Topes: Enabling End-User Topes: Enabling End-User Programmers to Validate and Reformat Programmers to Validate and Reformat

DataData

Christopher Scaffidi

Carnegie Mellon University

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Target populationTarget population

• In 2012, there will be 90 million computer end users in American workplaces.

• Of these, at least 55 million will create spreadsheets, databases, web applications, or other programs.– Spreadsheets for computing budgets– Spreadsheets and databases for storing information– Web applications for collecting data from coworkers

And similar programs for automating a wide range of tedious or error-prone work tasks.

Introduction Requirements Topes Tools Evaluation Conclusion

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Contextual inquiry:Contextual inquiry:What are the problems of end users?What are the problems of end users?

Observed 3 administrative assistants, 4 managers, and 3 webmasters/graphic designers (1-3 hrs, each)

Introduction Requirements Topes Tools Evaluation Conclusion

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Lots of manual labor—Lots of manual labor—validating and reformatting stringsvalidating and reformatting strings

• Building a staff roster, merging data from web sites:– Had to scrutinize data to identify questionable values

(e.g.: CMU campus phone numbers are usually 258-xxxx but 259-xxxx might be right)

– Had to manually transform data to consistent format(e.g.: Put person names in Lastname, Firstname format)

• Cannot automate with “web macro” tools – Intended for automating tasks like these– Tools don’t “know” how to check campus phone

numbers or reformat person names.

=> Users simply performed the tasks manually

Introduction Requirements Topes Tools Evaluation Conclusion

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Another person’s task: validate web forms--Another person’s task: validate web forms--but he didn’t know JavaScript / regexpsbut he didn’t know JavaScript / regexps

Is the input valid?“EDSH 225”

Does it need reformatting?“Smith 225”

Is the input questionable?“Gates 225”

Or is it obviously invalid?“412-555-5444”

Introduction Requirements Topes Tools Evaluation Conclusion

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Collaborations of programmers withCollaborations of programmers withwidely varying skills, interests, concernswidely varying skills, interests, concerns

• Interviewing creators of Hurricane Katrina “person locator” sites (helping survivors publish their status)

• 4 managers in IT firms, 1 student, 1 graphic designer

– 2 people each created a site on their own– 4 people collaborated with other programmers

(principally on site aggregation)

Introduction Requirements Topes Tools Evaluation Conclusion

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Hurricane Katrina “Person Locator” site:Hurricane Katrina “Person Locator” site:Many inputs unvalidatedMany inputs unvalidated

Introduction Requirements Topes Tools Evaluation Conclusion

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Data errors reduce the usefulness of data.Data errors reduce the usefulness of data.

Even little typos impede data de-duplication.

Age is not useful for flying my helicopter to come rescue you.

Nor is a “city name” with 1 letter.

Introduction Requirements Topes Tools Evaluation Conclusion

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Hurricane Katrina sites are not alone in Hurricane Katrina sites are not alone in lacking input validation.lacking input validation.

• Eg: Google Base web application–13 primary web forms –Even numeric fields accept unreasonable inputs (such as a salary of “-45”)

• If professional programmers can’t get this right, then it’s unsurprising that those 90 million end users also have so much trouble.

So many unvalidated inputs. So many data errors. So much time to find mistakes. So many millions of people laboriously reformatting data by hand.

We need a better way!

Introduction Requirements Topes Tools Evaluation Conclusion

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OutlineOutline

1. Requirements for a better data model

2. Topes• Model for describing data• Tools for creating/using topes

3. Evaluations

4. Conclusion

Introduction Requirements Topes Tools Evaluation Conclusion

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Underlying problem: abstraction mismatchUnderlying problem: abstraction mismatch

• Tools support strings, integers, floats, maybe dates.

• Problem domain involves higher-level data categories:

– Person names “Scaffidi, Chris”, “Chris Scaffidi”

– CMU phone numbers “8-1234”, “x8-1234”

– CMU room numbers “WeH 4623”, “Wean 4623”

Introduction Requirements Topes Tools Evaluation Conclusion

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Approach: Create a new abstraction for Approach: Create a new abstraction for each category of dataeach category of data

• Like software “libraries,” implementations of these abstractions could be reused in many programs.

• Abstractions would need to include functions for:– Recognizing instances of the category

(for automating data validation)

– Transforming instances among various formats

(for automating data reformatting)

Introduction Requirements Topes Tools Evaluation Conclusion

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1. Identify valid, invalid, and 1. Identify valid, invalid, and questionable valuesquestionable values

• Data is sometimes questionable… yet valid.– Eg: an unusually long email address– In practice, person names and other proper nouns are never

validated with regexps… too brittle.– Life is full of corner cases and exceptions.

• If code can identify questionable data, then it can double-check the data:– Ask an application end user to confirm the input– Flag the input for checking by a system administrator– Compare the value to a list of known exceptions– Call up a server and see if it can confirm the value

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2. Capture reformatting rules2. Capture reformatting rules

• Two different strings can be equivalent.– What if an end user types a date in the wrong format?– “Jan-3-2007” and “1/3/2007” mean the same thing because of

the category that they are in: date.– Sometimes the interpretation is ambiguous. In real life,

preferences and experience guide interpretation.

• If code can transform among formats, then it can put data in an unambiguous format as needed.– Display result so users can check/fix interpretation

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3. User-extensibility3. User-extensibility

• Many kinds of data are organization-specific

• But users at those organizations know what the data values mean—take advantage of what they know…

• Users can describe the constrained parts of data.– Eg: CMU room numbers, “EDSH 303”, have a building name

and an internal room number– Valid data obeys intra- and inter-part constraints.

Introduction Requirements Topes Tools Evaluation Conclusion

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4. Reusability across programming 4. Reusability across programming environments (“platforms”)environments (“platforms”)

• If a CMU room number is invalid, it’s generally because the room does not exist…– i.e.: it does not matter whether the room number is in

a spreadsheet or a webform or a database

• To validate a kind of data, people don’t want to write– JavaScript for webforms on the client side– C#/Java/PHP for webforms on the server side– Stored procedures for databases– VBScript for spreadsheets

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Limitations of existing approachesLimitations of existing approaches

Types do not support questionable values

Grammars (eg: regexps, CFGs, Lapis) do not either, and cannot reformat

Tools to integrate heterogeneous databases require a professional DBA and are specific to database systems (ie: not spreadsheets, webforms, etc).

Cues, Forms/3, -calculus, Slate, etc, infer numerical constraints but not constraints on strings, and they are tied to specific programming platforms

Information extraction algorithms rely on grammatical cues that are absent during validation

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Imagine a world where…Imagine a world where…

• Code can ask an oracle, “Is this a person name?”, and the oracle replies yes, no, almost definitely, probably not, and other shades of gray.

• Code allows input in any reasonable format, since the code can ask the oracle to put the input into the format that is actually needed.

• Regardless of whether they are working in spreadsheets, webforms, or other programming environment, end users can teach the oracle about a new data category by concisely stating its parts and constraints.

Introduction Requirements Topes Tools Evaluation Conclusion

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TopesTopes

• A “tope” = a platform-independent abstraction that describes how to recognize and reformat instances of a data category

• Greek word for “place,” because each corresponds to a data category with a natural place in the problem domain

Introduction Requirements Topes Tools Evaluation Conclusion

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A tope is a graph.A tope is a graph.Node = format, edge = transformationNode = format, edge = transformation

Notional representation for a CMU room number tope…

Formal building name& room number

Elliot Dunlap Smith Hall 225

Colloquial building name& room number

Smith 225

Building abbreviation& room number

EDSH 225

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A tope has functions for recognizing and A tope has functions for recognizing and transforming instances of a data categorytransforming instances of a data category• Each tope implementation has executable functions:

– 1 isa:string[0,1] function per format, for recognizing instances of the format (a fuzzy set)

– 0 or more trf:stringstring functions linking formats, for transforming values from one format to another

• Validation function:(str) = max(isaf(str))where f ranges over tope’s formats– Valid when (str) = 1– Invalid when (str) = 0– Questionable when 0 < (str) < 1

Introduction Requirements Topes Tools Evaluation Conclusion

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Common kinds of topes:Common kinds of topes:enumerations and proper nouns enumerations and proper nouns

• Multi-format Enumerations, e.g: US states– “New York”, “CA”, maybe “Guam”

• Open-set proper nouns, e.g.: company names– Whitelist of definitely valid names (“Google”), with

alternate formats (e.g. “Google Corp”, “GOOG”)– Augmented with a pattern for promising inputs that

are not yet on the whitelist

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Two other common kinds of topes:Two other common kinds of topes:numeric and hierarchicalnumeric and hierarchical

• Numeric, e.g.: human masses– Numeric and in a certain range– Values slightly outside range might be questionable– Sometimes labeled with an explicit unit– Transformation usually by multiplication

• Hierarchical, e.g.: address lines– Parts described with other topes (e.g.: “100 Main St.”

uses a numeric, a proper noun, and an enum)– Simple isas can be implemented with regexps.– Transformations involve permutation of parts, lookup

tables, and changes to separators & capitalization.

Introduction Requirements Topes Tools Evaluation Conclusion

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Topes in actionTopes in action

1. Users implement new topes to describe data categories.

2. Users publish tope implementations on repositories.

3. Other users download topes to a local cache.

4. Tool plug-ins help users browse their local cache and associate topes with variables and input fields.

5. Plug-ins get topes from local cache and use them at runtime to validate and reformat data.

Introduction Requirements Topes Tools Evaluation Conclusion

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Role of good tool supportRole of good tool support

• Some simple isa functions could be implemented as– Enumerations– Regular expressions / formal grammars

• But for many topes, we also need to support questionable values and reformatting

• And usability can almost always be improved by tailoring the tools to the problem domain– Integrate with users’ familiar tools– Match the user interface to the problem’s structure

Introduction Requirements Topes Tools Evaluation Conclusion

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What the user seesWhat the user sees

Introduction Requirements Topes Tools Evaluation Conclusion

User highlights cellsClicks “New” button on our Validation toolbar

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System infers a boilerplate topeSystem infers a boilerplate topeand presents it for review and customizationand presents it for review and customization

Introduction Requirements Topes Tools Evaluation Conclusion

Induction steps:1. Identify number & word parts2. Align parts based on punctuation3. Infer simple constraints on parts

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User gives names to the partsUser gives names to the partsand edits constraintsand edits constraints

Features• Part names• Soft constraints• Value whitelists• Testing features

Introduction Requirements Topes Tools Evaluation Conclusion

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System identifies typosSystem identifies typos

Introduction Requirements Topes Tools Evaluation Conclusion

Features• Targeted messages• Overridable• Filterable• Can add to “whitelist”• Integrated with Excel’s “reviewing” functionality

Checking inputs1. Convert description to CFG w/

constraints on productions2. Parse each input string3. For each constraint violation,

downgrade parse’s isa score

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Easy access to reformatting functionalityEasy access to reformatting functionality

Introduction Requirements Topes Tools Evaluation Conclusion

Reformatting string1. Parse with input format’s CFG2. For each part in target format,

a) Get node from parse treeb) Reformat node if needed (recurse)c) Concatenate (with separators if needed)

3. Validate result with target format’s CFG

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Recommending topes based on label and Recommending topes based on label and examples-to-matchexamples-to-match

Introduction Requirements Topes Tools Evaluation Conclusion

Efficient recommendation• Only consider a tope if its instances could possibly have the “character content” of each example string.(eg.: could this have 12 letters & 1 space?)

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Search repository by Search repository by label and/or exampleslabel and/or examples

Note: many repositories will be organization-specific

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Integration with Visual Studio.NETIntegration with Visual Studio.NET

Introduction Requirements Topes Tools Evaluation Conclusion

Features• Targeted messages• Overridable• Drag & drop code generation

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Other integrations to date:Other integrations to date:CoScripter, Robofox, XML/HTML libraryCoScripter, Robofox, XML/HTML library

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Other integration underwayOther integration underway

Introduction Requirements Topes Tools Evaluation Conclusion

• RedRover– Spreadsheet auditing– They already support formula auditing– Goal: Using topes for checking strings

• LogicBlox– Decision-support– Helping users enter data & make decisions from it– Goal: Using topes for validating data– Goal: Using topes for data de-duplication

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Evaluating accuracyEvaluating accuracy

• Implemented topes for spreadsheet data– Grouped 1712 spreadsheet columns into categories– Created 32 topes for the most common categories

(~ 70% of the data)– Compared validation with topes to validation with

existing regexps or enumerations from the web– Tope-based validation was 3 times as accurate

• Most benefit from supporting multi-format topes; smaller benefit from double-checking questionable values (~ 3% of inputs)

Introduction Requirements Topes Tools Evaluation Conclusion

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Evaluating reusabilityEvaluating reusability

• Reused spreadsheet-based topes on webform data– Downloaded data for 8 data categories on

Google Base and 5 in Hurricane Katrina website– Reused spreadsheet-based topes on the web data– Validation was just as accurate as on spreadsheets

Introduction Requirements Topes Tools Evaluation Conclusion

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Evaluating support for data cleaningEvaluating support for data cleaning

• Used topes to put web data into consistent formats– Again with the 5 columns in Hurricane Katrina website– Used transformation functions to put each string into

the most common format for that data category– Increased number of duplicate strings found by 10%

Introduction Requirements Topes Tools Evaluation Conclusion

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Evaluating usability for data validationEvaluating usability for data validation

• Users validating data with single-format topes– Between-subjects lab study– 8 users validated spreadsheet data with our tools;

for comparison, 8 users validated with Lapis patterns– Yes/no validation tasks (no questionable data)– Our tool users vs Lapis users

• Found three times as many typos• Were twice as fast• Reported significantly higher user satisfaction

– Our tool users vs users in earlier regexp study• Faster & more accurate

(Similar but not identical tasks: not statistically comparable)

Introduction Requirements Topes Tools Evaluation Conclusion

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Evaluating usability for data reformattingEvaluating usability for data reformatting

• Users reformatting data with multi-format topes– Within-subjects lab study– 9 users reformatted spreadsheet data by creating &

using topes; for comparison, they then did it manually– Effort of creating a tope “pays off” at only 47 strings

(further reuse is essentially “free”)– Every participant strongly preferred using our tools

instead of doing tasks manually

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Evaluating tope recommendationsEvaluating tope recommendations

• Quickly recommend existing tope for data at hand– Supports keyword-based search + search-by-match

(eg: topes that match “888-555-1212”)– Evaluated by searching through topes for the 32 most

common data categories in EUSES spreadsheet corpus, using strings from corpus

– High accuracy: Recall over 80% (result set size = 5)– Adequate speed: User is likely to have a few dozen

topes on computer, taking under 1 sec to search

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Topes improve data validationTopes improve data validation

• Validating with topes improves– Accuracy of validation– Consistency of data formatting– Reusability of validation code

• Primary contributions:– Support for ambiguous data categories– Support for reformatting values– Platform-independent, reusable validation

Introduction Requirements Topes Tools Evaluation Conclusion

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Research approachResearch approach

1. Understand users’ needs & context (empirically)

2. Identify a general abstract problem

3. Apply, adapt and extend methods and models ofsoftware engineering & human-computer interaction

4. Evaluate empirically; iterate w/ step 3 until adequate

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Long-term goal: Improving theLong-term goal: Improving thebenefit/cost ratio of end-user programmingbenefit/cost ratio of end-user programming

• Helping users automate larger tasks/computations– Finding reusable pieces of code– Repurposing and combining code

• Topes as “glue”

• Reducing cost of supporting end-user programming– Need appropriate software application architectures– May impact design and maintenance of applications– Requires partnership with software development

companies to reach those 90 million end users

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Thank You…Thank You…

• To you for this opportunity to present

• To Oregon State University for leading EUSES

• To my advisor, Mary Shaw at Carnegie Mellon,and EUSES for great feedback

• To NSF for funding

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ReferencesReferences

For more information on end users and topes- End users’ counts and needs: VL/HCC’05, VL/HCC’07- Topes model: ICSE’08- Format inferrence: ICEIS’07- Integration with other systems: WEUSE’08 & FSE’08- Our latest tools + usability validation: ISEUD’09 & IUI’09

For more information on some related work- Dependent types, eg: X. Ou, Dynamic Typing with Dependent Types, Tech Rpt TR-695-04, Princeton Univ, 2004

- Regexp induction, eg: K. Lerman, S. Minton. Learning the Common Structure of Data, Proc. AAAI, 2000.

- Lapis system: R. Miller, Lightweight structure in text, Tech Rpt CMU-CS-02-134, Carnegie Mellon Univ., 2002.

- SWYN regexp editor: A. Blackwell, See What You Need: Helping End-users to Build Abstractions, JVLC, 2001.

- Federated databases, eg: A. Sheth, J. Larsen, Federated database systems for managing distributed, heterogeneous, and autonomous databases, CSUR, 1990.

- ETL Tools, eg: E. Rahn, H. Do, Data Cleaning: Problems and Current Approaches, IEEE Data Eng. Bulletin, 2000.

- Potter’s Wheel: V. Raman, J. Hellerstein, Potter's Wheel: An Interactive Data Cleaning System, VLDB, 2001.

- Forms/3 : M. Burnett et al, End-user software engineering with assertions in the spreadsheet paradigm, ICSE, 2003.

- -calculus: M. Erwig, M. Burnett, Adding Apples and Oranges. Symp. Practical Aspects of Declarative Lang., 2002.

- Named entities, eg: Message Understanding Conference series.

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Professional programmers use lots of tricks Professional programmers use lots of tricks to simplify validation code. Eg: njtransit.comto simplify validation code. Eg: njtransit.com

Split inputs into many easy-to-validate fields.Who cares if the user has to type tabs now,or if he can’t just copy-paste into one field?

Make users pick from drop-downs.Who cares if it’s faster for users to type

“NJ” or “1/2007”?(Disclaimer: drop-downs sometimes are good!)

I implemented this site in 2003.

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Even with these tricks, writing validation is Even with these tricks, writing validation is still very time-consuming.still very time-consuming.

Overall, the site had over 1100 lines of JavaScript

just for validation….Plus equivalent server-side Java code (too bad code

isn’t platform-independent)

if (!rfcCheckEmail(frm.primaryemail.value)) return messageHelper(frm.primaryemail, "Please enter a valid Primary Email address.");var atloc = frm.primaryemail.value.indexOf('@');if (atloc > 31 || atloc < frm.primaryemail.value.length-33) return messageHelper(frm.primaryemail, "Sorry. You may only enter 32 characters or less for your email name\r\n”+ ”and 32 characters or less for your email domain (including @).");

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That was worst case.That was worst case.Best case: reusable regexps.Best case: reusable regexps.

• Many IDEs allow the programmer to enter oneregular expression for validating each input field.– Usually, this drastically reduces the amount of code,

since most validation ain’t fancy.– Yet programmers don’t validate most inputs.

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Users’ spreadsheets are rife with Users’ spreadsheets are rife with formatting inconsistencies & other typosformatting inconsistencies & other typos

In one study by Univ Nebraska, nearly 40% of spreadsheet cell values were strings (not numbers or dates).

Part of an actual spreadsheet on Carnegie Mellon’s intranet

Introduction Requirements Topes Tools Evaluation Conclusion

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Evaluating expressivenessEvaluating expressiveness

• Implemented topes for common webform inputs– Instrumented web browsers of 4 administrative

assistants for 3 weeks– Logged strings that they typed into forms – in a

regexp-masked format e.g.: user0@XYZ.EDU [a-z]{4}[0-9]@[A-Z]{3}.[A-Z]{3}

– Also logged strings nearby to textfields– Semi-automatically grouped strings into categories

e.g.: project number, expense type, email address, zip code

– Implemented 14 most common topes– Found 22 probable typos in user inputs (0.5%)

Introduction Requirements Topes Tools Evaluation Conclusion

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Tope Development Environment (TDE)Tope Development Environment (TDE)

Topei ModuleInfers tope from

examples

Toped ModuleEnables users to create/edit topes

Topeg ModuleGenerates context-free

grammars and transformations

Topep ModuleParses data against grammars, performs

transformations

Plug-insRead/write program

data

RobofoxWeb macros

Vegemite/CoScripterWeb macros

Visual Studio.NETWeb applications

Microsoft ExcelSpreadsheets

…

Introduction Requirements Topes Tools Evaluation Conclusion

RepositoryStores topes for sharing/reuse

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As a tool builder, what do I have to do so As a tool builder, what do I have to do so that people can use topes in my tool?that people can use topes in my tool?

You need to make a plug-in1. Figure out what kind of fields you want to help your

users validate/reformat(eg: spreadsheets’ cells; webforms’ textboxes)

2. Download our open source C# or Java API (library)

3. In your tool’s UI, add buttons and other widgets so user can select a tope for the fields; in your event handler, call our API methods

4. At runtime, pass field’s value (a string) to our API methods to validate or reformat strings

5. Display validation error messages; update value in UI

Introduction Requirements Topes Tools Evaluation Conclusion