A Data Model to Support End-User Software Engineering Christopher Scaffidi Carnegie Mellon University

2 Questions for the panel Some areas where I would appreciate suggestions: What aspects of this work would be of most interest to the ICSE community (in future research papers)? For any potential problems that you see in the work, what solutions can you suggest?

3 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 ● prototype ● proposed work ● evaluation

4 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) [3][9] introduction ● prototype ● proposed work ● evaluation

5 How can EUPs validate web forms if they do not know JavaScript? introduction ● prototype ● proposed work ● evaluation 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”

6 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 ● prototype ● proposed work ● evaluation

7 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“ ”, “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 ● prototype ● proposed work ● evaluation

8 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 ● prototype ● proposed work ● evaluation

9 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. introduction ● prototype ● proposed work ● evaluation

10Thesis 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 ● prototype ● proposed work ● evaluation

11Topes 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 ● prototype ● proposed work ● evaluation

12 Related Work: Existing approaches do not meet the requirements. Regexps / grammars / data detectors recognize data but do not specify how to transform data Types: –A value is or is not a valid instance of a type (non-fuzzy) –If invalid at compilation, values cannot become valid at runtime –Typed languages are probably difficult for EUPs who are uncomfortable with untyped scripting languages. Research on units (e.g.: Slate) and constraint systems (e.g.: Cues) typically only apply to numeric data in certain applications (e.g.: spreadsheets). And none of these has built-in support for helping users decide which abstractions to trust, so sharing is impeded. introduction ● prototype ● proposed work ● evaluation

13Outline Introduction Related work Prototype Proposed work Evaluation introduction ● prototype ● proposed work ● evaluation How could flexible formats be expressed?

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

15 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 ● prototype ● proposed work ● evaluation

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

17 Sample task: web form validation Creating this format took 55 seconds introduction ● prototype ● proposed work ● evaluation

18 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 ● prototype ● proposed work ● evaluation

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

20 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:  We manually grouped values into 19 semantic families (eg: 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 ● prototype ● proposed work ● evaluation

21 Expressiveness evaluation results 9 families needed 1 format each; 5 needed 2 formats each 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 After support for multiple formats is added, then the editor as a whole will be evaluated for usability. introduction ● prototype ● proposed work ● evaluation [6]

22Outline Introduction Related work Prototype Proposed work Evaluation introduction ● prototype ● proposed work ● evaluation Generalizing the prototype: A lightweight data model + A development environment to help EUPs create, share and use topes

23 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 ● prototype ● proposed work ● evaluation

24 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 ● prototype ● proposed work ● evaluation Formal building name & room number Elliot Dunlap Smith Hall 225 Building abbreviation & room number EDSH 225 Colloquial building name & room number Smith 225

25 Proposed development environment Functional decomposition diagram Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins introduction ● prototype ● proposed work ● evaluation 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.

26 Proposed development environment Enhanced basic topes editor Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins introduction ● prototype ● proposed work ● evaluation

27 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 ● prototype ● proposed work ● evaluation

28 Proposed development environment Publishing tools Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins introduction ● prototype ● proposed work ● evaluation

29 Proposed Work Publishing topes in repositories 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, including… –a human-visible non-unique name & description –an internally-used globally unique id (guid) based on the tope’s URL in the repository introduction ● prototype ● proposed work ● evaluation

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

31 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 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 ” –For efficiency, filter based on “signature” (\d{3}-\d{3}-\d{4}) introduction ● prototype ● proposed work ● evaluation

32 Proposed work Searching for trustworthy topes introduction ● prototype ● proposed work ● evaluation 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

33 Proposed development environment Enhanced plug-ins Basic Topes Editor Repository Software Publishing ToolsSearch Tools Development Environment Plug-Ins introduction ● prototype ● proposed work ● evaluation

34 Proposed work Enhancing plug-ins Target tools –Microsoft Excel –Microsoft Visual Studio.NET –Robofox Operations supported –Assertions run isa on selected cells –Transformation run trf on selected cells –De-duplication run eqc on selected cells, cluster the cells Each will support basic editor topes & JavaScript topes introduction ● prototype ● proposed work ● evaluation

35 Proposed work Recognizing exceptions in plug-ins 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 ● prototype ● proposed work ● evaluation

36Outline Introduction Related work Prototype Proposed work Evaluation introduction ● prototype ● proposed work ● evaluation Examples Experiments Field testing

37Evaluation Expressiveness – Identify test tasks based on previous studies; create topes for data involved in those tasks Creation of topes by EUPs – Controlled experiment in which students & staff create topes Usefulness for tasks – Controlled experiment in which students & staff use topes to perform the test tasks Flexibility of topes – Test the topes created by participants on test data drawn from EUSES spreadsheet corpus Sharability of topes – Field testing in which several dozen students & staff will install and use the environment introduction ● prototype ● proposed work ● evaluation

38 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, [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 , [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 , Other papers [6]C. Scaffidi, B. Myers, M. Shaw. The Topes Format Editor and Parser, Technical Report CMU-ISRI , School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, May [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 , [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), introduction ● prototype ● proposed work ● evaluation

39 Thank You… …to the symposium committee/panel for the opportunity to present …to many people for helpful suggestions …to NSF and EUSES for funding (ITR and CCF ) introduction ● prototype ● proposed work ● evaluation 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

40 Questions for the panel Some areas where I would appreciate suggestions: What aspects of this work would be of most interest to the ICSE community (in future research papers)? For any potential problems that you see in the work, what solutions can you suggest? introduction ● prototype ● proposed work ● evaluation

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42 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 ● prototype ● proposed work ● evaluation

43 Interviews of web site creators: Confirmation of specific problems 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 ● prototype ● proposed work ● evaluation

44 Sample task: validating person names Customizing constraints in our prototype User can add/edit constraints introduction ● prototype ● proposed work ● evaluation

45 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 ● prototype ● proposed work ● evaluation