a severity rating a priori Evaluation: compare the severity of faults exposed with our technique versus other test suite reduction approaches

43. 43 Expected Contributions Problem: faults in web-based applications cause losses of revenue, and they are hard to test Approach: study errors in web-based applications in the context of web testing Solution: improve the state of the art in web testing techniques through guidelines targeted at high severity faults and automation and precision in regression testing

44. 44 Expected Contributions Reduce the cost of regression testing by constructing a precise oracle-comparator Develop a model of customer-perceived severities of web application faults Validate or refute fault injection as a standard for measuring web application test suite quality Propose new software engineering guidelines for web application development and testing

45. 45 Questions?

46. 46 Original Contributions Fault Severity Model –Severity has not been studied in this domain, and customers are an integral part of these applications –Providing a new metric to the research community –Validate/refute fault seeding Precise-oracle comparators –First to use different versions of benchmarks –Can be completely automated –XML and HTML

47. 47 Expected Impact Fault Severity Model –Can be applied to testing techniques in this field to make them financially feasible for developers –Change the way in which test suite efficacy is measured –Potentially impact web site design as usability issues may become more evident Precise oracle-comparator –Automation makes it much more feasible for adoption than existing techniques –Potentially allow companies to conduct regression testing if they were not testing beforehand

48. 48 Timeline Steps 1 and 2: precise comparators –Completed, 2 papers under submission Steps 3 and 4: human study –Data collection completed, analysis under way for submission of 1 paper –Expected completion by September Step 5: software engineering guidelines –Expected completion by October –Expected 0.5 paper Step 6: testing according to fault model –Expected completion by February –Expected 1 paper