1. Root Cause Analysis for HTML Presentation Failures using Search-Based Techniques Sonal Mahajan, Bailan Li, William G.J. Halfond Department of Computer Science University of Southern California

2. What is a presentation failure? Web page rendering ≠ expected appearance Expected appearance (oracle)Web page rendering

3. What is a presentation failure? Web page rendering ≠ expected appearance Difference 1: Alignment problem Expected appearance (oracle)Web page rendering

4. What is a presentation failure? Web page rendering ≠ expected appearance Difference 2: Color problem Expected appearance (oracle)Web page rendering

5. What is a presentation failure? Web page rendering ≠ expected appearance Difference 3: Style problem Expected appearance (oracle)Web page rendering

6. Presentation Failures Common in modern web applications – Highly complex – Dynamic nature of HTML, CSS, Javascript Difficult to diagnose and debug – Each page has hundreds of HTML elements – Each HTML element contains several styling properties

7. Why is handling presentation failures important? Presentation of a website – factors company branding – gives first impression about your business Presentation failures can – impact usability – negative perception about quality

8. When do presentation failures occur? 1.Front-end developer did not comply to pixel- perfect implementation [1] 2.Refactoring of UI 3.Web application was not tested sufficiently

9. Need to Debug Presentation Failures Throughout the development process 3 such scenarios - 1.Presentation Development Testing 2.Regression Debugging 3.Standard Debugging

10. 1. Presentation Development Testing Front-end developers – Expected to convert mockups to “pixel perfect” template pages

11. “Pixel-perfect” pages… Is it reasonable?

23. 1. Presentation Development Testing Front-end developers – Expected to convert mockups to “pixel perfect” template pages Back-end developers – Change templates by adding dynamic content Test to check if the implemented page is compliant with the given mockup Expected appearance (oracle) –> mockup

24. 2. Regression Debugging Changes to code after initial implementation – E.g.: Refactoring page from based layout to based layout Changes not intended to change appearance Change may have direct or indirect impact Test for presentation failures and debug to find responsible HTML elements Expected appearance (oracle) -> previous correct version of the page

25. 3. Standard Debugging Make corrective code changes based on bug reports – E.g.: Resolve user-reported failures Reproduce the failure and debug Expected appearance (oracle) -> marked screenshot with failure area

26. What is root cause of a presentation failure? Root cause Faulty HTML element Faulty visual property CSS property HTML attribute

27. Limitations of Related Approaches Manual interaction – Browser developer tools (e.g.: Firebug) – Labor-intensive and error-prone Selenium, Sikuli – Require to exhaustively specify correctness invariants Cross-browser testing – Cannot report exact root cause – faulty visual property Fighting layout bugs – Cannot report a root cause and application independent DOM differencing – Techniques such as XBT, GUI differencing, automated oracles – Assume “golden” version of the page – Cannot be used if no golden version or DOM has changed

28. Simple Approach Brute force exploration of possible root cause space 1.Substitute different values for each root cause 2.Compare web page and oracle 3.If same appearance, stop, else continue Limitation – Large universe of possible values E.g.: Margin property: [-∞, +∞] Color property: 16 million colors – Very expensive

29. New Idea Key Insights 1.Image processing defines successful search Compare web page and oracle Correct root cause identified 2.Image processing guides search Fitness functions (E.g. minimizing difference pixels) Use image processing to define root cause analysis as a search based technique

30. Mapping Root Cause Analysis to Search-based Problem Motivations – Large search space of root causes – Image processing to define search parameters – Availability of oracle image -> natural form of invariant specifications Use genetic algorithm

31. Genetic algorithm Population: Possible values for a visual property Initial population: Generated randomly Selection: Linear ranking Crossover: One point Mutation: Uniform mutation Fitness function: Minimize visual differences Stopping criteria: web page = oracle

32. Core Idea Try different values for a candidate root cause Fitness value = compare web page and oracle If max. fitness value (web page = oracle) – Stop Else – Continue search

33. Example Candidate root cause: Population: [-∞, +∞] Initial population: {20, 50, 100, …, 0, 5} OracleTest web page

34. Example

41. Match found!

42. Example Correct root cause found!

43. Basic Technique 1.Detect presentation failure Faulty HTML element 2.Find root cause Faulty visual property 

44. Prior work: WebSee [2] Goal: Detect and localize presentation failures Input: Test web page, oracle Output: Prioritized list of HTML elements Phases 1.Detection: Image processing techniques to find visual differences 2.Localization: Maps HTML elements to visual differences 3.Result set processing: Prioritizes HTML elements based on heuristics

45. Basic Technique 1.Detect presentation failure Faulty HTML element 2.Find root cause Faulty visual property 

46. Classification of Visual Properties Effective use of search-based techniques Define appropriate fitness function Based on the impact on rendering of HTML element 1.Size and Position 2.Color 3.Predefined values

47. Category 1: Size and Position E.g.: margin, padding, height, width Numeric values Population: [-∞, +∞] Fitness function – Minimize number of difference pixels – Property value Number of difference pixels

48. Example OracleTest web page

49. Example e = {... } Number of difference pixels = 300 Value = 50px -> No. of difference pixels = 2,100 Value = 2px -> No. of difference pixels = 175 Value = 5px -> No. of difference pixels = 0......

50. Category 2: Color E.g.: text color, background-color, border-color Color value – 140 color names – 16 million colors (#000000 to #FFFFFF) Population: [#000000, #FFFFFF] Fitness function – Minimize number of difference pixels -> not useful – Determine expected color from oracle -> complex – Use minimizing color distance

51. Category 2: Color analysis (… contd.) Color distance: Euclidean distance between RGB Oracle avg = Compute average color in oracle Test avg = Compute average color in test web page screenshot Color distance = dist (Oracle avg, Test avg ) Property value color distance Final check -> full image comparison

52. Example OracleTest web page

53. Example e = {... } Average oracle color = #FFA000 Average test screenshot color = #8E8E8E Color distance = 369 Value = #FFFFFF -> color distance = 394 Value = #FFF000 -> color distance = 32 Value = #FF0000 -> color distance = 0......

54. Category 3: Predefined values E.g.: font-style, display, font-family, border-style Set of discrete predefined values – font-style = {italic, oblique, normal} Exploration method – No notion “closeness” to guide search Genetic algorithm not used – Use exhaustive exploration – Not very expensive max. 21 elements, avg. 5 elements

55. Experiment Evaluate accuracy Compare results with random search Evaluated for Category 1 and 2 only Subject application: Gmail homepage Oracle: Gmail homepage screenshot Test cases: Seeded faults

56. Implementation steps Goal: Find root cause of presentation failure Input: 1.P: Test web page 2.O: oracle 3.E: set of potentially faulty HTML elements (provided by WebSee) Output: Root cause

57. Implementation steps (… contd.) 1.Find possible root cause space 2.Find pool of possibly correct values for each root cause 3.Use genetic algorithm to select candidate value 4.Substitute selected value in web page 5.Compare web page and oracle 6.If web page = oracle, then return root cause 7.Else, continue

58. Experimental Procedure Total 37 test cases Run both, our and random, approaches 5 times on each test case = 37 * 5 * 2 = 370 executions Limit search space for experiment to run within 24 hours = 24 hours / 370 ≈ 3.89 min Terminate random approach based on genetic algorithm

59. Experimental results CategoryRCARandom SearchTest # 1. Numeric100%59%30 2. Color100%37%7 Total100%55%37

60. Experimental results CategoryRCARandom SearchTest # 1. Numeric100%59%30 2. Color100%37%7 Total100%55%37 Conclusions – Validates feasibility of our search-based approach – Outperform random search Threats to validity – Restriction on the search space – Small sample of web applications CategoryRCARandom SearchTest # 1. Numeric100%59%30 2. Color100%37%7 Total100%55%37

61. Future Work Improve performance – Improve search space initialization E.g.: For category 1, use sub-image searching – Prioritize visual properties Create a comprehensive search framework Improve fitness functions Handle limitation of presence of faulty property Handle multiple failures Evaluate several real web applications

62. Summary 1.Technique for automatic root cause analysis 2.Root cause analysis mapped as a search problem 3.Helpful in debugging presentation failures 4.No HTML/CSS expertise required 5.High accuracy compared to random search

63. References 1.Front-end Developers Job Postings, URL: http://www-scf.usc.edu/ spmahaja/front- end-job-postings/, Apr 2014. 2.S. Mahajan and W. G. Halfond. Finding HTML Presentation Failures Using Image Comparison Techniques. In submission, 2014.