1. A Data Model and Development Environment to Help End-User Programmers Validate and Reuse Data Christopher Scaffidi Thesis Proposal, May 8, 2007 Committee Mary Shaw (chair)Institute for Software Research, Carnegie Mellon University Sebastian ElbaumComputer Science & Engineering, University of Nebraska-Lincoln Jim HerbslebInstitute for Software Research, Carnegie Mellon University Brad MyersHuman-Computer Interaction Institute, Carnegie Mellon University

2. 2 Target audience In 2012, we project that there will be 90 million computer end users (“EUs”) in American workplaces. Of these, at least half will create spreadsheets, databases, and/or web applications. These are called end-user programmers (“EUPs”). [5] Both EUs and EUPs will benefit from the proposed research, though the proposed research is primarily aimed at EUPs (including EUs who become EUPs because of the research). introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

3. 3 Contextual inquiry: What are the problems of EUs and EUPs? Observed 3 administrative assistants, 4 managers, and 3 webmasters/graphic designers (1-3 hrs, each) introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

4. 4 How do you validate web forms if you do not know JavaScript? introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Is the input valid? “EDSH 225” Is the input nearly valid? “EDXH 225” Does it just need reformatting? “Smith 225” Or is it obviously badly invalid? “Robotics Institute”

5. 5 Other tasks, other data, other problems When building a staff roster by merging data sources into a single spreadsheet, one of the EUs: –Had to manually transform data to consistent format (e.g.: Put person names in Lastname, Firstname format) –Had to scrutinize data to identify questionable values that deserved double-checking (e.g.: A first name with 15 characters might be right) –Had to manually check for (near-) duplicates (e.g.: “Scaffidi, Christopher” and “Scaffidi, Chris”) We and research collaborators identified many additional data validation and data reuse tasks that were poorly supported by existing tools. [3][7][9] introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

6. 6 Underlying problem: abstraction mismatch Tools support strings, integers, floats, sometimes dates. Problem domain involves higher-level categories of data: –University names“ Carnegie Mellon”, “CMU” –Person names“ Scaffidi, Christopher”, “Chris Scaffidi” –CMU phone numbers“ 8-1234”, “x8-1234” –CMU room numbers“ WeH 4623”, “Wean 4623” These data categories are: –Human-readable –Short (~ 1 input field) –Multi-format –Sometimes ambiguous / fuzzy (non-binary scale of validity) –Often particular to certain groups of people introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

7. 7 A New Direction: Create a new abstraction for each category of data Like software “libraries,” implementations of these abstractions could be reused in many programs. Abstractions would need to include functionality for: –Recognizing instances of the category (for automating data validation) –Transforming instances among various formats (for automating data reformatting) –Testing instances for equality (for automating removal of duplicates) introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

8. 8 A New Direction: Other requirements for abstractions EUPs over a range of programming expertise must be able to create custom new abstractions. Flexibility: –Abstractions must capture fuzziness when recognizing instances of the category and when testing equivalence. –EUPs must have the option of configuring abstractions to learn exceptional cases. Sharability: –EUPs must still be able to share and find useful abstractions even as the number of abstractions grows. –Latency and throughput of operations must not become burdensome as EUPs share numerous abstractions. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

9. 9Thesis The proposed data model and development environment will enable end-user programmers to implement and share custom abstractions for flexibly recognizing, transforming and equivalence-testing values in categories of short, human-readable data. The model and environment will help end-user programmers to more quickly and correctly validate and reuse data than is possible through currently practiced methods. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

10. 10Topes Tope = an abstraction implementation for a data category –Greek word for “place,” because each corresponds to a data category with a natural place in the problem domain Topes in practice: 1.EUPs create new topes by using the basic tope editor (or by writing topes in another language, such as JavaScript) 2.EUPs publish topes on repositories. 3.Other EUs & EUPs download topes to their local cache. 4.Tool plug-ins let EUs & EUPs browse their local cache and associate topes with variables and input fields. 5.Plug-ins get topes from local cache and use them to recognize, transform, and equivalence-test data. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

11. 11Outline Introduction Related work Exploratory studies Prototype Proposed work Evaluation Summary and schedule introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Existing approaches lack an easy way for EUPs to create flexible, sharable abstractions for data categories

12. 12 Existing programming tools for EUPs ( eg: Excel, Visual Studio Express, Robofox ) Limited support for a closed set of data categories: –Spreadsheets (like Excel) allow EUs to associate certain formats with cells, but these do not actually validate data –Web application design tools (like Visual Studio) allow EUPs to apply certain limited constraints to validate input –Web macro tools (like Robofox) allow EUPs to store certain personal data (eg: phone #) and reuse it No straightforward mechanisms for EUPs to create new abstractions for unsupported categories of data introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

13. 13 User-definable data formats ( eg: SWYN, Grammex, Lapis, Data Detectors ) EUPs struggle to understand and create regexps/CFGs These formats are binary (non-fuzzy) recognizers Formats alone do not transform or equivalence-test data Only Apple Data Detectors offers sharing mechanisms introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Lapis example @DayOfMonth is Number equal to /[12][0-9]|3[01]|0?[1-9]/ ignoring nothing @ShortMonth is Number equal to /1[012]|0?[1-9]/ ignoring nothing @ShortYear is Number equal to /\d\d/ ignoring nothing Date is flatten @ShortMonth then @DayOfMonth then @ShortYear ignoring either Spaces or Punctuation

14. 14 Formal and OO types ( eg: ML, Java, C# ) Type systems are inflexible: –A value is or is not a valid instance of a type (non-fuzzy) –If a value is invalid at compile-time, it cannot become valid at runtime Typed languages are probably difficult for EUPs who are uncomfortable with untyped scripting languages. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

15. 15 Format-inference and constraint-enforcing ( eg: info. extraction, Lapis, Cues, Slate ) Various approaches: –Many algorithms infer an abstract model, CFG-like grammar, or other format with very low editability. –Other algorithms enforce constraints (either inferred or specified by EUPs) that cannot handle string-like data Formats, grammars, and constraints are not able to transform or equivalence-test data. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

16. 16Outline Introduction Related work Exploratory studies Prototype Proposed work Evaluation Summary and schedule introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Tasks commonly involve Recognizing Transforming Equivalence-testing values in categories of short, human-readable data.

17. 17 Survey of EUPs: Better data-manipulation features needed Asked 831 information workers about use of 23 features in 5 tools (eg: creating spreadsheet macros, database stored procedures, and web forms) [4][9] The most widely used features were related to manipulating linked structures of data (eg: database tables) rather than imperative or macro programming Yet respondents complained about these features: –“Not always easy to move sturctured [sic] data or text” –“Not always integrated a lot of data manipulation redundant” –“Information entered inconsistently into database fields by different people leaves a lot of database cleaning” introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

18. 18 Contextual inquiry of EUs and EUPs: Specific data-manipulation features needed Observed 3 administrative assistants, 4 managers, and 3 webmasters/graphic designers (1-3 hrs, each) [3][9] They needed better support for automatically: –Transforming data values among different formats within the same category of data (eg: ST to State ) –Identifying questionable data values that could be acceptable for a task but deserve double-checking –Identifying duplicate values, including values that were probably equivalent introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

19. 19 Interviews of web site creators: Confirmation of specific features needed Interviewed 6 people involved in creating “person locator” web sites after Hurricane Katrina [7][9] Many omitted data validation on web forms –Hard to detect that “12 Years old” is an invalid street address (what would the regexp look like?) “Aggregator” sites were built to scrape and consolidate data from numerous person locator sites. –Hard to transform data into a single consistent format –Hard to identify probable duplicates in the merged data set introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

20. 20Outline Introduction Related work Exploratory studies Prototype Proposed work Evaluation Summary and schedule introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary How could flexible formats be expressed?

21. 21 Prototype Task flow diagram introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Algorithm infers a format from cell values User reviews and customizes format User creates a format from scratch User loads an existing format from a file Plug-in flags cells that don’t match format User highlights spreadsheet cells [1][6] or

22. 22 Sample task: validating a spreadsheet with the prototype we have built The second column is “supposed” to contain first names, but some initials have snuck in. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

23. 23 Sample task: validating a spreadsheet Customizing an inferred format User can specify meaningful names for parts introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

24. 24 Sample task: validating a spreadsheet Customizing constraints in our prototype User can add/edit constraints introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

25. 25 Sample task: validating a spreadsheet Flagging potential errors A red flag (reviewer comment, actually) appears on cells that do not match the format; mouse over for message introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

26. 26 Sample task: web form validation The painful old way Drag widgets and validator onto page, select a regexp, customize if desired. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

27. 27 Sample task: web form validation Results of the painful old way Invalid inputs cause a hard-coded message to appear. Oops, forgot to enter a message at design-time. For valid inputs, no error message appears. Hm, didn’t realize the area code was optional. What if I want to allow campus phone numbers? introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

28. 28 Sample task: web form validation The wonderful new way Drag widgets and validator onto page, select a format, customize if desired. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

29. 29 Sample task: web form validation Creating this format took 55 seconds introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

30. 30 Sample task: web form validation Results of the new way Invalid inputs cause a targeted message to appear. Inputs that violate an always or never constraint cannot be submitted to the server. Inputs that violate an often constraint cause a warning, which the application user can override. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

31. 31 Prototype implementation System block diagram introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Spreadsheet Microsoft Excel Plug-in Microsoft Visual Studio.NET Plug-in Format editor Parser Web application Validator

32. 32 Benefits of the format editor Exotic regexp notation is replaced with sentence-like screen prompts. Soft constraints (“often”) are supported. Negation constraints (“never”) are supported. In terms of expressiveness, Augmented context-free grammars > context-free grammars > regexps But is the expressiveness adequate for common data? introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

33. 33 Expressiveness evaluation Four administrative assistants’ use of a web browser was logged for three weeks, resulting in nearly 6000 sample data values that they typed into web forms. Not logged verbatim: characters were generalized –Eg: Cscaffid0@gmail.com  Aa{7}0@a{5}.a{3} We manually grouped values into 19 semantic families (eg: email address) based on widget’s HTML name and words visually nearby to the widgets Created and tested formats for 14 families (4250 values) –Omitted: username/passwords and long blocks of “text” –Inference & testing features were not used during format creation introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

34. 34 Expressiveness evaluation results 9 families needed 1 format each; 5 needed 2 formats each Easy to quickly express a reasonably correct format? –11 families took < 1 minute each; others 3, 5, 7 minutes –No errors found in formats for 9 families; 5 had errors Most errors: forgetting to mark a part as optional Testing feature was added after this evaluation The only error attributable to editor expressiveness: –1 of the 4250 test values had a trailing period on a street type (in an address line) –This particular version of the editor had no way to say that a part could contain a period but only at the end introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary [6]

35. 35 Extension and further evaluation needed The editor evaluation again highlighted the need for supporting multiple formats within each data category. The proposed work will add this support. Then, usability of the editor as a whole will be evaluated. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

36. 36Outline Introduction Related work Exploratory studies Prototype Proposed work Evaluation Summary and schedule introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Generalizing the prototype: A lightweight data model + A development environment to help EUPs create, share and use topes

37. 37 Proposed data model 1 tope implementation contains executable functions: –1 isa:string  [0,1] function per format, for recognizing instances of the format –0 or 1 eqc:string x string  [0,1] function per format, for testing equivalence of two values in a format (default is a binary test for being exactly identical) –0 or more trf:string  string function linking formats, for transforming values form one format to another A lightweight data model… –Only contains 3 kinds of functions (isa/eqc/trf) –These correspond to the operations that people had to keep performing manually in our studies. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

38. 38 Example tope Notional representation An example tope for CMU room numbers –3 isa functions, up to 3 eqc functions, 4 trf functions –A tope’s eqc and trf functions can be omitted if desired introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Formal building name & room number Elliot Dunlap Smith Hall 225 Building abbreviation & room number EDSH 225 Colloquial building name & room number Smith 225

39. 39 Proposed development environment Functional decomposition diagram Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins Normalization introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary EUPs implement topes in basic topes editor (or JavaScript), then publish in repositories. Other EUs and EUPs search for topes, download them, then use them through plug-ins.

40. 40 Proposed development environment Enhanced basic topes editor Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins Normalization introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

41. 41 Proposed work Enhancing the basic topes editor Extend isa support –Improve error message generation Add trf support –EUPs will specify a series of steps: Select a part, select an operator Operators: permutation, lookup, arithmetic, capitalization –Add (regression) testing features to facilitate consistency Add eqc support –For each part, EUPs will specify a comparison operator, returning value in [0,1], and these will be multiplied. Operators: exactly identical, case-insensitive comparison, ~arithmetic distance, ~edit distance introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

42. 42 Proposed development environment Repository software Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins Normalization introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

43. 43 Proposed work Repository software Clients will have a list of “known” repository servers –Generally pre-configured to include a global server at CMU –Organizations will configure clients to include the organizational server –EUs and EUPs will be able to add new servers to their list To support publishing/searching, the repository will house meta-information about topes. (EUPs can also simply email topes to EUs and other EUPs, bypassing the repository system.) introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

44. 44 Proposed development environment Publishing tools Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins Normalization introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

45. 45 Proposed work Publishing topes Publishing a tope on a repository –Anonymously, or authenticated –EUPs can gather into groups, publish group-private topes –Each tope can have a non-unique name & description –Internally, each tope will have a globally unique id (guid) For published tope, guid = URL of the master copy (For emailed tope, guid based on sender’s email address) Tope aliases –EUPs can publish tope aliases –Alias has no implementation; just points to another tope –Alias can have its own name, description introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

46. 46 Proposed development environment Search tools Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins Normalization introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

47. 47 Proposed work Searching for relevant topes Search by keyword: –Search tope name and description –And match based on words that are visually near to topes Search by groups of people: –Within an organization, or by author’s email domain –Within spaces that are “group-private” Search by groups of topes: –“If you liked this tope, you may also like XYZ” –Similar to Amazon.com’s product recommendations Search by example: –“Find me a tope that recognizes 412-555-1212” –For efficiency, filter based on “signature” (\d{3}-\d{3}-\d{4}) introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

48. 48 Proposed work Searching for trustworthy topes introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Evidence [8] EUs and EUPs may trust topes:Search features Explicit formal rolesCreated by their organization’s system administrators. Search by tope author Prior performanceFrom people who have previously supplied good topes. Model of motivationFrom vendors that care about brand image. Group membershipFrom people who are known to have a similar background. ReputationThat earned anonymous votes of confidence. Search by tope ratings (either anonymous or not) ReferencesThat present a list of high-profile people who like the topes. CertificationThat are inspected and certified by a third party. Social contextThat are actively maintained—that is, for which improved versions are regularly available. That are implemented in a familiar language/platform. Search by tope publication date and execution platform

49. 49 Proposed development environment Enhanced plug-ins Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins Normalization introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

50. 50 Proposed work Enhancing plug-ins Microsoft Excel –Outlier finding infer format on selected cells, run isa –Assertions run isa on selected cells –Transformation run trf on selected cells –De-duplication run eqc on selected cells, cluster the cells Microsoft Visual Studio.NET –Input validation run isa on form widget, show error message –Input consistency run trf on value if in wrong format Robofox –Assertions run isa on selected variable –Transformation run trf on selected variable In each, support basic editor topes & JavaScript topes introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

51. 51 Proposed development environment Normalization (“the tope who cried wolf”) Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins Normalization introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

52. 52 Proposed work Normalization: Recognizing exceptions Tope creators might overlook values. From the standpoint of a tope format, these “normal” values are exceptional cases that need to be tolerated. Simple approach: Record a whitelist of exceptions More sophisticated: For each format, record exceptions, infer a format (new isa function), and average this function’s score with the raw function’s score Exceptional values can be incorporated into the tope in the local cache and/or, at EUP’s discretion, propagated to the repository of the tope’s master copy introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

53. 53Outline Introduction Related work Exploratory studies Prototype Proposed work Evaluation Summary and schedule introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Expressiveness: evaluation on examples Use by EUPs: evaluation in controlled experiments Flexibility: evaluation through analyses Sharability: field testing + analyses

54. 54Thesis The proposed data model and development environment will enable end-user programmers to implement and share custom abstractions for flexibly recognizing, transforming and equivalence-testing values in categories of short, human-readable data. The model and environment will help end-user programmers to more quickly and correctly validate and reuse data than is possible through currently practiced methods. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

55. 55 Expressiveness is needed Claim: End users’ tasks commonly involve categories of short, human-readable data that appear in multiple formats, and that users recognize and test for equivalence in a fuzzy manner. Using contextual inquiry and interview data, identify and characterize examples of these data categories. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

56. 56 Expressiveness is provided Claim: The operators and constructs supported by the basic editor are expressive enough for creating topes for data categories in common tasks. –We’ll create topes for data categories in four tasks similar to those that we saw in our prior studies: –1 “graduated response” validation task in web application –1 web macro task –1 outlier finding task in spreadsheet –1 data de-duplication task in spreadsheet introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

57. 57 EUPs can create topes Claim: Given a suitable development environment, EUPs can create custom software abstractions for recognizing, transforming and equivalence-testing values in commonly occurring data categories. Evaluate with controlled experiment (with CMU staff): –Create topes for data categories in sample tasks –Within-subjects, we may have subjects use a comparison method Eg: Lapis for isa, manual for trf, Excel formulas for eqc Measure time-on-task and error rates introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

58. 58 EUPs can benefit from using topes Claim: Extending existing programming tools with these abstractions enables EUs and EUPs to more quickly and correctly validate and reuse data than is possible through currently practiced methods. Evaluate with controlled experiment (with CMU staff): –Provide subjects with appropriate topes –Have them perform the sample tasks, using plug-ins –Within-subjects, we may have subjects use a comparison method Eg: JavaScript, manual performance, Lapis, Excel formulas Measure time-on-task and error rates introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

59. 59 Recognition, equivalence-testing, and exception-handling are flexible Claim: The abstractions created by EUPs flexibly capture the fuzziness of data recognition and equivalence- testing, and flexibly adapt at runtime when validating exceptional inputs. Evaluate with analyses: –Take topes created by EUPs in experiments –Run them on test data from EUSES spreadsheet corpus –Based on manual annotation of test data, score the topes –Evaluate the normalization algorithms: which works best? Measure topes’ precision/recall, compare to Lapis scores introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

60. 60 EUPs can share topes Claim: Given a suitable development environment operating on meta-information about these abstractions, EUPs can share abstractions with one another. Evaluate through field testing –Create an installer for plug-ins and basic topes editor –Recruit CMU grad students and staff to use it for 3 months –Log user actions (eg: published topes, queries, downloads) –Record (and answer) frequently asked questions –Periodic surveys Which features do EUPs consider helpful (or need work)? Which sources of “trust” evidence are actually helpful? introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

61. 61 Performance is scalable Claim: The latency and throughput of operations does not become burdensome as EUPs share numerous abstractions with one another. Evaluate with analyses: –Logs provide sample queries –Measure execution time of queries on sample tope sets –Perform algorithmic analysis of the search algorithms Combining execution time with algorithmic analysis yields a rough estimate of scalability introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

62. 62Outline Introduction Related work Exploratory studies Prototype Proposed work Evaluation Summary and schedule introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary 3 knowledge contributions 5 technical contributions 20 months

63. 63 Knowledge contributions Characterization of the fuzzy, multi-format categories of data commonly involved in end-user programming Lightweight data model (isa/trf/eqc) for representing these data categories A list of sources of evidence that help EUPs share abstractions introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

64. 64 Primary technical contributions Algorithms –For validating, transforming, and equivalence-testing data based on formats implemented by EUPs –For generating targeted error messages –For search-by-example –For collecting and searching on context words –For normalization and format inference introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

65. 65 Green = implementation Blue = evaluation Purple = dissertation Intended schedule: 20 months introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Editor and plug-in support for trf and eqc (3 mo) Evaluate with examples, experiments, analyses (3 mo) Addl. editor and plug-in enhancements (3 mo) Implement repository (5 mo) Evaluate sharability & scalability (3 mo) Dissertation (3 mo)

66. 66 Referenced papers Conference papers [1]C. Scaffidi. Unsupervised Inference of Data Formats in Human-Readable Notation. Proceedings of 9th International Conference on Enterprise Integration Systems (ICEIS'07), 2007, to appear. [2]C. Scaffidi, K. Bierhoff, E. Chang, M. Felker, H. Ng, C. Jin. Red Opal: Product-Feature Scoring from Reviews. Proceedings of 8th ACM Conference on Electronic Commerce (ACMEC'07), 2007, to appear [3]C. Scaffidi, A. Cypher, S. Elbaum, A. Koesnandar, and B. Myers. Scenario-Based Requirements for Web Macro Tools. Submitted for publication, 2007. [4]C. Scaffidi, A. Ko, B. Myers, M. Shaw. Dimensions Characterizing Programming Feature Usage by Information Workers. VL/HCC'06: Proceedings of the 2006 IEEE Symposium on Visual Languages and Human-Centric Computing, pp. 59-62, 2006. [5]C. Scaffidi, M. Shaw, and B. Myers. Estimating the Numbers of End Users and End User Programmers. VL/HCC'05: Proceedings of the 2005 IEEE Symposium on Visual Languages and Human-Centric Computing, pp. 207-214, 2005. Other papers [6]C. Scaffidi, B. Myers, M. Shaw. The Topes Format Editor and Parser, Technical Report CMU-ISRI-07-104, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, May 2007. [7]C. Scaffidi, B. Myers, and M. Shaw. Trial By Water: Creating Hurricane Katrina "Person Locator" Web Sites. In Leadership at a Distance: Research in Technologically-Supported Work (S. Weisband, ed), Lawrence Erlbaum, pp. 209-222, 2007. [8]C. Scaffidi, M. Shaw. Toward a Calculus of Confidence. First International Workshop on the Economics of Software and Computation, co-located with ICSE'07, 2007, to appear. [9]C. Scaffidi, M. Shaw, B. Myers. Games Programs Play: Obstacles to Data Reuse, 2nd Workshop on End User Software Engineering (WEUSE), 2006. introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

67. 67 Thank You… …to many people for helpful suggestions …to NSF and EUSES for funding (ITR-0325273 and CCF-0438929) …to my wife, and to the Lord, for emotional support introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary Marwan Abi-AntounMargaret BurnettMartin ErwigAndy KoMary Beth Rosson Robin AbrahamOwen ChengGeorge FairbanksThomas LaTozaMary Shaw Matt BassCiera ChristopherThomas GreenAlon LavieJeff Stylos Nels BeckmanMichael CoblenzJosh GrossHenry LiebermanDean Sutherland Kevin BierhoffAllen CypherGreg HartmanLarry MaccheroneSteve Tanimoto Alan BlackwellUri DekelJim HerbslebBrad MyersSusan Wiedenbeck Barry BoehmSebastian ElbaumJohn HoskingJohn Pane

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69. 69 Contextual inquiry: What are the problems of EUs and EUPs? Admin assistants and managers performed tasks in browsers and/or spreadsheets for the entire observation. Tasks required copying data among web forms and/or spreadsheets. –E.g.: using a government web site to look up an appropriate per diem rate based on a locality ( City, ST ) and a date ( MM/DD/YYYY ) in an expense report introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

70. 70 We considered helping them automate their tasks by creating web macro programs. But existing tools cannot perform needed data transformations introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary E.g.: Selecting the year based on the date ( MM/DD/YYYY ) and selecting the state based on the locality ( City, ST )

71. 71 Proposed work Searching for topes – by example Overview –Required meta-information: Published topes can include positive/negative examples (e.g.: “EDSH 225” matches this format) Tope users can also post examples, with ratings & comments –Generalize these examples to a format signature Required algorithm is similar to existing format inference but slightly more coarse (e.g.: “[a-z]{2-5} [0-9]{2-4}”) To search by example: 1.Specify some examples of the desired tope 2.Repository generalizes these examples to a signature 3.Repository returns topes with a similar signature introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

72. 72 Proposed work Searching for topes – in groups of topes Overview –People with one tope in common probably have other topes in common (eg: medical staff, CMU students, etc) –Approach: cluster topes based on who creates/uses them –Many algorithms exist for this kind of problem (eg: HAC) Searching by tope group: 1.The person searching has already used a few topes 2.Return topes that are in the same clusters introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary

73. 73 Proposed work Searching for topes – by keyword Keywords can occur in tope name or description Keywords can occur contextually: 1.EUP identifies the field where the tope will be used –Eg: a spreadsheet cell, or a web form widget 2.The programming tool plug-in looks for nearby words –Eg: top of spreadsheet column, left end of spreadsheet row, labels above form widget, or form widget’s HTML name 3.With user’s permission, these are sent to repository –As meta-information, when publishing –As a query, when searching Adapt algorithm for finding products based on features? [2] introduction ● related work ● studies ● prototype ● proposed work ● evaluation ● summary